Getting started with Universal App Campaigns

With 3.8 million apps available for Android users and 2 million apps in Apple's App Store, it can be tough for an app developer to stand out among the competition. But with Google's Universal App Campaigns (UAC), developers have an opportunity to market their mobile apps with targeting options based on audience demographics and behavior. It all happens automatically -- as long as you set up the campaigns correctly. In this post I take a look at how you can put machine learning to work for you, using the power of Google’s Universal App Campaigns. Campaign set up Getting started with a UAC is relatively easy. The three steps are to identify an audience, ensure conversion tracking is set up correctly, and relevant text, video, and images are available for the campaign. The two major actions for UACs are to find new users who will install the app or those who will perform an action inside the app, such as making an additional purchase. One the UAC is set-up, it is eligible to show on Search, Display, YouTube and the Play Store. The initial setup is straightforward. The advertiser only needs to provide four lines of text with images and with machine learning, Google decides which combination to show to a particular user. Goals When you consider goals for your UAC, the install action is an obvious one regardless of the app category. Targeting options includes people who are likely to install the app or who are likely to install it and perform in app action. It is up to the advertiser to determine what a valuable action looks like and ensure conversion tracking is set up before launching a campaign. In-app actions, or goals, or can be either success actions or proxy actions. With a success action, the app user makes a purchase inside the app, upgrades the service, or signs up for a paid subscription; something that generates revenue. Assuming success actions happen at least ten times a day with users, the system has enough data to identify and target the right audience for your UAC. If volume of success actions is low, there is not enough data for machine learning to make decisions. In that case, the advertiser can identify a proxy action which is a behavior that is likely to lead to success action. An example of this is someone who added payment information to upgrade service but did not follow through with upgrading. Or it could be tracking which of your users share incentives with their network. Advertisers need to think carefully about what a proxy action truly is. When it it is too early in the funnel, it includes people who are less likely to convert and not a good representation of those who will later perform a success action. If a mid funnel behavior is identified as a proxy action, rather the the top of the funnel, it may better represent people who are closer to converting so it is more likely to later result in a success action. Conversions Setting up and collecting conversion data is a crucial piece to success because these campaigns look at past searches, browsing behavior, and other apps used to determine who is most likely to convert. Before launching a UAC, ensure this conversion tracking is set up correctly or your will not be measuring goals that matter. For e-commerce sites, the primary conversion is clearly to drive revenue in the form of an in-app purchase or perhaps subscriptions. With luxury retail, it is especially important to have conversion recording correctly because of the multiple touch points. And Shopify users can use the Littledata reporting app to gain even more insight on the user journey through that platform. Measurement and optimization There are immediate metrics to monitor - app installs and in-app purchase - but there are also long term considerations such as the customer lifetime value (CLV), that should be part of your overall strategic marketing plan. A single user who makes a purchase provides direct revenue. If they refer someone to your app, that is considered indirect revenue. The first number is clear-cut revenue and easy to measure. The second is one that you determine based on your internal data, meaning what type of behavior and interaction with customers generally leads to a sale. The value of both of these actions contribute to the CLV. Lifetime is the length of time they interact with your app. If they install the app and use it to buy things over the course of a year, then stop, their CLV time period is one year. Once you have identified your CLV, use this value to set your target CPA and optimize it based on performance. Decide what you are willing to pay for a success action and what you will pay for a proxy action, knowing that number will likely change over time. As data comes in from your UAC, you can compare the lifetime value of your different customers through segments. Segments help you uncover those customers who purchase every couple months compared to those who only make an initial purchase. Those the make multiple purchases represent segments with a higher value. Drilling into data with segments allows you to see who gives you the best return for your investment. This level of detail helps you identify how much you paid in your UAC for to acquire each type of customer so you can adjust accordingly. Review what you paid initially for the type of users that you bring in and compare that to their lifetime value. Are you investing your budget in a UAC that brings in users that generate recurring revenue? When you bid strategically based on a lifetime value, you are not overly focused on short-term transactions. It is less expensive to keep a customer than to acquire a new one so you want to think in those terms. What next? Decide on UAC goals that make sense for the purpose of your app. What should users do in addition to downloading the app and what behaviors indicate they are getting close to a conversion? Gather assets - text, video, and image - that are enticing for users and ensure conversion tracking is setup properly. Without proper conversion tracking, you miss out on the data you need to determine success. Monitor performance of your campaigns, and if you run an ecommerce site, track a wealth of data with the Littledata app. Think about the CLV and optimize your campaigns to reach the right users rather than any users. Your bottom line is generating revenue so keep that in mind with every UAC. With careful planning and well managed campaigns, your app can stand out in a crowded marketplace.

2018-10-31

How to increase Average Order Value (AOV) on your ecommerce site

Average order value (AOV) is a bona fide north star metric for Shopify stores, and ecommerce companies more broadly. Increasing AOV is a priority goal for ecommerce teams as it directly boosts revenue (and profits, if you’re doing things right). Growing revenue often requires retailers to acquire more traffic, but with AOV you can increase sales simply by convincing shoppers to spend that little bit more. AOV can be improved by adopting a number of proven optimisation techniques. Many of these have their roots in offline retail, where price, promotion, placement and merchandising all play a part in persuading customers to buy additional - or more expensive - items. We’ll get onto these tactics soon enough, but for now let’s start at the beginning. What is average order value? AOV is the average amount spent by customers when they place an order. To calculate AOV you divide total sales by the total number of orders (typically over a certain period of time). You can monitor AOV via Google Analytics. If you’re using Littledata then you’ll see it on your dashboard and in ecommerce report packs. Why is average order value important? AOV is one of the primary KPIs in ecommerce. It is a measure of sales trends and reflects customer behaviour and buying preferences. This insight can be used to optimise your website, pricing strategy, and guide decisions on what you choose to sell. It is also a good indicator of your ability to optimise ROI, as your marketing budget will go that much further if you increase AOV. It is worth investing time and money into moving the AOV needle, as it will create universal uplift. Implement the right kind of tactics - and technology - and we are sure that you will see some positive results, especially if this is an activity you haven’t yet spent too much time on. The results? New and existing customers are likely to spend more with you whenever they buy. Better sales numbers, bigger profits, and various additional benefits. Just like the other ecommerce KPIs, it is best not to view AOV in isolation. Related metrics include customer lifetime value (CLV) and customer acquisition cost (CAC), particularly for ecommerce subscription businesses. How do I know if my average order value is in good shape? Littledata has robust benchmarking data from a sample of 12,000 ecommerce sites. You can drill down by category and revenue to see how you compare vs your peers. For example, we analysed AOV across 379 medium-sized ecommerce sites in September and found that $123 is the typical amount spent. But average is relative - it very much depends on the sector. Start a free Littledata trial to see your AOV alongside the benchmark for your sector (we will show you some other juicy metrics and benchmarks too). It will look like this: Pretty cool, huh? If you happen to be underperforming in any area then our app will suggest some proven optimisation ideas to help you improve your store. Other stores have used our ecommerce benchmarks to grow sales, and we're confident that you will experience similar results. What affects average order value? Lots of things influence how much people spend when they buy from your site. Consider the last time you bought a higher priced item, such as a TV, laptop or mobile phone. More often than not there are upsells and cross-sells as you progress down the purchase path. You end up buying related items (mobile phone cases), upgrading your initial choice (256MB memory vs 64MB), purchasing add-ons (extended warranty), or clicking on a compelling product bundle (phone + case + warranty = 15% off). This kind of buying behaviour helps ecommerce teams to sleep soundly at night. It is to be encouraged. A real world example Apple is an absolute master of maximising AOV. Let’s take a quick walkthrough of one of its purchase pathways. First, we’ll select a Macbook Pro and will then see the following page, which invites us to customise our order. Add a little more memory and one item of software, and the order value increases by about 30%. Boom. Now let’s click the ‘Add to Bag’ button. We’ll progress to an ‘Essentials’ page. Yet more ideas to help us spend extra money. Think we’re all done? Not so fast. Click on ‘Review Bag’ and you’ll enter the checkout. Note the ‘Related Products’ that appear underneath the basket summary. Is it any wonder that Apple is valued at more than one trillion dollars? How can I increase my average order value? The million dollar question (or maybe a few billions, in the case of Apple). The researchers for our newest product feature - called Missions - have discovered plenty of ideas for you to try out. Littledata Missions provide step-by-step guides to help ecommerce teams optimise performance, and AOV was one of the very first metrics we wanted to explore. The following ideas are taken from our Average Order Value Fundamentals mission. There are a bunch of others in there to try too. Missions automatically generate based on your ecommerce benchmark data in the Littledata app (try Missions for free today). I’ll wager that at least one of the following will help you to grow AOV. And a super combo might seriously move the dial. Once you’ve optimised AOV - and there might be a ceiling - you can work on increasing purchase frequency, customer referrals, and then scale up your customer acquisition efforts. So then, here are 12 ideas to help you start to grow average order value... 1. Provide free shipping for orders above a certain amount Betterware grew AOV by 20% after introducing free shipping for orders above £30. M&S also provides free standard delivery for orders that exceed £30, as seen in the screenshot. A study by UPS found that 58% of consumers would add extra items to their cart in order to qualify for free delivery. As such this is a great way of increasing average order value. Free delivery is an expectation these days, so if you're late to the party - and concerned about margins - then a minimum threshold is worth testing. 2. Offer minimum spend discounts Much like introducing a free shipping threshold, you can provide a discount if the customer spends a certain amount on your site. Although it might seem to go against the goal of increasing average order value, setting offers such as this can tempt visitors into spending whatever is necessary to achieve the discount, because it appears like a deal. There are a number of ecommerce plugins to help with this. A lot of happy Shopify stores use the Product Discount app. 3. Make the most of up-selling Up-selling is the art of convincing prospective customers to increase their spend, typically by buying a more expensive item to the thing they're looking at. For example, in the screenshot below we can see how Amazon shows higher priced TVs to the one initially selected. By listing out the features side by side it may be enough to convince the prospective buyer that the next model up is a more attractive option. This is a sure-fire way to increase average order value, though it's not without its risks as you'll need to change the shopper's mind about something ("You don't really want that, you want this."). So be careful when experimenting with up-selling techniques. 4. Embrace cross-selling Amazon has attributed around 35% of its revenue to cross-selling. Not exactly small change. As such it is crucial to find a cross-selling strategy that works for your website. Cross-selling is the science of persuading customers to buy additional products related to the thing they’re about to purchase. For example, buy a camera and you might see recommendations for camera cases, bigger memory cards, battery chargers, etc. Adding items to the basket in this way is highly likely to increase average order value. However, it is important to specify which customers receive cross-sell offers. You should certainly think twice before cross-sells to customers who regularly return items. 5. Allow customers to use live chat A Forrester study found that there is a 10% increase in order value from customers who used the live chat function. The study also discovered that live chat helps to increase revenue by 48% per chat hour, and increased conversion rate by 40%. The business case for live chat would appear to be strong. Why is this? Mainly because customers like the immediacy - and familiarity - of chat. It has been reported that 73% of consumers who have used live chat were pleased with the experience. So, live chat is good for AOV, sales, conversion rates and customer satisfaction. What's not to like? 6. Show how others have enjoyed the product Average order value is 6% higher among shoppers who read reviews, compared with those who don't bother, according to a Bazaarvoice study. Positive social proof is incredibly powerful. It goes a long way towards encouraging people to progress to the checkout. Social proof comes in many forms, from reviews and ratings to testimonials and buyer videos. Make it highly visible at key points in the buyer journey, to build trust and reinforce the decision to buy. 7. Offer financing for high-ticket items Analysis by Divido has shown that sales can increase by 40% when high-ticket items are offered in monthly instalments. Your most expensive items are the ones which can be heavily responsible for driving up your average order value. If you offer customers the option to pay in instalments it can help you sell more of these higher valued products. For example, Goldsmiths offers shoppers 0% interest-free credit on purchases which total more than £750. This may appeal to people looking at items in the £500+ range - they might end up being tempted to spend more once they see the financing available. 8. Offer volume-based discounts Office supplies company Paperstone generated a 19% average order increase when a bulk discount deal was offered. As well as helping to grow AOV, strategic discounting can be great for clearing out excess inventory. However, remember that it is important to calculate bulk discounts very carefully. You need to offer deals that attract customers, but which do not hurt your profit margins. 9. Use dynamic retargeting to increase average order value Stella & Dot increased AOV by 17% after experimenting with dynamic retargeting, which allows ecommerce firms to show shoppers the right kind of ads during the shopping journey (such as product recommendation ads, based on their browsing behaviour or purchase history). This technology also recaptures lost sales from visitors who leave websites, by showing them personalised offers to re-engage them. 10. Send personalised emails OneSpot found that average order value increased by 5% upon the personalisation of emails. Simply put, customers are more likely to feel valued by your site if you provide them with messages that are relevant to their specific interests. Personalisation often starts at the customer's name ('Dear sir' won't cut it), but extends to the content of the email. If this is based on prior browsing and purchase history then you're more likely to engage the shopper, to reinforce - or complete - the purchase. 11. Offer a gift card or loyalty scheme By offering customers rewards for shopping with you, you’re likely to see an increase in orders, as well as an increase in the size of those orders. It has been shown that offering rewards for purchases 15-20% above average order size can increase the amount people are willing to spend. Encouraging big spenders to buy more frequently will also have the effect of increasing AOV. A study by BigDoor found that loyal customers make up 70% of total sales in some cases, so it is important to give something back to those customers once in a while. 12. Create product packages A case study into BaubleBar, a jewellery site, showed that average order value increased significantly when product bundling was offered. One pair of its earrings costs $30, but a bundle of three is just $48. This bundle screams “deal” to a customer. BaubleBar saw its average order increase by $22 in a matter of days. Bundling reduces cognitive load. If you can help shoppers avoid thinking too much then you're onto a good thing. Bundles can be viewed by visitors as a valuable deal, especially if they contain products which supplement the one they are already interested in. Packaging up items this way can be incredibly persuasive, particularly when you're offering a discounted price. They can also save the shopper time - no need to browse for add ons. Start the AOV mission today In summary, trying to increase average order value is worth the effort, and will be a gift that keeps on giving once you move the dial in the right direction. You can launch the Average Order Value mission directly from your Littledata dashboard. Our app will track your progress as you test ideas to discover what works best for your site. People trust Littledata to audit, fix and automate reporting. They use our benchmarks to check and compare their performance, relative to their peers. And now, with Missions, digital teams can actively set about increasing ecommerce revenue.

2018-10-25

Categorising websites by industry sector: how we solved a technical challenge

When Littledata first started working with benchmark data we found the biggest barrier to accuracy was self-reporting on industry sectors. Here’s how we built a better feature to categorise customer websites. Google Analytics has offered benchmarks for many years, but with limited usefulness since the industry sector field for the website is often inaccurate. The problem is that GA is typically set up by a developer or agency without knowledge or care about the company’s line of business - or understanding of what that industry sector is used for. To fix this problem Littledata needed a way to categorise websites which didn’t rely on our users selecting from a drop-down list. Google Analytics has offered benchmarks for many years, but with limited usefulness since the industry sector field for the website is often inaccurate. The first iteration: IBM Watson NLP and a basic taxonomy Our first iteration of this feature used a pre-trained model as part of IBM Watson’s set of natural language APIs. It was simple: we sent the URL, and back came a category according to the Internet Advertising Bureau taxonomy. After running this across thousands of websites we quickly realised the limitations: It failed with non-English websites It failed when website homepage was heavy with images rather than text It failed when the website was rendered via Javascript Since our customer base is growing most strongly outside the UK, with graphical product lists on their homepage, and using the latest Javascript frameworks (such as React), the failure rate was above 50% and rising. So we prioritised a second iteration. The second iteration: Extraction, translation and public APIs The success criteria was that the second iteration could categorise 8 sites which the first iteration failed with, and should go on to be 80% accurate. We also wanted to use mainly public APIs, to avoid maintaining code libraries, so we broke the detection process into 3 steps: Extracting meaningful text from the website Translating that text into English Categorising the English text to an IAB category and subcategory The Watson API seemed to perform well when given sufficient formatted text, at minimal cost per use, so we kept this for step 3. For step 2, the obvious choice was Google Translate API. The magic of this API is that it can detect the language of origin (with a minimum of ~4 words) and then provide the English translation. That left us focussing the development time on step 1 - extracting meaningful text. Initially we looked for a public API, and found the Aylien article extraction API. However, after testing it out on our sample sites, it suffered from the same flaws as the IBM Watson processing: unable to handle highly graphical sites, or those with Javascript rendering. To give us more control of the text extraction, we then opted to use a PhantomJS browser on our server. Phantom provides a standard function to extract the HTML and text from the rendered page, but at the expense of being somewhat memory intensive. Putting the first few thousand characters of the website text into translation and then categorisation produced better results, but still suffered from false positives - for example if the text contained legal-ease about data privacy it got categorised as technical or legal. We then looked at categorising the page title and meta description, which any SEO-savvy site would stuff with industry language. The problem here is that the text can be short, and mainly filled with brand names. After struggling for a day we hit upon the magic formula: categorising both the page title and the page body text, and looking for consistent categorisation across the two. By using two text sources from the same page we more than doubled the accuracy, and it worked for all but one of our ‘difficult’ websites. This hold-out site - joone.fr - has almost no mention of its main product (diapers, or nappies), which makes it uniquely hard to categorise. So to put it all the new steps together, here’s how it works for our long-term enterprise client MADE.com's French-language site. Step 1: Render the page in PhantomJS and extract the page title and description Step 2: Extract the page body text, remove any cookie policy and format Step 3: Translate both text strings in Google Translate Step 4: Compare the categorisations of the title vs page body text Step 5: If the two sources match, store the category I’m pleased that a few weeks after launching the new website classifier we have found it to be 95% accurate. Benchmarking is a core part of our feature set, informing everything that we do here at Littledata. From Shopify store benchmarks to general web performance data, the improved accuracy and deeper industry sector data is helping our customers get actionable insights to improve their ecommerce performance. If you’re interested in using our categorisation API, please contact us for a pilot. And note that Littledata is also recruiting developers, so if you like solving these kind of challenges, think about coming to join us!

2018-10-16

Ecommerce trends at Paris Retail Week

Physical or digital? We found merchants doubling down on both at Paris Retail Week. At the big event in Paris last month, we found retailers intent on merging the online and offline shopping experience in exciting new ways. See who we met and what the future of digital might hold for global ecommerce. Representatives from our European team had a great time at the big ecommerce event, one of the 'sectors' at Paris Retail Week. Outside of the event, it was great to have a chance to catch up with Maukau, our newest Shopify agency partner in France. (Bonjour!) Among the huge amount of digital sales and marketing trends we observed throughout the week, a few emerged again and again: mobile-first, phygital experience, and always-on, multi-channel marketing. Getting phygital Phygital? Is that a typo? Hardly. It’s the latest trend in ecommerce, and it was prevalent everywhere at Paris Retail Week. Phygital combines “physical” and “digital” experiences in a new ecosystem. This offers the consumer a full acquisition experience across different channels. From payment providers to marketing agencies, everyone was talking about going phygital. One of our favourite presentations was by AB Tasty. They focused on how optimising client experience can boost sales and conversions in the long-term. It’s not enough to promote your products, nor to link to an influencer for social proof -- you need to create a full customer experience. Starbucks and Nespresso are good examples of how this works offline, assuring that a customer who comes in to drink a coffee will linger around for the next 20-30 minutes. By keeping the customers in the shop, they will eventually order more. The goal is to reproduce this immediately sticky experience online too, and focusing on web engagement benchmarks is the best way to track your progress here. Using the example of conversion rate optimisation (CRO) for mobile apps, AB Tasty's Alexis Dugard highlighted how doing data-driven analysis of UI performance, on a very detailed level, can help clarify how mobile shopping connects with a wider brand experience. In the end, customer experience means knowing the customer. 81% of consumers are willing to pay more for an optimal customer experience. Brands that are reluctant to invest in customer experience, either online or offline, will hurt their bottom line, even if this isn't immediately apparent. Those brands that do invest in multi-channel customer experience are investing in long-term growth fuelled by higher Average Order Value (AOV). 81% of consumers are willing to pay more for an optimal customer experience -- the statistic speaks for itself! Another great talk was from Guillaume Cavaroc, a Facebook Academie representative, who discussed how mobile shopping now overlaps with offline shopping. He looked at experiments with how to track customers across their journeys, with mobile login as a focal point. In the Google Retail Reboot presentation, Loïc De Saint Andrieu, Cyril Grira and Salime Nassur pointed out the importance of data in retail. For ecommerce sites using the full Google stack, Google data represents the DNA of the companies and Google Cloud Platform is the motor of all the services, making multi-channel data more useful than ever in assisting with smart targeting and customer acquisition. The Google team also stated that online shopping experiences that don’t have enough data will turn to dust, unable to scale, and that in the future every website will become, in one way or another, a mobile app. In some ways, "phygital" really means mobile-first. This message that rang out clearly in France, which is a mobile-first country where a customer's first encounter with your brand or product is inevitably via mobile -- whether through a browser, specific app or social media feed. Multi-channel experience (and the data you need to optimise it) Physical marketing is making a comeback. Boxed CEO Chieh Huang and PebblePost founder Lewis Gersh presented the success of using online data for offline engagement, which then converts directly back on the original ecommerce site. Experimenting heavily in this area, they've seen personalised notes on invoices and Programmatic Direct Mail (with the notes based on viewed content) generate an increase of 28% in online conversion rate. Our real-world mailboxes have become an uncluttered space, and customers crave the feel of a paperback catalogue or simple postcard, to name just a bit of the physical collateral that's becoming popular again -- and being done at a higher quality than in the years of generic direct mail. Our real-world mailboxes have become an uncluttered space, and customers crave the feel of a paperback catalogue or simple postcard. However, data is still the backbone of retail. In 2017 Amazon spent approximately $16 billion (USD) on data analysis, and it was worth every penny, generating around $177 billion in revenue. Analysing declarative and customer behaviour data on the shopper’s path-to-purchase is a must for merchants to compete with Amazon. Creating an omni-channel experience for the user should be your goal. This means an integrated and cohesive customer shopping experience, no matter how or where a customer reaches out. Even if you can't yet support an omni-channel customer experience, you should double down on multi-channel ecommerce. When Littledata's customers have questions about the difference, we refer them to Aaron Orendorff's clear explanation of omni-channel versus multi-channel over on the Shopify Plus blog: Omni-channel ecommerce...unifies sales and marketing to create a single commerce experience across your brand. Multi-channel ecommerce...while less integrated, allows customers to purchase natively wherever they prefer to browse and shop. Definitions aside, the goal is to reduce friction in the shopping experience. In other words, you should use anonymous data to optimise ad spend and product marketing. For marketers, this means going beyond pretty dashboards to look at more sophisticated attribution models. We've definitely seen this trend with Littledata's most successful enterprise customers. Ecommerce directors are now using comparative attribution models more than ever before, along with AI-based tools for deeper marketing insights, like understanding the real ROI on their Facebook Ads. The new seasonality So where do we go from here? In the world of ecommerce, seasonality no longer means just the fashion trends of spring, summer, autumn and winter. Online events like Black Friday and Cyber Monday (#BFCM) define offline shopping trends as well, and your marketing must match. "Black Friday" saw 125% more searches in 2017, and "Back to School" searches were up 100%. And it isn't just about the short game. Our own research last year found that Black Friday discounting is actually linked to next-season purchasing. Phygital or otherwise, are you ready to optimise your multi-channel marketing? If not, you're missing out on a ton of potential revenue -- and shoppers will move on to the next best thing.

2018-10-09

What's the real ROI on your Facebook Ads?

For the past decade Facebook’s revenue growth has been relentless, driven by a switch from TV advertising and online banners to a platform seen as more targetable and measurable. When it comes to Facebook Ads, marketers are drawn to messaging about a strong return on investment. But are you measuring that return correctly? Facebook has spent heavily on its own analytics over the last three years, with the aim of making you -- the marketer -- fully immersed in the Facebook platform…and perhaps also to gloss over one important fact about Facebook’s reporting on its own Ads: most companies spend money with Facebook 'acquiring' users who would have bought from them anyway. Could that be you? Here are a few ways to think about tracking Facebook Ads beyond simple clicks and impressions as reported by FB themselves. The scenario Imagine a shopper named Fiona, a customer for your online fashion retail store. Fiona has browsed through the newsfeed on her Facebook mobile app, and clicks on your ad. Let’s also imagine that your site -- like most -- spends only a portion of their budget with Facebook, and is using a mix of email, paid search, affiliates and social to promote the brand. The likelihood that Fiona has interacted with more than one campaign before she buys is high. Now Fiona buys a $100 shirt from your store, and in Facebook (assuming you have ecommerce tracking with Pixel set up) the sale is linked to the original ad spend. Facebook's view of ROI The return on investment in the above scenario, as calculated by Facebook, is deceptively simple: Right, brilliant! So clear and simple. Actually, not that brilliant. You see Fiona had previously clicked on a Google Shopping ad (which is itself powered by two platforms, Google AdWords and the Google Merchant Center) -- how she found your brand -- and after Facebook, she was influenced by a friend who mentioned the product on Twitter, then finally converted by an abandoned cart email. So in reality Fiona’s full list of interactions with your ecommerce site looks like this: Google Shopping ad > browsed products Facebook Ad > viewed product Twitter post > viewed same product Link in abandoned cart email > purchase So from a multi-channel perspective, how should we attribute the benefit from the Facebook Ad? How do we track the full customer journey and attribute it to sales in your store? With enough data you might look at the probability that a similar customer would have purchased without seeing that Facebook Ad in the mix. In fact, that’s what the data-driven model in Google Marketing Platform 360 does. But without that level of data crunching we can still agree that Facebook shouldn’t be credited with 100% of the sale. It wasn’t the way the customer found your brand, or the campaign which finally convinced them to buy. Under the most generous attribution model we would attribute a quarter of the sale. So now the calculation looks like this: It cost us $2 of ad spend to bring $1 of revenue -- we should kill the campaign. But there's a catch Hang on, says Facebook. You forgot about Mark. Mark also bought the same shirt at your store, and he viewed the same ad on his phone before going on to buy it on his work computer. You marked the source of that purchase as Direct -- but it was due to the same Facebook campaign. Well yes, Facebook does have an advantage there in using its wide net of signed-in customers to link ad engagement across multiple devices for the same user. But take a step back. Mark, like Fiona, might have interacted with other marketing channels on his phone. If we can’t track cross-device for these other channels (and with Google Marketing Platform we cannot), then we should not give Facebook an unfair advantage in the attribution. So, back to multi-channel attribution from a single device. This is the best you have to work with right now, so how do you get a simple view of the Return on Advertising Spend, the real ROI on your ads? Our solution At Littledata we believe that Google Analytics is the best multi-channel attribution tool out there. All it misses is an integration with Facebook Ads to pull the ad spend by campaign, and some help to set up the campaign tagging (UTM parameters) to see which campaign in Facebook brought the user to your site. And we believe in smart automation. Shhhh...in the past few weeks we've quietly released a Facebook Ads connection, which audits your Facebook campaign tagging and pulls ad cost daily into Google Analytics. It's a seamless way to pull Facebook Ads data into your overall ecommerce tracking, something that would otherwise be a headache for marketers and developers. The integration checks Facebook Ads for accurate tagging and automatically pulls ad cost data into GA. The new integration will normally only be available in higher-tier plans, but we're currently offering it as an open beta for ALL USERS, including Basic plans! For early access, just activate the Faceb|ook Ads connection from your Littledata dashboard. It's that easy! (Not a subscriber yet? Sign up for a free trial on any plan today.) We believe in a world of equal marketing attribution. Facebook may be big, but they’re not the only platform in town, and any traffic they're sending your way should be analysed in context. Connecting your Facebook Ads account takes just a few minutes, and once the data has collected you’ll be able to activate reports to show the same kind of ROI calculation we did above. Will you join us on the journey to better data?

2018-09-20

Intro to the Littledata app (VIDEO)

How does the Littledata app work? It's magic! Or at least it feels that way. This new video gives a quick overview of how it all fits together. Our ecommerce analytics app is the only one on the planet to both fix your tracking and automate reporting. Our customers see dramatic growth, from higher add-to-cart rates to better return on paid search. But what happens first, and what happens next? If you're an ecommerce marketer using Google Analytics, Littledata will make your job a whole lot easier. The process breaks down to four core steps, which you can repeat as often as you'd like. First you connect your analytics account, marketing channels like Google AdWords and Facebook Ads, and website data from tools like Shopify, ReCharge and CartHook. (And yes, we'll help you comply with GDPR). Then you use the Littledata app to audit your analytics setup and fix your tracking. Shopify stores can fix tracking automatically -- other sites get clear recommendations on what to do. If your goals include higher marketing ROI and increased conversions, the next step is to automate reporting with report packs and a smart dashboard, available directly in the app. And then it's time to optimise revenue with industry benchmarks, enhanced reporting and buyer personas, all built automatically. Sign up today for a free audit of your analytics setup, or book a demo to learn more. A complete picture of your ecommerce business is just around the corner!

by Ari
2018-08-14

How auditing Google Analytics can save you money

When is the last time you audited your Google Analytics account? If the answer is 'never', I understand, but you could be wasting a ton of cash - not to mention potential revenue. It's easy to put off an analytics audit as a 'someday' project considering the multitude of other tasks you need to accomplish each day. But did you know that auditing your Google Analytics account can save you money and add a big bump to online revenue, even with sites that are not ecommerce? Whether people spend money directly on your site, or your site is primarily for lead generation, you spend money to get those site visitors through your marketing channels. When you view a channel like AdWords, there is a clear financial cost since you pay for clicks on your ads. With organic traffic, such as from Facebook fans, you spend time crafting posts and measuring performance, so the cost is time. With an investment of any resource, whether time or money, you need to evaluate what works - and what does not - then revisit the strategy for each of your marketing channels. In this post, I’ll walk you through some of the automated audit checks in Littledata and take a look at what they mean for your online business. If your analytics audit doesn't ask the following questions, you're probably wasting money. Is your AdWords account linked to Google Analytics? If you run AdWords campaigns, linking AdWords and Analytics should be at the top of your to-do list. If AdWords and Analytics are not linked, you cannot compare your AdWords campaign performance to your other channels. Although you can still see how AdWords performs within the AdWords interface, this comparison among channels is important so you can adjust channel spend accordingly. If you discover that AdWords is not delivering the business you expected compared to other marketing channels, you may want to pause campaigns and reevaluate your PPC strategy. Are you tracking website conversions? There should be several conversion goals set up on your website because they represent visitor behavior that ultimately drives revenue. The above example shows a warning for a lead generation website. Although it is possible that no one contacted the site owner or scheduled an appointment in 30 days as indicated in the error, it does seem unlikely. With this warning, the site owner knows to check how goals are set up in Google Analytics to ensure they track behavior accurately. Or, if there really was no engagement in 30 days, it is a red flag to examine the strategy of all marketing channels! Although the solution to this warning will be different based on the individual site, this is an important problem to be aware of and setting up a goals in Google Analytics, such as for by destination, is straightforward. You can also get creative with your goals and use an ecommerce approach even for non-ecommerce websites. Do you use campaign tags with social media and email campaigns? This is an easy one to overlook when different marketing departments operate in silos and is a common issue because people do not know to tag their campaigns. Tagging is how you identify your custom social media and email campaigns in Google Analytics. For example, if you do not tag your paid and organic posts in Facebook, Google Analytics will lump them together and simply report on Facebook traffic in Google Analytics. In addition to distinguishing between paid and organic, you should also segment by the types of Facebook campaigns. If you discover poor performance with Facebook ads in Google Analytics, but do great with promoted posts in the Facebook newsfeed, you can stop investing money in ads at least for the short term, and focus more on promoted posts. Are you recording customer refunds in GA? Refunds happen and are important to track because they impact overall revenue for an ecommerce business. Every business owner, both online and offline, has dealt with a refund which is the nature of running a business. And this rate is generally fairly high. The return rate for brick-and-mortar stores is around 9% and closer to 20% for online stores, so less than 1% in the above audit seems suspicious. It is quite possible the refund rate is missing from this client’s Google Analytics account. Why does this matter? Let’s assume the return rate for your online store is not terrible - maybe 15% on average. However, once you track returns, you see one product line has a 25% return rate. That is a rate that will hurt your bottom line compared to other products. Once you discover the problem, you can temporarily remove that product from your inventory while you drill into data - and talk to your customer support team - to understand why that product is returned more than others, which is a cost savings. Are you capturing checkout steps? Most checkouts on websites have several steps which can be seen in Enhanced Ecommerce reports in Google Analytics. Shoppers add an item to their cart, perhaps log-in to an existing account or create a new one, add shopping information, payment etc. In the ideal world, every shopper goes through every step to ultimately make a purchase, but in the real world, that is rare. Last year alone, there was an estimated $4 trillion worth of merchandise abandoned in online shopping carts. Reasons for this vary, but include unanticipated extra costs, forced account creation, and complicated checkouts. By capturing the checkout steps, you can see where people drop out and optimize that experience on your website. You can also benchmark checkout completion rates see how your site compares to others. Are you capturing product list views? If you aren't tracking product list views correctly, your biggest cash cow might be sleeping right under your nose and you wouldn't even know it! Which products are the biggest money makers for you? If a particular product line brings in a lot of buyers, you want to make sure it is prominent on your website so you do not leave money on the table. Product list views enable you to see the most viewed categories, the biggest engagement, and the largest amount of revenue. If a profitable product list is not frequently viewed, you can incorporate it in some paid campaigns to get more visibility. The good news An audit is not only about what needs fixing on your website, but also can show you what is working well. After you run an audit, you will see the items that are set up correctly so give yourself a pat on the back for those - and know that you can trust reporting based on that data. Either way, remember to run an analytics audit regularly. Once a month is a good rule. I have seen cases where a website was updated and the analytics code was broken, but no one noticed. Other times, there may be a major change, such as to the customer checkout, so the original steps in your existing goal no longer work. Or an entirely new marketing channel was added, but with missing or inconsistent tagging. It is worth the time investment to ensure you have accurate Google Analytics data since it impacts influences your decisions as a business owner and your bottom line. Littledata's automated Google Analytics audit is especially useful for ecommerce sites, from online retailers to membership sites looking for donations. It gives a clear list of audit check results, with action plans for fixing your tracking. And Shopify stores can automatically fix tracking to capture all marketing channels and ensure that data in Google Analytics matches Shopify sessions and transactions (not to mention the data in your actual bank account!), even when using special checkouts like ReCharge and CartHook. When you're missing out on the revenue you should already have, an audit is the first step in understanding where it's falling away, or where you're over-spending. Run an audit. Make a list. Fix your tracking. Grow your revenue. Sometimes it really is that simple!

2018-08-01

CartHook integration for tracking one-page checkouts and upsells

We're excited to announce that Littledata now fully integrates with CartHook. The integration provides automatic tracking for sales from CartHook's one-page checkout and connects that data to marketing channels and shopper behaviour. Littledata -- CartHook integration is the easiest way to get accurate data and smart reporting to improve sales and marketing ROI. All you need is a Shopify store with CartHook Checkout installed (even for just one product) and a Google Analytics account! What is CartHook? CartHook makes it easy for Shopify stores to add customisable one-page checkouts and post purchase one-click upsells. Their intuitive funnel builder lets any store customise the checkout process to increase conversions and decrease abandonment. Features include: Customisable one-page checkout One-click post-purchase upsells, including for subscription products (works great with our ReCharge integration) Product Funnels allow you to send traffic to a pre-loaded checkout page from any landing page Native Shopify integration means no custom coding required! How it works Integrating CartHook with Littledata ensures that all sales activity is tracking correctly in Google Analytics. Littledata weaves together your Shopify and CartHook data and connects it with your marketing channels and campaigns. Why spend developer time on custom scripts and events when you can just activate the integration in a couple of minutes? Benefits of CartHook integration: Sales tracking - Get automatic tracking for sales from CartHook, seamlessly synced with sales made via standard Shopify checkout Marketing attribution - Connect marketing channels and campaigns with shopping cart activity and buyer behaviour Optimisation - Scale the smart way with Littledata's industry-leading optimisation tools, including a personalised dashboard, report packs, benchmarks and buyer personas It's all about accurate data. Littledata's script runs in the background, pulling from CartHook, Shopify, and any other source you've connected to your analytics. If you're an advanced Google Analytics user, you can dig into the improved data collection directly in GA. Read more about why CartHook customers should use Littledata. Setup guide For the Littledata -- CartHook integration to work, you need to have both apps installed for your Shopify store, then connect them by activating the integration. Install CartHook and Littledata Follow these steps to activate the integration Yes, it's that easy! Shopify Plus If you run a larger Shopify store on Shopify Plus, we're here to help you scale. Both Littledata and CartHook offer enterprise plans that include custom setup and a dedicated account manager. Larger stores looking for an enterprise plan or managed services are encouraged to sign up directly and then contact us for a free consultation. If you're a digital agency with multiple customers on Shopify using CartHook, even better! Check out our agency partner program for Shopify experts.

by Ari
2018-07-24

The World Cup guide to marketing attribution

It’s World Cup fever here at Littledata. Although two of the nationalities in our global team didn’t get through the qualifiers (US & Romania) we still have England and Russia to support in the next round. And I think the World Cup is a perfect time to explain how marketing attribution works through the medium of football. In football (or what our NYC office calls 'soccer'), scoring a goal is a team effort. Strikers put the ball in the net, but you need an incisive midfield pass to cut through the opposition, and a good move starts from the back row. ‘Route one’ goals scored from a direct punt up the pitch are rare; usually teams hit the goal from a string of passes to open up the opportunity. So imagine each touch of the ball is a marketing campaign on your site, and the goal is a visitor purchasing. You have to string a series of marketing ‘touches’ together to get the visitor in the back of the net. For most ecommerce sites it is 3 to 6 touches, but it may be more for high value items. Now imagine that each player is a different channel. The move may start with a good distribution from the Display Ads defender, then a little cut back from nimble Instagram in the middle. Facebook Ads does the running up the wing, but passes it back to Instagram for another pass out to the other wing for Email. Email takes a couple of touches and then crosses the ball inside for AdWords to score a goal – which spins if off the opposing defender (Direct). GOAL!!! In this neat marketing-football move all the players contribute, but who gets credit for the goal? Well that depends on the attribution model you are using. Marketing attribution as a series of football passes Last interaction This is a simplest model, but less informative for the marketing team. In this model the opposing defender Direct gets all the credit – even though he knew nothing about the end goal! Last non-direct click This is the attribution model used by Google Analytics (and other tools) by default. In this model, we attribute all of the goal to the last campaign which wasn’t a Direct (or session with unknown source). In the move above this is AdWords, who was the last marketing player to touch the ball. But AdWords is a greedy little striker, so do we want him to take all the credit for this team goal? First interaction You may be most interested in the campaign that first brought visitors to your website. In this model, Display ads would take all the credit as the first touch. Display often performs best when measured as first interaction (or first click), but then as a ‘defender’ it is unlikely to put the ball in the net on its own – you need striker campaigns as well. Time decay This model shares the goal between the different marketing players. It may seem weird that a player can have a fraction of a goal, but it makes it easy to sum up performance across lots of goals. The player who was closest to the goal gets the highest share, and then it decays as we go back in time from the goal. So AdWords would get 0.4, Email 0.5 (for the 2 touches before) and Instagram gets 0.1. Data-driven attribution This is a model available to Google Analytics 360 customers only. What the Data-driven model does is run through thousands of different goals scored and look at the contribution of each player to the move. So if the team was equally likely to score a goal without Facebook Ads run down the wing it will give Facebook less credit for the goal. By contrast, if very few goals get scored without that pass from Instagram in the midfield then Instagram gets more credit for the goal. This should be the fairest way to attribute campaigns, but the limitation is it only considers the last 4 touches before the goal. You may have marketing moves which are longer than 4 touches. Position based Finally you can define your own attribution weighting in Position Based model, based on which position the campaign was in before the goal. For example, you may want to give some weight to the first interaction and some to the last, but little to the campaigns in between. Still confused? Maybe you need a Littledata analytics expert to help build a suitable model for you. Or the advice of our automated coach known as the analytics audit. After all, every strategy could use a good audit to make sure it's complete and up-to-date. So go enjoy the football, and every time someone talks of that ‘great assist’ from the winger, think of how you can better track all the uncredited marketing campaigns helping convert customers on your site.

2018-07-02

Increase ecommerce conversion rates with segments in Google Analytics

Are you generating enough site traffic? Are there enough visitors each month that engage with your content and spend time on your site? Those are good things, but the important question is how you are doing with conversions, as that is where the magic happens! For many businesses, there will be slumps where conversions are not where you need them to be and increasing conversions is tougher than bringing in visitors. The reasons for not converting are many, which could include a poorly designed landing page or frustration with a slow page load time. Fortunately, the technical aspects of your site are somewhat clear cut and influence all users to the site. Either it loads quickly or it does not. It responds to mobile devices or does not. But there are principles that are about the groups visitors to a site. What do they search for and can you provide it? How are they different from each other? How are they similar? In short, you need smart segmentation if you want to continue to increase conversions. Here's a quick guide to using segments in Google Analytics. Segments vs personas In this post, we will build on some of the work you have hopefully done to create personas and highlight the value of segments when optimizing for conversions. Personas help you be empathetic to your customers. Visualizing a 35-year old professional female makes it easier to create the right message for her rather than general messages to all women. This is not about stereotypes. Personas help you hypothesize about similarities in how people behave. So how is that different from segments? There is confusion with segments versus personas and you want establish a definition for your team so you all work from the same framework. In the simplest terms, you segment your audience with existing data and create campaigns based on personas. Start with your segments. With Google Analytics, you can use segmentation to group people by identifying criteria such as location. Think of segments as the somewhat objective view of your audience based on raw data. (There is still some subjectivity when deciding the makeup of segments). Personas are very subjective - based more how a person thinks or feels. Get to know your audience with Google Analytics Google Analytics provides a lot of data that helps us understand our segments if we go beyond basic metrics, such as pageviews. Below are a few ways to learn more about your segments with the goal of increasing conversions and adding depth to your personas. Pages per Session: This is a basic metric in Google Analytics but you can go beyond scenarios, such as users visiting two pages compared to those visiting seven pages. Look at which pages they visited. Did they visit the intro offerings (probably a newcomer) or the help section (probably an existing customer)? Did they read the entire section about a topic (more methodical) or buy on the first visit (maybe more impulsive)? Note these are assumptions about motivations but you can develop hypotheses based on behavior. Content Grouping: Content grouping categorizes your site content based on rules created in the Admin section of your Google Analytics account.   Once you have these rules, you can view content groups for different scenarios, such as where people are the journey, how they flow through content, how they came into your site (traffic source), and how much time they spent on there. For sites with thousands of pages, this makes it more manageable than viewing individual pages. You can analyze conversions on the categories of your site rather than a specific page. Cohort Analysis: Found in the Audience section of Google Analytics, this is used to examine the behavior and performance of groups of users related by common attributes. It allows you to view a group of visitors based on a shared acquisition date. If you have a drip campaign scheduled for May, you may want a Cohort Date Range of May 1 to May 7 to target people who first visited the site during that time period. You can learn if people who visited on a specific day were more inclined to visit again than other members of that group. User Login: Custom Variables can be fired when users login. That provides additional data for more advanced segments by identifying the behavior of different customer types. Site visitors self-segment when they log into the site to take an action. With Custom Variables, you can see how behavior is different for those who log-in versus those who do not. Bounce Rate: We all get hung up on this metric. People see a site bounce rate of 78% and begin to panic but you need to drill in to see if that matters. Do existing customers and regular visitors bounce from a blog post? That is expected. However, if new people regular bounce from the site, look at the landing pages. There could be a message mismatch with the source that sent them to a particular page. Affinity Segments: Use affinity and in-market segments in Google Analytics to help define your personas. They are broad classifications about users which may be helpful when layered on top of other characteristics. For example, you may discover segments that prefer one content grouping over another. Collect metrics that matter When there is a difference in the conversion rate and user journey among segments, it indicates your identified segments truly represent distinct types of users. Read that again because whether your segments make sense determines whether your data is any good. With the right segments, you can determine which groups to cultivate or which ones to not pursue with limited resources. For example, if one segmented group regularly buys add-ons for product, that might justify allocating more advertising dollars. With target segments identified, you can also look at which marketing effort attracted them to your site. Some of this is obvious. If users in their 30s never respond to a CTA on your site from Facebook, you may not want to pay for ads on that channel or even post to it regularly. So yes, we all care about who converts compared to those who do not. But remember there are stages leading up to a conversion and this Facebook audience could still have a role, so watch where in the process people drop off. And hopefully by now you realize that non-converters are more than just non-converters. View this by segment too to identity what non-converters may have in comment. As data comes in, additional segmenting can be done by on locations, time of conversion, brand search terms versus early stages searches. But do not collect data for the sake of collecting data. Although it is easy to do with the abundance of data available in Google Analytics, it does not guarantee a return for your efforts. Want to know more? Get in touch with Tina’s agency, 360 Internet Strategy, and follow her on LinkedIn.  

2018-06-19

Google Analytics Data Retention policy - which reports does it limit?

From 25th May 2018 Google allowed you to automatically wipe user-level data from the reporting from before a cut-off date, to better comply with GDPR. We made the change for Littledata's account to wipe user-level data after 26 months, and this is what we found when reporting before February 2016. Reports you can still view before the user data removal  Audience metrics Pageviews ✓ Sessions  ✓ Users  X Bounce rate  ✓  Audience dimensions Demographics  X OS / Browser  X Location  X User Type  X  Behaviour Pageviews ✓ Custom events X

2018-06-04

How to implement a successful mobile marketing strategy

Mobile as a marketing strategy isn’t a new idea to anyone, but the landscape is changing quickly. Back in 2015, Google told us it would be expanding its use of mobile-friendliness as a ranking signal. More recently, in early 2018, they stated that page speed will be a ranking factor for mobile searches middle of this year. As consumers change their behavior on mobile devices, this greatly impacts our strategy as marketers. We now need to be visible on all devices, all the time. What do all these changes mean for marketers? Whether you're a solo AdWords consultant or a member of a digital agency, it's essential to stay on top of consumer trends in a way that is measurable and repeatable. In this post I break down how to develop a data-driven mobile marketing strategy that can easily scale with your online business. Mobile search has changed As consumers, we are research-obsessed. We want to know everything we can about an ecommerce product or service so we can make informed decisions. And as more of us search for seemingly minor things and do so on a small device, advertisers have the opportunity to be present in those micro moments. With an increase of searches on mobile devices (and with mobile searches already having bypassed desktop searches several years ago) we need to be present across the entire consumer experience, making the customer experience a business priority regardless of our brand or business size by providing a seamless experience on every device. Analyzing data with a last-click attribution model misses some of these mobile moments. Assumptions have changed along with search behaviors. In September 2015, Google shared that “near me” or “nearby” searches on Google had grown 2X in the previous year, but the use of that phrase has since declined. People still want results that are near them, but the assumption of today’s searchers is that Google knows the location of the searchers and where to find what was searched because people are using their devices throughout the day. Increase of use for “open now” and “tonight" and “today” travel-related terms indicate people are seeking information on their device. What this means for brands Does your strategy consider these trends and adjust to changes in consumer behavior? A mobile experience leads to a brand impression. People expect a consistent experience every time they interact with a brand. If your site does not deliver and does not deliver quickly, they will quickly leave. Regardless of which channel they used to get to your site, the mobile experience must be as seamless as the desktop experience. What this means for Google AdWords As mobile use continues to increase and consumer behavior changes, we need to better align our PPC efforts and use an attribution model that addresses all steps of the journey. With AdWords, we can align our marketing strategy to mobile use with mobile search ads, mobile display ads and app ads on mobile devices. Each option offers slightly different features. Text ads can display on any device. The primary difference with ads on mobile vs desktop is more ads per page on a desktop and only a couple on a mobile device. Because the first couple ads take up most of the screen on a smartphone, advertisers need to be in the first or second position because that is all that will display. Impatient searchers will not scroll down on their device to your ad in position four. On the Display Network, you can be more creative with ads, adding images and videos to the mix. Although image sizes that work on desktop computers will also work on mobile devices, aim for a smaller size of 320 x 50 when possible, keeping the layout of smaller screen sizes in mind. The third option for mobile ads are appearing on mobile apps, which are part of the Display Network. App promotion ads have a goal of driving downloads. Campaigns with only app promotion ads are eligible for phones and tablets; they are not on desktop computers. Bid adjustments With your AdWords campaigns, set bids on mobile devices that are aligned with your goals. As mentioned above, many will not scroll down the search results page on a smartphone to view ads so may want to increase these bids. This is also important for branding goals; you need to be at the top to be seen. When determining mobile bids based on ROI, identify ROI for desktop versus tablets and devices. That way, your adjustment is based specifically on the mobile value of conversions. Keywords In any AdWords campaign, the key to success is selecting the correct keywords. But you can go a step further and use the keyword tool to also see mobile trends for your selected keyword over the previous year. Use these findings to inform your bidding strategy. A subjective approach is to view your keywords in the eyes of your users. Are the keywords in your campaigns ones that you would type into your mobile device? Although more people use voice recognition to search, there are still those who type in their request. Since typing on a small screen results in typos, you want broad match keywords in your campaign when targeting mobile users. Make sure these keywords include action-oriented terms. Some people may surf their device out of boredom while standing in line, but many search to find information to make a decision. You can capture these early clicks with an attribution model other than last-click. Mobile URLs Google provides an option of using mobile URLs in ads to customize the mobile experience, but if the mobile URL is the same as the Final URL in AdWords, adding it does not impact mobile performance. This is designed for people who have different pages for mobile users. AMP pages An open source initiative, Accelerated Mobile Pages (AMP) solve the issue around slow landing pages to make them faster for mobile. Business that have used them find a much quicker loading time and a more engaging experience. You can also use the AMP version of your website in this option for final URL Bid strategy Take advantage of machine learning with a Smart Bidding strategy in your AdWords campaigns. It considers the multiple signals around device type and browser for auction-time changes, offering more targeting than we could do manually as an AdWords account manager with simple bid adjustments. Monitor device performance with this strategy and prioritize mobile traffic if it does particularly well on devices. Attribution models In all AdWords campaigns, regardless of device, many advertisers use the last-click attribution model, which is not ideal for any campaign, including those targeting mobile. It gives all the credit for a conversion to the last touchpoint - the last click - which misses out on how other interactions influenced the decision to convert. If you have enough data in your account, utilize the Data-Driven Attribution Model. If it is not available to you, consider one of the other options besides last-click attribution. The right reporting for mobile marketing Before you target mobile users with advertising, check first that your site performs well on mobile devices if you do not plan to have a mobile specific URL. Start with a quick test for mobile speed to see if you are at risk of losing traffic. Next do a quick SEO check of your site which is based on Google’s guidelines, which is also relevant to paid traffic. For all your campaigns, not just AdWords, you need to consider metrics such as sessions by device type for general site behavior and conversions once a campaign is running for a while. To minimize manual work for reporting and analysis, use a Littledata report pack which pulls in data from Google Analytics to offer automated reporting on customer touch points, providing data you need without the manual labor. And remember your mobile users are on the go, so any advertising needs to cater to them in the moment!   Want to know more? Get in touch with Tina's agency, 360 Internet Strategy, and follow her on LinkedIn.

2018-04-19

Google Analytics 360 versus the free version

We often receive questions about what customers get when they upgrade from the free version of Google Analytics to Google Analytics 360. The quick answer is that you get a lot - the possibilities are literally endless - as long as you're a big, data-driven company willing to put energy into customer engagement and marketing. Google emphasises that their enterprise analytics are designed to help large companies, like major ecommerce sites, create better customer experiences. But what does that mean in practice? There are a lot of details to understand if you're thinking of transitioning to the big paid version of Google Analytics. The main differences lie in how each product deals with the volume of data and integrations that they have available by default. I've broken those differences down into three categories: Data Collection, Data Sampling and Data Sources. Data collection In short, Google Analytics 360 allows for a faster, smarter, larger data collection. With unlimited hits per month and up to 200 custom dimensions per web property. Features Google Analytics (free) 360 Suite (paid) Hits per Month up to 10M unlimited Custom Dimensions/Metrics 20 Per Property 200 Per Property Calculated Metrics 5 Per View 50 Per View Properties per Account 50 50+ Views per Property 25 25+ Roll-Up Properties No Yes Data Freshness 24 – 48 hours 4 Hours   Data sampling and limits As your web traffic grows, Analytics 360 lets you get more out of both sampled and unsampled data sets. Compared with the standard version of GA, you get better reporting on large amounts of data. Understanding how data is sampled in Google Analytics will help you scale the smart way. Features Google Analytics (free) 360 Suite (paid) Report Row Limit per Day Yes Yes Standard Reports Pre-Aggregated 50K 75K Sampling in Ad-Hoc Reports 500K Sessions per Property 100M Sessions per Property Custom Tables No 100 Custom Table Report Row Limit per Day No 1M Rows Unsampled Reports No Yes Unsampled Report Row Limit No 3M (for download) Data sources The 360 Suite makes it especially easy to pull in data from a wide range of advertising platforms and sources, including non-Google products like Salesforce. For some of our enterprise customers, especially large ecommerce sites with a focus on PPC lead gen and retargeting, the ability to seamlessly integrate with DoubleClick is itself enough to make their 360-buy worthwhile! Features Google Analytics (free) 360 Suite (paid) AdWords Yes Yes AdSense Yes Yes DoubleClick Campaign Manager No Yes DoubleClick Bid Manager No Yes DoubleClick For Publishers No Yes Custom Data Sources Yes Yes Query-Time Data Import No Yes Salesforce No Yes BigQuery No Yes   Additional perks (GTM 360, beta testing) In addition to the above benefits, being able to connect Google Analytics to other Google 360 Solutions like Google Optimize 360 and Google Tag Manager 360 is a big plus. As an added perk, Analytics 360 clients often get early access to beta programs for testing and product feedback -- getting directly involved with product development to suit their needs -- plus first-hand support from Google. Google 360 can be purchased directly from Google or through a sales partner. We don't currently sell the 360 Suite ourselves, but we’ve been a certified Google Analytics Service Partner since 2015, including Google Tag Manager and Google Optimize certification, and have extensive experience with custom tagging and reporting. Plus, we built the Littledata app around those analytics best-practices. Our larger consulting clients get the most benefits out of our enterprise plans, which include automated analytics audits, unlimited access to app features, custom setup and reporting, and a dedicated account manager to help ensure deep, accurate tracking. Whether or not you've already upgraded to Google Analytics 360, we highly recommend getting in touch to make sure you're able to use this powerful tool to its full potential!

2018-02-28

Using Google Analytics to refine merchandising and product promotions

The whole purpose of having Google Analytics tracking on your site is to find out how your website is performing and to use this data to improve your digital efforts. Yet many businesses miss the mark when it comes to taking action at the level of product listings, despite the fact that this can lead to huge revenue gains! Why do they miss the mark? Two reasons: inaccurate tracking and unclear reporting. The Littledata app helps to fix these issues automatically, providing users with a reliable data stream and automated reporting based on Google Analytics data, but it's still useful to drill down into Google Analytics itself to understand all of the details. In this post I break down how to use Google Analytics to refine merchandising, product promotions and product listings in a way that can have a direct effect on both short-term and long-term revenue for your ecommerce site. For this to work, you'll need to have Enhanced Ecommerce set up on your website. You'll also need some spreadsheet software (Excel, Google Sheets, etc.) so we can play with extracted data and drill down deep. Banners and creatives: getting users to see what we want them to see A full enhanced ecommerce setup will enable you the power to see how much money each of the creatives on your site is bringing you. If your website is like most ecommerce sites, you have several creatives displayed, such as: Homepage carousel Homepage pods Category main banner Choosing which creative should get on your homepage might feel like just a preference, but it doesn't have to be that way. You can use the 'Internal Promotion' menu in Google Analytics (Marketing > Internal Promotions) to make data-driven decisions about your homepage creatives. Imagine an online store that sells scooters and accessories: We have banners for categories like Helmets, Accessories, Mini Micro and Maxi Micro (different sizes of scooters). We have 2 banners on the homepage with these two creatives: Safety (the first one) and Built for Adults (the second one). We want to change one of the creatives on the carousel. Let's analyze what is the best strategy here. The first banner on the carousel was seen 24,404 times. It has a 5.01% click thru rate (CTR) and a £3.90 value per click. The second banner on the carousel was seen 17,109 times. It has a 5.52% CTR a £2.02 value per click. Now we can make a decision. What to discard and what to keep Even though we have a higher CTR on the second banner and this is an indicator that the message is more appealing, the reality is that the revenue that comes with that click is not even half of the revenue we get from a click on the first banner. If you want to make a 100% correct decision here you can analyze the margins on the product promoted by each of the banners. If you have double the margin for the products in the second banner you can get rid of the Safety banner and make the second banner primary. If your margin is the same for both categories then the best decision here is to replace the second banner with the first one. How to populate the carousel We already decided to keep the first banner, but now we need a replacement for the second one. So we need to find a creative in the website that had performed at least the same as the second banner. Based on the example above if we search by CTR higher than 5.52% we can see that we have a banner for Maxi Micro with 20% CTR and a value per click of £5.32. The action here is to replace the second slot of the carousel with this creative. After 1-2 weeks we can retake this process all over again and we may decide to reverse the creatives (Banner 1 will be Banner 2 and Banner 2 will be Banner 1 in the carousel). This is not a one-time job. The analysis should be made every time you add a new creative or make a new promotion.--or even as a weekly task. Many Littledata clients run this type of analysis on a regular basis, whether or not they've launched a new promotion, to make sure they are optimizing sales and conversions. You should pay attention to the average click thru rate (CTR) based on creatives category, and also you should know what is your standard deviation for each category so that you can quickly spot which are over- or under-performing. Based on the example above, the average CTR for a carousel banner on the site is 5.26% and the standard deviation is 0.25%. So I know that if I see a banner that has a CTR less than 5.01%, there is room to improve. As per above for the category pages, we have an average of 10.92% CTR with a standard deviation of 6.28. This means that everything under 4.63% should be replaced ASAP and everything above 17.20% should be promoted. List views: how to arrange products for ultimate engagement One of the best Enhanced Ecommerce features in Google Analytics is the Product List Performance Report (Conversions > Ecommerce > Product List Performance). This report shows you how many views each list gets. Why does this matter? Because if you have a high margin on some products from a specific category, you should find out if that list (category) is being sufficiently promoted on your site. From these reports, we can find out things like: Most viewed categories (sort by Product List Views) The category that has the biggest engagement (sort by Product List CTR) The list that is bringing you the most money per view (Product Revenue divided by Product List Views) Which categories are performing best -- and which are most profitable? Let's say I have three categories in my store: categories 1, 2 and 3. And my margin for products in category 3 is three times the margin for those in category 1. In the report above, we see that we don't have a click thru for Category 2. This could mean: The tracking is not working on that page Users have issues clicking on the products There is no call to action (CTA) on that page So we can assume that Category 2 is not working. Moving forward we should analyze the performance of Category 1 vs Category 3. Views Clicks CTR Revenue Revenue / click Margin at each $1 sold Margin at 1000 clicks Category 1 1,701,660 57,038 3.35% $329,799.67 $5.78 0.23 $1,329.88 Category 3 46,895 3,175 6.77% $23,881.37 $7.52 0.69 $5,189.97 We can see that even though we have a fraction of the views for Category 3, this category is for us almost 3 times more profitable per 1000 clicks. At this point, we should investigate how much marketing we're doing around Category 3 to see if there are options to push harder on this highly profitable category, alongside whatever's already working for promoting Category 1. Order matters The Product List Performance Report can also help us find out how customers progress from viewing a product in a list to clicking through for more information. Let's analyze the data in the above report. The table is sorted by Product List Views for Mobile devices. We know that the alignment for this website is one product under the other and for a product view to be sent the user needs to see it for at least one second. So we can draw these conclusions: Position 2 and 3 are normally visually scanned by users. The fourth product in a list is seen in more detail but has a lower CTR than the second or third product in the list. We know that each page has 10 products so the average Product List CTR rate for page 1 is 1.36% and the standard deviation is 0.42. From this, we can see that position 2 has a good CTR and we need to change the photo and text of the listing to attract more attention -- products placed in the second position in a product listing on this site tend to convert well. Position 4 gets attention but has low performance so we could try changing the photo and title of products in this position in order to increase the CTR. If we are looking at this report as aggregate data then we can conclude that if we want to make a push for particular products, we should place them in position 1 or 4 for maximum visibility, or position 1 or 2 for maximum CTR. How to monetize product list positions We can take this analysis further by examining how list slots relate to product revenue, whether on your site or via affiliate programs. Looking at the report in aggregate and extracting the data, we can give a monetary value to each slot in the product listings. Product List Position Product List Views Product Revenue Revenue/view per slot 1 2,290,505 £183,207.00 £0.08 4 2,279,917 £99,830.00 £0.04 3 2,246,164 £117,096.00 £0.05 2 2,239,943 £157,605.00 £0.07 6 2,062,271 £73,183.00 £0.04 5 2,053,534 £94,889.00 £0.05 8 1,788,080 £58,585.00 £0.03 7 1,775,762 £60,603.00 £0.03 9 1,750,248 £52,366.00 £0.03 10 1,606,599 £50,913.00 £0.03 From the above example, we can see that each of the slots in the listing has a value per view. And the value is decreasing with the position. Using the known margin for a specific product in a list, you can improve your ROI just by positioning it in a slot with a higher CTR based on the model above. Which photos should you show first in a listing? If you offer a product in multiple colors, you should use an image and a default (primary) product selection in the most popular color. But how do you figure that out? Product variants are too often left behind in analysis. The Product Variant field captures the specific variation of a product, e.g., XS, S, M, L for size; or Red, Blue, Green, Black for color. It is an Enhanced Ecommerce feature that can give you powerful insights into your users' searches, interests and preferences. Paying attention to variant performance can have a big effect on shopping behavior and sales. In the example above, we're looking closely at the Product Variant dimension to figure out which color is most popular. We have a product with 4 colors: Black, Grey, Midnight Black and Persian Grey. There isn't enough transaction data to make a decision based on purchases, but we can calculate the most popular variant (in this case, the most popular color) based on how often users have added items in each color to their shopping carts (Adds To Cart). For Black, we have a View to Add To Cart rate of 0.6% and for Grey 0.8%. So in this case we should use the main Grey color for advertisements and main photos in listings pages. We might also try using the Persian Grey variant. Note that in this example we can calculate for each product view because we've listed each color as a different product. If you're listing only one product and you show variants on the product page, then you'll need to divide the Adds To Cart for each variant by the total Product List Views. What to do next If you need help with Enhanced Ecommerce reporting, our analysts are ready to come to the rescue. You can either request a consultation or just sign up for a free Google Analytics audit and contact us directly from the app. How are you using Enhanced Ecommerce reports in Google Analytics? Drop us a note below.

2018-02-23

Troubleshooting your Google Analytics goals setup (VIDEO)

https://www.youtube.com/watch?v=SGY013J9QGg So you've got your new sales plan in action and you've set up unique goals in Google Analytics. Are they tracking what you think they're tracking? Are you sure they're giving you reliable data? If you've audited your analytics setup, you might have noticed any number of incorrect audit checks about how you've set up custom events for your Google Analytics (GA) goals. Goals are used to make important business decisions, such as where to focus your design or advertising spend, so it's essential to get accurate data about them. In this quick video we cover common issues with setting up Google Analytics goals, including: Tracking pageviews rather than completed actions Selecting the wrong match type Inconsistent naming when tagging marketing campaigns Filters in your GA view rewriting URLs (so what you see in the browser is different from what you see in GA) Issues with cross-domain tracking In GA, a goal is any type of completed activity on your site or app. GA is a remarkably flexible platform, so you can use it to measure many different types of user behaviour. This could be visitors clicking a subscribe button, completing a purchase, signing up for membership -- known as 'conversion goals' -- or other types of goals such as 'destination goals', when a specific page loads, and 'duration goals', when a user spends over a particular amount of time on a page or set of pages. That all sounds well and good, but trouble comes if you simply set up goals and then trust the data they give you in GA, without double-checking to make sure that data's consistent and reliable. We hope you find the video useful. And don't despair -- even a little extra time spent on your GA setup can yield awesome results. Sign up for the Littledata app to audit your site for free, and let us know if you've experienced other common issues with setting up goals in GA.

2018-02-21

GDPR compliance for ecommerce businesses

Ecommerce companies typically store lots of personally identifiable information (PII), so how can you make compliance easier without compromising analysis? With the deadline for GDPR compliance looming, I wanted to expand on my previous article on GDPR and Google Analytics to focus on ecommerce. Firstly, who does this apply to? GDPR is European Union legislation that applies to any company trading in Europe: so if you sell online and deliver to European Union member countries, the regulations apply to you. It's essential that you understand how your online business is collecting and storing PII. Splitting PII from anonymous data points Your goal should be to maintain two separate data stores: one that contains customer details, from where you can look up what a specific customer bought, and one that contains anonymous data points, from where you can see performance and trends. The data store for the customer details will typically be your ecommerce back-end and/or CRM (see below). This will include name, email, address, purchase history, etc. It will link those with a customer number and orders numbers. If a customer wants the right of access all the relevant details should be in this store. We use Google Analytics as the anonymous data store (although you may have a different ecommerce analytics platform). There you can store data which only refers to the customer record. These are called pseudo-anonymous data points under GDPR: they are only identifiable to a customer if you can link the customer number or order number back to your ecommerce back-end. Pseudo-anonymous data points you can safely send to Google Analytics include: Order number / transaction ID Order value / transaction amount Tax & shipping Product names and quantities Customer number Hashed email address (possibly a more flexible to link back to the customer record) If a customer exercises their right to removal, removing them from the ecommerce back-end will be sufficient. You do not also have to remove them from your Google Analytics, since the order number and customer number now have nothing to refer to. You do still need due process to ensure access to Google Analytics is limited, as in extreme circumstances a combination of dimensions such as products, country / city and browser, could identify the customer. Isn’t it simpler to just have one store? Every extra data store you maintain increases the risk of data breaches and complexity of compliance – so why not just analyse a single customer data store? I can think of three reasons not to do so: Marketing agencies (and other third parties) need access to the ecommerce conversion data, but not the underlying customer data Removing a customer’s order history on request would impact your historic revenue and purchase volumes – not desirable Your CRM / ecommerce platform is not built for large scale analysis: it may lack the tools, speed and integrations needed to get meaningful insights Beware of accidental transfers There are a few danger areas where you may inadvertently be sending PII data to Google Analytics: Customer emails captured in a signup event A customised product name – e.g. ‘engraving for Edward Upton’ Address or name captured in a custom dimension Our PII audit check is a quick, free way to make sure that’s not happening. Multiple stores of customer details GDPR compliance becomes difficult when your customer record is fragmented across multiple data stores. For example, you may have product and order information in your ecommerce database, with further customer contact details in a CRM. The simplest advice is to set up automatic two-way integrations between the data stores, so updating the CRM updates the ecommerce platform and visa-versa. Removing customer records from one system should remove them from the other. If that’s not possible, then you need clear processes to update both systems when customer details change, so you can comply with the right to rectification. Conclusion GDPR compliance need not require changing analytics tools or databases, just a clear process for separating out personally identifiable information – and training for the staff involved in handing that data. I hope this brief overview has been helpful. For further advice on how your ecommerce systems comply, please contact us for a free consultation. Littledata has experience with every major analytics platform and a wide range of custom setups. However, as a number of global companies are concurrently prepping for compliance, we highly recommend that you get in touch sooner rather than later!

2018-02-13

How to improve AdWords retargeting using ecommerce checkout steps

In the ecommerce world, one of the smartest ways to improve ROI for marketing campaigns is to retarget customers who visited your website in the first place. These visitors are already in the market for the types of products that you sell, but how do you pull them back if they've dropped out of the checkout process? The most effective way to grab these customers is to target them based on where they dropped off. Luckily, Google lets you do exactly that: with the right analytics, you can set up retargeting campaigns based on checkout behaviour. At Littledata we've helped online stores in over 50 countries to improve marketing ROI using ecommerce tracking. In this post I share three simple steps you can take to improve your AdWords retargeting based on ecommerce checkout behaviour. 1. Set up accurate product tracking for your store Enhance Ecommerce tracking has been available from Google Analytics for a couple of years now. If you're already using this Google Analytics feature, good for you! Having product data means you can take advantage of this and create Audiences that then can be shared with AdWords (and other platforms). In order to improve AdWords retargeting using checkout steps, you must have checkout tracking and Enhanced Ecommerce enabled in Google Analytics. Then you can follow this checklist to set up accurate product tracking that can be used for Audiences in AdWords. Check out this resource (or share it with your lead developer): Google's Guide to Measuring a Checkout Repeat after me: "The fields must by dynamically populated! This is important!" Clarify where the checkout process starts and ends on your website (and again, if your developer is handling the setup make sure they're clear about each stage in your checkout funnel, including where  the process starts and stops) Set up checkout tracking based on that process Once this data is successfully coming into Google Analytics, you're ready to create Audiences and share them with AdWords At this point, it's important to mention that there are a lot of elements to Enhanced Ecommerce tracking and each part needs to be set up separately. For example, you will not automatically be tracking product categories, listings and details. If you're not sure how to implement the full extent of Enhanced Ecommerce, we're here to help. If you're using the Shopify platform, you're in luck, as our Shopify reporting app's audit feature checks for accurate product and checkout-step tracking, and automatically assists with setting these up for you. The app works directly with the Google Analytics setup for your Shopify store, so you don't have to deal with Shopify's native reporting, which doesn't let you see how users are progressing through the checkout process. 2. Analyse customer behaviour, including checkout steps Shopping cart abandonment is the most frequent complaint we hear from ecommerce marketers. Why does someone add products to their shopping cart and then just abandon it completely? This isn't common in brick-and-mortar stores, so why does it happen so often online? Remember that online shoppers don't want to leave those things behind. They were attracted to those products and have expressed the desire to buy. But with a bad checkout flow, too much information or too little, they'll fly away and leave behind only unloved products with high shipping costs or under-promoted benefits. One of the best Enhanced Ecommerce use cases is the Checkout Behaviour report. This is essentially a Shopping Cart Abandonment report, showing weaknesses in your checkout process and where to invest your time and money to convince users that have added-to-cart to go ahead and complete a purchase. Why is this important and relevant to AdWords? Well, everything in marketing is about perspective. The above report doesn't only show you where you could improve your checkout flow, but also where you've lost customers. 'Lost' is the key word here. If you're losing a significant percentage of customers at the shipping stage of your checkout process, this is an opportunity to improve - and to market those improvements using AdWords. For example, you might look at that report and ask yourself: Are you charging customers too much for shipping? You can't really change that cost for all carts (we know that shipping costs are significant) but you could, for example, offer free shipping to shoppers with items in their cart over some profitability margin. Retargeting those users in Google AdWords is an effective way to show them that you're ready to reward them for making large purchases from your online store. Are you limiting yourself to too few territories? Put your analysts to work to find out where customers that leave the purchase flow want their goods to be delivered. Can you extend your logistical capabilities, or do you have a brick-and-mortar store nearby where you can direct these shoppers? Use AdWords retargeting to let them know. Of course, Google Analytics' native reports aren't for everyone. If you find them confusing or haven't worked extensively with enhanced ecommerce data, check out Littledata's report packs. These automated reports are an easy but comprehensive way to read and interpret ecommerce data without any hassle. For the purposes of tracking checkout steps to improve retargeting, I'd recommend our Ecommerce behaviour pack, which includes reports on shopping behaviour by marketing channel and checkout steps. 3. Set up retargeting campaigns based on that data How do you retarget users in AdWords based on Google Analytics data? Fear not, my brave colleagues! If you've made it to this step, you shouldn't have any trouble creating powerful retargeting campaigns. First you'll need to create a new Audience. In your Google Analytics Admin, find Audience Definitions in the middle of the screen near the bottom. Click on New Audience. Click on Create New and on this screen go to Conditions and Filter Users to Include the steps you want to target with this Audience. Set the Shopping Stage to contain (equal) 'Checkout_Abandonment' or 'Checkout_1', 'Checkout_2', etc. - wherever your customers have been falling off and leaving a basket full of goodies without completing the purchase. (Note that this field is auto-completed, so give GA a second after you start typing to show the options here.) You'll then need to set a time period. Think about your specific business and how far back you want to go with the search. Once you're happy with your selection, pick which Google AdWords account you'll want to link to this new Audience. That's it! You're now ready to run PPC promotions to a buy-ready audience that would otherwise have disappeared. I hope you've enjoyed this quick guide. Please drop me a line below and let me know how you use checkout steps in relation to AdWords. I always love to hear how other specialists in the field combine platforms to create perfect marketing. PRO TIP: If you're in a country with Google Merchant available, you can benefit from dynamic remarketing. This does take some extra setup on the product level, so let us know if you have specific questions. (And stay tuned - we're planning some Google Merchant Center-related posts for the near future.)

2018-02-06

Our top 5 posts from 2017

We're an ecommerce analytics company, so it's no surprise that Shopify and Google Analytics top the list of topics in our most-read and most-shared posts of 2017. But what continues to surprise us is how many online businesses know that their analytics setup needs to be fixed, but put off the decision to take action. Luckily tools like our Shopify reporting app are making it easier than ever to get accurate data and automated reporting that really drives revenue. If fixing your tracking and making decisions based on trustworthy data wasn't your main new year's resolution for 2018, it should be! Here are the top 5 posts from our analytics blog in 2017. They should provide some inspiration. 1. Is Google Analytics compliant with GDPR? From May 2018 the new General Data Protection Regulations (GDPR) will come into force in the European Union, causing all marketers and data engineers to re-consider how they store, transmit and manage data – including Google Analytics. This popular post looks at basic and full compliance. The rights enshrined by GDPR relate to any data your company holds which is personally identifiable: that is, can be tied back to a customer who contacts you. 2. Shopify Marketing Events vs Google Analytics The ability for other Shopify apps to plug their campaign cost and attribution data into Shopify (via the new marketing events API) is a logical step to building Shopify’s own analytics capability, but is it really a viable substitute for Google Analytics? Google already has a team of hundreds working on Google Analytics, and it seems unlikely that Shopify will be able to dedicate resources to keep up with the functionality that power users need. 3. Is Google Analytics accurate? 6 common issues and how to resolve them How do you know if your Google Analytics setup is giving you reliable data? In this much-linked blog post we look at common problems and explain what can be done to make your tracking more accurate. If the journey of visitors on your site proceeds via another payment processor or gateway, you could be losing the link between the sale (or goal conversion) and the original marketing campaigns. 4. How to increase revenue with Refersion and affiliate marketing Affiliate marketing consistently outperforms other channels for ecommerce businesses. In this special guest post, our integration partner Refersion shares essential tips about how Littledata customers can get a piece of the action. When customers come through affiliate channels, their average customer revenue is 58% higher than other channels. 5. What you can track with our Shopify app Here at Littledata we believe that everyone should have access to professional-level analytics tools for tracking, reporting, and improving sales and engagement. That’s why we built the ultimate Shopify reporting app. This much-shared post outlines 'Shopify’s Standard Tracking vs Littledata for Shopify'. It's a match we're betting on! Shopify is one of the best ecommerce platforms on the planet, but their standard analytics are extremely limited.

by Ari
2018-01-11

How to set up demographics tracking in Google Analytics (VIDEO)

https://www.youtube.com/embed/PAeCubNxoKI Could you be missing out on your best customers - those that are more likely to convert, and more likely to make big purchases when they do? Watch this quick video to find out how to to set up demographics tracking in Google Analytics. Demographics and interests data provides information about the types of customers that are using your site, along with the interests they express through their online travel and purchasing activities. Once you set up this tracking, you'll be able to see your customer base broken down by age group, gender and interests. This data isn't just nice to have; it helps you market to the biggest potential spenders by discovering who's most interested in your products or services. Analytics and AdWords use the same age, gender, and interests categories, so this is particularly useful for improving your targeting on the Google Display Network. That said, connecting demographics data with shopping activity and revenue is a complicated art. Our popular Buyer Personas feature automates reporting and shows you how to improve that spend. And we don't just stop with paid ads. We include personas for every significant channel, including email marketing, organic search, affiliates/referrals and social media campaigns. Wherever you want to use demographics targeting to increase revenue, we've got you covered.

2017-12-05

How to dramatically increase revenue with Refersion and affiliate marketing

Affiliate marketing consistently outperforms other channels for ecommerce businesses. In this special guest post, Refersion's Robert Woo shares essential tips about how Littledata customers can get a piece of the action. Affiliate marketing is a powerful channel to drive sales, but is surprisingly overlooked by many small and medium-sized businesses. In a 2016 report by Heinz Marketing, referrals made the most positive impact on revenue for businesses, by far. As business owners know, the easiest sales come from customer recommendations to their friends and family. Especially for SMBs, word-of-mouth is often the backbone of how they acquire new customers. Now here’s another statistic: when customers come through affiliate channels, their average customer revenue is 58% higher than other channels. In other words, not only is it easier to get more customers via word-of-mouth, if they are referrals, but those customers also spend more. As you can see, getting into affiliate marketing is a double win for your business. But it can seem tricky to get started. The traditional way of doing affiliate marketing Online affiliate, or referral, marketing is as old as the internet. Here’s how it traditionally works: Research various affiliate networks that are accepting new merchants (that’s you). Pay a fee to join one (as high as $5000). Use this network to find affiliate partners to market your product/service. Pay out a commission to these partners. Pay out a monthly fee, and a portion of these commissions (15 to 25%) to the affiliate network. In this traditional way, you can see a clear trade-off for the benefit of joining an existing network. While you’ll have immediate access to many publishers waiting to market your product, there are a lot of fees for this privilege. So much so that for smaller businesses often find it hard to make a good profit from this model. On the other hand, you could start your own program up from scratch. But while you’d save a fortune in fees, the big trade off is your time investment. It takes time to put an affiliate marketing program in place. From creating a portal for your affiliates to use, to finding these influencers in the first place, to getting the hang of the metrics you need to monitor; it can all be a lot, especially for SMBs with a small team devoted to marketing. The better way, for Littledata customers Luckily, we here at Refersion have made it easy and affordable to forego joining an existing affiliate network and start your own. What we do is help businesses take a 'hybrid approach', taking the best of both worlds, making running a program cheap and simple. The best part? We’ve now integrated with Littledata to make data analysis even more insightful, so your business can easily maximize the ROI of your in-house affiliate marketing program. Used together, Littledata and Refersion are a supercharged toolbox for ecommerce entrepreneurs who have always wanted to launch a referral program, but was afraid to commit the time and energy. With Refersion, you can set up your business to start taking advantage of affiliate marketing in less than ten minutes. Connect your online shopping cart, create custom affiliate emails and coupon codes, and quickly find the right publishers to work with in the Refersion Marketplace. And if you’re already a Littledata customer, you’ll know that you can get all your affiliate marketing metrics and analysis in your dashboard and reporting. Don’t leave money on the table With the rise of ad blockers, many types of online marketing have taken big hits. But affiliate marketing isn’t subject to this limitation. Don’t ignore one of the best channels of getting new customers and higher sales! If you want to learn more about Refersion, watch this short intro video on how it all works. Ready to take the plunge? Here’s a special signup page for Littledata customers. Get a 14 day free trial today! Robert Woo is a Marketing Manager at Refersion.

2017-11-06

How to set up campaign tagging in Google Analytics (VIDEO)

https://www.youtube.com/watch?v=YVxi0sQmro0&t=5s Google Analytics is only as smart as your tagging. To lower average CPA and increase conversions in a sustainable way, you need an in-depth view of customer acquisition channels. Accurate campaign tagging makes it possible to get the data needed to understand acquisition costs based on particular source and medium. If you want to improve marketing ROI, it's essential to get campaign tagging right in Google Analytics. But how does it all work? Follow the simple rules in this quick how-to video to make sure you're getting accurate data about where your traffic is coming from. Questions addressed in the setup video: What is a campaign in Google Analytics (GA)? What is UTM Parameter and how do I use it? Is it possible that a large volume of my 'Direct' traffic in GA is actually coming from sources such as email or social, but just wasn't tagged correctly? How do I know? I want to see all email marketing campaign traffic as one line item in my GA reports. Do spellings matter? Are UTM parameters case-sensitive? What are the best practices for GTM tagging using the Google Analytics Link Builder? For more info on custom campaign tracking, check out this detailed post about campaign parameters and how to use them. Remember that when you set up new campaigns or marketing channels, things can change or get lost in the mix. It's important to keep an eye on your analytics setup. Even once you've successfully set up campaign tagging in GA, we recommend auditing your analytics on a regular basis. And don't stop there. Once you've established data accuracy, follow in the footsteps of the most successful ecommerce sites and use Buyer Personas to get a clear view of which types of customers are more likely to convert in each channel. Now that's smart growth, driven by data!

2017-10-31

Is Google Analytics compliant with GDPR?

From May 2018 the new General Data Protection Regulations (GDPR) will come into force in the European Union, causing all marketers and data engineers to re-consider how they store, transmit and manage data – including Google Analytics. If your company uses Google Analytics, and you have customers in Europe, then this guide will help you check compliance. The rights enshrined by GDPR relate to any data your company holds which is personally identifiable: that is, can be tied back to a customer who contacts you. The simplest form of compliance, and what Google requires in the GA Terms of Use, is that you do not store any personally identifiable information. Imagine a customer calls your company and using the right of access asks what web analytics you hold on them. If it is impossible for anyone at your company (or from your agencies) to identify that customer in GA, then the other right of rectification and right of erasure cannot apply. Since it is not possible to selectively delete data in GA (without deleting the entire web property history) this is also the only practical way to comply. The tasks needed to meet depends on your meaning of ‘impossible to identify’! Basic Compliance Any customer data sent ‘in the clear’ to GA is a clear break of their terms, and can result in Google deleting all your analytics for that period. This would include: User names sent in page URLs Phone numbers captured during form completion events Email addresses used as customer identifiers in custom dimensions If you’re not sure, our analytics audit tool includes a check for all these types of personally identifiable information. You need to filter out the names and emails on the affected pages, in the browser; applying a filter within GA itself is not sufficient. But I prefer a belt-and-braces approach to compliance, so you should also look at who has access to the Google Analytics account, and ensure that all those with access are aware of the need not to capture personal data and GDPR more generally. You should check your company actually owns the Google Analytics account (not an agency), and if not transfer it back. At the web property level, you should check only a limited number of admins have permission to add and remove users, and that all the users only have permission to the websites they are directly involved in. Or you could talk to us about integrations with your internal systems to automatically add and remove users to GA based on roles in the company. Full Compliance Other areas which could possibly be personally identifiable and you may need to discuss are: IP addresses Postcodes/ZIP codes Long URLs with lots of user-specific attributes The customer’s IP address is not stored by Google in a database, or accessible to any client company, but it could potentially be accessed by a Google employee. If you’re concerned there is a plug-in to anonymise the last part of the IP address, which still allows Google to detect the user’s rough location. ZIP codes are unlikely to be linked to a user, but in the UK some postcodes could be linked to an individual household – and to a person, in combination with the web pages they visited. As with IPs, the best solution is to only send the first few digits (the ‘outcode’) to GA, which still allows segmenting by location. Long URLs are problematic in reporting (since GA does not allow more than 50,000 different URL variants in a report) but also because, as with postcodes, a combination of lots of marginally personal information could lead to a person. For example, if the URL was mysite.com/form?gender=female&birthdate=31-12-1980&companyName=Facebook&homeCity=Winchester This could allow anyone viewing those page paths in GA to identify the person. The solution is to replace long URLs with a shortened version like mysite.com/form And for bonus points... All European websites are required to get visitors to opt in to a cookie policy, which covers the use of the GA tracker cookie. But does your site log whether that cookie policy was accepted, by using a custom event? Doing so would protect you from a web-savvy user in the future who wanted to know what information has been stored against the client ID used in his Google cookie. I feel this client ID is outside the scope of GDPR, but guaranteeing that the user on GA can be linked to opt-in consent of the cookie will help protect against any future data litigation. The final area of contention is hashing emails. This is the process used to convert a plain email like ‘me@gmail.com’ into a unique string like ‘uDpWb89gxRkWmZLgD’. The theory is that hashing is a one-way process, so I can’t regenerate the original personal email from the hash, rendering it not personal. The problem is that some common hashing algorithms can be cracked, so actually the original email can be deduced from a seemingly-random string. The result is that under GDPR, such email hashes are considered 'pseudonymized' - the resulting data can be more widely shared for analysis, but still needs to be handled with care. For extra security, you could add a ‘salt’ to the hashing, but this might negate the whole reason why you want to store a user email in the first place – to link together different actions or campaigns from the same user, without actually naming the user. There are ways around that strike a compromise. Contact Littledata for a free initial consultation or a GDPR compliance audit.

2017-10-19

5 steps to higher ecommerce search traffic

Search traffic is essential for ecommerce growth, and it takes time to build. In this guest post, SEO expert Bill Widmer highlights 5 easy steps to rise to the top. There are over 1 billion websites on the internet today, with almost 2.4 million websites created every day. Of those sites, only 10 make it to the front page of Google. And the top result gets 30% or more of all the search traffic. Where does that leave you? If you don’t take SEO seriously, there’s no way your ecommerce site will beat the competition. If you want to make tens of thousands of extra sales every year, without spending a dime on marketing, listen up. It’s time to boost your ecommerce search traffic. Step 1: Start a blog and produce high-quality content Don’t think you can get away with slapping together a few paragraphs about your latest collection and calling it a blog article. The content gods are watching! In all seriousness, quality content is crucial to ranking on the first page of Google. It’s one of their top 2 ranking factors to determine what to show (the other is backlinks). But what exactly does quality content entail? Let’s hear it from the horse’s mouth: Google's basic principles for high-quality content Make pages primarily for users, not for search engines. Don't deceive your users. Avoid tricks intended to improve search engine rankings. A good rule of thumb is whether you'd feel comfortable explaining what you've done to a website that competes with you, or to a Google employee. Another useful test is to ask, 'Does this help my users? Would I do this if search engines didn't exist?' Think about what makes your website unique, valuable, or engaging. Make your website stand out from others in your field. In a nutshell, Google wants you to focus on providing value to your readers with every blog article. Producing high-quality, long-form content (at least 1,500 words) is the key to ecommerce content marketing and pleasing the search gods. Pro Tip: Not sure what kind of blog articles to produce? As a general rule of thumb, steer clear from anything that’s too obvious and salesy (eg. 5 Shoes From Our Latest Collection That You’ll Love). Instead of this, try to produce content that’s useful to your customers (eg. How To Maintain Leather Shoes: A Comprehensive Guide). With these less salesy articles, you can still include links and call to actions for readers to shop your products after they’re done reading the article. As an added bonus, these articles can help you rank for keywords which your product and category pages can’t (such as 'how to maintain leather shoes'). Step 2: Fix your on-page SEO On-page SEO refers to elements which you can optimise within your website (off-page SEO, on the other hand, deals with external links and other factors). Image from FlightMedia.co With on-page SEO, the first thing you need to do is select the keywords you want to target. Once you’ve got your keywords in mind, optimize your title, header tags, content, image alt texts, and metadata for each page and post on your website. If this sounds like Greek to you, don’t stress. Here’s a step by step guide which will take you through the entire process. Pro Tip: Only target one keyword per page to increase your chances. However, it’s always a good idea to include LSI keywords! Step 3: Add internal links to your most important pages By adding internal links (links from one page on your site to another page on your site), you’re helping Google to understand the relationship between the different pages and posts on your ecommerce site. The more internal links a specific page or post on your website has, the more 'important' it is deemed by Google. Think of your website as a pyramid, with the most important content - your 'cornerstone' content - at the top. You should be linking from your cornerstone content to other related pages in order to pass on link value to them. At the same time, link to these cornerstone pages from other pages in order to bolster their standing. Want to learn more about internal links? Check out this article. Step 4: Build external links Once your internal links are done, it’s time to move on to building external links. You might need to invest some budget into this, but since Google has confirmed that external links are amongst the top 3 ranking factors, I’d say it’s definitely worth your while. First, look for influencers in your industry and reach out to them to enquire if they’d be willing to link to your website in exchange for a small fee OR for a partnership. You can use platforms such as Mailshake and VoilaNorbert to speed up the communication process. Another way of getting backlinks is to guest-post on other websites. Whilst this typically takes longer to execute, it’s a great way of building your brand and establishing thought leadership whilst getting more backlinks. Step 5: Consider paid traffic Assuming you’ve completed all the above steps (and you reallllly should!), this doesn’t mean you’ll see results overnight. It’ll take some time (a few months, or even a year) for you to experience a boost in your organic traffic. In the meantime, you can consider 'supplementing' with paid traffic. Image from ThinkDigi.org The two most commonly used channels are Facebook Ads and Google Ads - and there are tons of useful resources online that will teach you all the basics (read this guide for Facebook ads or this guide for Adwords). Alternatively, if you don’t want to handle your ads yourself, you can always outsource them to an expert. Once those ads are running, a full-cycle analytics platform like Littledata is essential to help you optimise your ad spend and connect it to revenue. After all, the idea isn't just to get more traffic, but to get the best kind of traffic and sell to your best type of customer - the kind that's more likely to convert. The truth about ecommerce growth A few parting words. A lot of ecommerce store owners think that as they become more established, they’ll automatically have more people visiting their website. The truth is, word of mouth can only get you so far - and if you’re serious about growing your ecommerce store and increasing your profits, you’ll need to boost your search traffic through SEO and the other methods discussed above. And you'll want to optimise that search traffic by paying attention to specific metrics such as bounce rates from mobile Google search. Do you want to see a nice exponential curve in your search traffic analytics, or are you content to have your traffic flatlining? The sooner you get started, the sooner you’ll be able to snag that highly coveted spot in the first page of Google. I’m rooting for you! Bill Widmer is a content marketing and SEO expert who has worked with many well-known brands like Content Marketing Institute, Social Media Examiner, and SEMrush.

2017-10-05

Introducing Report Packs

We're excited to announce that the first automated report packs are live in the app! Each pack contains a curated set of reports proven to help ecommerce businesses scale faster and smarter. Looking for next-gen analytics reporting that doesn't break the bank? We developed report packs to make advanced analytics accessible to every customer - in just the right combination. You can subscribe to an entire pack for one low monthly price. Why we built report packs Call us crazy, but we believe that every ecommerce business should have the tools to automatically transform their Google Analytics data into actionable insights. Otherwise, what's the point of all that tracking? Unlike the reporting features in some other analytics apps, Littledata's reports never sacrifice accuracy for usability, nor the other way around. Put simply: we have no time for fluff. We believe that the most useful analytics can - and must - be both clean and accurate, and we've built the app's reporting functionality around the actual reporting needs of successful ecommerce businesses, based on our experience with enterprise customers, Shopify stores, and some of the biggest charities in the world. Our analysts considered the many setups we’d built for customers on top of the core Littledata app, and the idea for report packs grew out of this work. We found that growth-oriented ecommerce businesses weren't just looking for clutter-free analytics, but the right combinations of reports to guide ad spend, marketing channel priorities, ecommerce site design and customer journeys. As a result, report packs are next-gen reporting with just enough algo-awesomeness to keep the data geeks happy while letting your marketing team focus on actionable insights to increase engagement at every stage of the shopper journey, from first views and clicks to repeat buying behaviour. The first three packs We've launched three report packs to start: a Basics pack, an Ecommerce Performance pack, and a Shopify marketing pack. Basics pack Overview of site performance Sessions and bounce rate by city Sessions by device type Pages where users enter and exit The Basics pack includes four essential reports on site performance and user behaviour. It's a must-have for any ecommerce site with active users, whether you have a ton of conversions or are still growing your shopper base. Ecommerce Performance pack Overview of ecommerce stats Product category performance Number of sessions to make a transaction Number of days until a purchase is made Many Littledata customers use an enhanced ecommerce setup in Google Analytics. With four essential reports on shopping behaviour and store performance, the Ecommerce Performance pack will help you get the most out of that setup and make data-driven decisions for rapid growth. Shopify pack Conversion rate by marketing campaign Conversion rate by marketing channel When users are most likely to buy Shopping behaviour by channel The Shopify pack includes four reports that connect marketing channels with shopping behaviour. Built to give our Shopify app users a pro reporting experience, the pack contains essential analytics for growing a Shopify store through intelligent targeting. Anticipated addition to our reporting feature set Report packs offer high value at a lower price point by automating data collection and presentation based on proven ways to use and interpret Google Analytics data. Even though they're newly launched, they've already become a much-used feature alongside our popular custom reports, which agencies and large ecommerce stores use to dig deeper into marketing channels and user behaviour specific to their site design and business models. We recommend starting with one report pack and then adding more packs and custom reports to fit your needs. Subscribe to a report pack today to lock in an early-bird discount and start making better-informed marketing and product decisions. New to Littledata? Sign up for a free analytics account. PS. Our developers are hard at work on a number of new report packs, including packs for enhanced ecommerce, ReCharge subscription businesses, email marketing, Facebook ad performance, and more. Subscribe to this blog for the latest updates.

by Ari
2017-09-20

How to see shopping behaviour for each product you sell (VIDEO)

[embed]https://www.youtube.com/watch?v=YVGAdHTkw3s[/embed] Product performance can seem confusing, but it doesn't have to be. In this quick video, we show you how to use Google Analytics to see shopping behaviour related to each product you're selling. All you'll need to see this report is a site connected to Google Analytics with the Enhanced Ecommerce plugin setup. Using the Shopping Behavior report in Google Analytics Whether your ecommerce site is large or small, the Shopping Behavior report makes it easy to drill deep into user behaviour to understand why some products are converting better than others. If a particular product isn't selling well, the Shopping Behavior report will help you figure out why. It shows how far shoppers engage with your products, from initial list views through to shopping cart activities. Reasons a product might not be selling well It isn't at an optimal place in a product list or display The product details, such as images and description, aren't sending the right message Customers are abandoning their shopping carts completely, or removing that particular product (or group of products, such as multiple pairs of jeans) after adding it Who knows? You haven't audited your Google Analytics setup lately so your customer behaviour data can't be trusted to help you improve Each of those issues requires different actions, sometimes by entirely different departments (ie. marketing, pricing, ux)! That's what makes the Shopping Behavior report so important for improving ecommerce sales and conversions. We hope you enjoyed this latest video in our series of Google Analytics how-to guides. Need help setting up Enhanced Ecommerce in Google Analytics, or ensuring that your data is accurate? Contact a Littledata consultant today.

2017-09-14

5 (bad) reasons not to do a Google Analytics audit

Does this sound familiar? 'We know our data's bad, but we don't have the time or resources to fix it'. Or, even worse: 'I checked a bunch of other metrics and they didn't justify our current ad spend, so I think I'll just present that same old report at the meeting today...again. Luckily we haven't fixed our Google Analytics setup to track too much relevant data about other marketing channels, or to connect those channels directly to revenue, because then we might need to change our whole strategy!' There's still a lot of confusion out there about the role and scope of an analytics audit. With a free audit tool directly in the  app, Littledata is on a mission to change this. Here are some (slightly exaggerated) versions of common objections to doing an analytics audit, and how to overcome them. 1. You don't know what a Google Analytics audit is Okay, not to start this somewhat ironic post with an entirely un-ironic objection, but not understanding the process is probably the only good reason not to audit your analytics setup. Luckily an analytics audit is actually very straightforward: it's simply a check of your analytics configuration and implementation. Some consultants and last-gen apps can make the audit process seem confusing and disorienting. If that's been your experience, we're here to help. Our free Google Analytics audit tool explains the process in real time. Not only that, but many tracking and reporting issues can be fixed automatically by the app (hello, intelligent algorithm!). 2. You don't believe in marketing ROI There are a lot of fluffy tools out there. Google Analytics isn't one of them. It's not that all digital marketers take action based on analytics, but a majority of the top ones do. That's what makes them the best. If you need convincing that accurate data is the secret sauce behind higher marketing ROI (return on investment), check out the recent Google Analytics research with Econsultancy, where they found that '60% of leading marketers routinely take action based on analytics, and are also 48% more likely than mainstream marketers to say their strategy is strongly data-driven'. 3. You trust everything you read online Failing to audit your analytics setup is basically the same as believing that everything you read online is true, no matter the source. Why? Because bad data produces bad reports. This is true no matter how fancy your reporting templates might be, or how much time you've spent making spreadsheets of Google Analytics data look accessible. Unless you regularly audit your analytics setup, how do you know if you're tracking the right things in the right manner? This is especially true if you're using an otherwise awesome ecommerce platform like Shopify, which has notoriously questionable tracking that also happens to be easy to fix with the right analytics app. 4. You think that the customer is always wrong Customer happiness isn't just a buzzword, it's increasingly what's driving the growth and expansion of online businesses, especially in the ecommerce space. Big players like Amazon learned this early on, and they built an effective - and addictive - customer experience around heaps of data on everything from affiliate ads to repeat buying activity. Think you don't have access to those same tools? Think again. If you want to build a better customer experience, it's essential to start with the correct Google Analytics setup and end the guessing games about where your leads and customers come from, and how they act. That's where the audit comes in. 5. You're betting on failure Are you betting that your own company will fail? Unless you secretly run an ecommerce hedge fund and have shorted your own startup, this is probably a bad idea. Auditing your data tracking across the customer life cycle is a sure way to see what's working, what's not, and what can be improved. Otherwise you're stuck with bad data and revenue tracking that might not have much to do with the reality - or the future - of your online business. Is there a better way? Look, we get it. Change can be scary, but choosing to stay stuck in the same data rut isn't the way forward. We've helped over a thousand online businesses fix their Google Analytics setup to capture accurate, relevant data. Littledata's industry-leading automated audit tool is free to run as often as you'd like. Sign up today and start trusting your data.

by Ari
2017-09-07

How to set up Site Search tracking in Google Analytics (VIDEO)

https://www.youtube.com/watch?v=OlsMBWFt5aQ What are visitors searching for on your website? Watch this quick video to learn how to set up Site Search tracking in Google Analytics. Site Search makes it easy to track search activity on your site. In the video we show you how to: Set up Site Search for a web property connected to your Google Analytics account Understand which query parameters you're using, and apply them to your Site Search setup View the resulting search metrics, including visits with search, total unique searches, specific search terms (what web visitors are searching for), and search depth Tracking on-site search terms is surprisingly easy! All you'll need to get started is a Google Analytics account and a search box on your site. What are visitors searching for on your site? On-site search is one of the things we scan for with our free Google Analytics audit tool. Many online businesses forget to add this to their Google Analytics setup, focusing instead on external search data such as that from Google AdWords (or ignoring search activity altogether!), but this is a mistake. Capturing on-site search terms is essential for any online business that that is serious about growth. Understanding what web visitors are searching for - and how that leads to deep engagement with your site or app - can help you improve site UX (user experience), develop product offerings which your customers are already hungry for, and get a higher ROI from product marketing campaigns and ad spend. For more details on the Site Search feature and how to identify search query parameters, check out the Google Analytics help guide. Still have questions? The Littledata team is always here to help. You can contact us directly in the app, or feel free to connect with our Google Analytics consultants for larger projects. Hint: Use search-related benchmarks to find out how your on-site search traffic compares with other sites in your industry and location. The Littledata app includes analytics benchmarks to make this as easy as possible. For example, you can compare usage of internal search on your website against internal search usage on all websites. Once you've set up Site Search, you will automatically be able to see relevant search-related benchmarks in your Littledata dashboard.  

2017-09-06

The end of the ecommerce 'thank you' page

For two decades the ecommerce customer journey has stayed roughly the same. Customers browse, add to cart, checkout, and then see a page confirming their purchase: the 'thank you' page. That last step is changing, and this is no small change as it threatens to break how many sites measure purchases. Ecommerce stores that stop using a final 'thank you' page without adjusting their analytics setup accordingly are in danger of getting inaccurate purchase data, or even losing track of shoppers altogether. In order to help our customers get ahead of the curve, we've gone through a number of test cases to find short and long term fixes to this issue. But first, a little history. In the old days... In the early days of ecommerce the biggest barrier during checkout was trust. Retailers paid to be certified as ‘hack-proof’ and customers wanted to make quite sure when and how their money was taken. Fast forward twenty years to today, and in the developed world most consumers have transacted online hundreds of times. They are familiar with the process, expect a seamless user experience, and confident that when they click 'buy' their payment will be taken and the products delivered. Online shoppers are so confident, in fact, that an increasing number we observe don’t even bother waiting for that ‘thank you for your order’ page. That page is becoming redundant for three reasons: Almost every checkout process captures an email address to send an order receipt to, and the email acts as a better type of confirmation: one that can be searched and referenced. Seriously, when was the last time you opted to ‘print the confirmation page’ for your records? Many retailers are forced to compete with the superb customer support offered by Amazon. This includes refunds for products that were ordered in error, and quick handling of failed payments. So from a customer's perspective there’s little point in waiting for the confirmation page when any issues will be flagged up later. Which leads to the third reason: as retailers improve the speed of checkout, the payment confirmation step is often the slowest, and so the one where customers are most likely to drop out on a slow mobile connection. This is no small issue, as mobile revenues are expected to overtake desktop revenues for ecommerce businesses globally this year. What does this mean for ecommerce sites? The issue is that for many sites the linking of sales to marketing campaigns is measured by views of that ‘thank you' page. In the marketing analysis, a ‘purchase’ is really a view of that 'thank you' page - or an event recorded on the customer’s browser with the sale. If customers don’t view the page, then no sale is recorded. If you have ever been frustrated by the lack of consistency between Google Analytics and your own payment/back-end records, this is the most likely issue. A dependency on viewing the 'thank you' page brings other problems too: a buggy script, perhaps from another marketing tag, will block the recording of sales. This is another source of the type of analytics inaccuracy which the Littledata app combats automatically. How to adjust your ecommerce tracking The short-term fix is to tweak the firing order of marketing tags on the 'thank you' page, so that even customers who see the page for fractions of a second will be recorded. Sites with a large number of marketing tags will have the greatest room for improvement. But in the long term, as this trend continues, the analytics solution is to link the marketing campaigns to the actual payments taken. This removes the need for the customer to see any type of 'thank you' or confirmation page, and also removes discrepancies between what your marketing platform tells you was purchased and what actually got bought. This is known as server-side tracking. The good news for those of you on the Shopify platform is that our Shopify reporting app does this already - and solves a lot of other analytics problems in one install. For those on other stores, please do contact us for advice. The Littledata team has worked with ecommerce businesses to set up integrations with Magento, DemandWare and numerous custom platforms. Not only can we help fix your analytics setup for accurate tracking, but our app then automates the audit and reporting process for all of your sites going forward.

2017-08-30

How to add tracking for multiple websites or apps (VIDEO)

If you're tracking multiple sites or apps in Google Analytics, you can connect all of these views to your Littledata account and easily switch between them. Watch this quick video to learn how to add or remove a Google Analytics data source in the Littledata app. FAQs - Working with multiple Google Analytics views How do Littledata reports link to Google Analytics views? When you click to set up another site you will see a list of all the Google Analytics properties and views linked to your Google account. Typically you will only be interested in one of the views, which contains data for the site or app you are working on. When you select a view, Littledata fetches the data it needs to enable core features such as our intelligent Google Analytics audit and industry benchmarking. Note that this doesn't commit you to purchase anything. The underlying data in your Google Analytics account is not affected unless you opt-in to our automated fixes, which let you automatically fix particular aspects of your Google Analytics setup. How many websites or apps can I track? You can set up standard reporting for as many websites as you like. However, if you're using Littledata's Pro services for advanced custom reporting, this is priced per view or data source. You can switch between these sites using the drop-down menu in the top bar. Does your reporting work with mobile app properties? Right now, some of the features will work - such as dashboards, alerts and buyer personas - but audit and benchmarking are specifically for websites. How do I add or remove a site? Once you've connected multiple web properties to your Littledata account, you can manage them using the My Sites page under the profile photo drop-down menu in the upper right. Can Littledata handle micro-sites? Yes. If each micro-site have it's own Google Analytics view, then go ahead and connect them all to your Littledata account. If the micro-sites are all under one web view, then ask the Littledata team about custom solutions to create a multi-site dashboard that lets you visualise Google Analytics data from many micro-sites and benchmark against each other. We have done this for a range of customers and are happy to discuss the details of what is involved in reporting on multiple micro-sites, whether just a few or several hundred!

2017-08-02

What you can track with Littledata's Google Analytics reporting app for Shopify

Here at Littledata we believe that everyone should have access to professional-level analytics tools for tracking, reporting, and improving sales and engagement. That's why we built the ultimate Shopify reporting app. Shopify is one of the best ecommerce platforms on the planet, but their standard analytics are extremely limited. Even if you have a Shopify Plus plan with Acquisition and Behaviour reports, this default reporting misses out on essential metrics for understanding how to improve sales and conversions on your site. How can you expect to improve marketing ROI without marketing-channel attribution for every type of sale in your store? How can you expect to increase sales without a clear picture of shopping cart behaviour? And how can you grow a business unless you understand what share of your sales comes from repeat buying versus new customers? Here's a table detailing what you can track with the Littledata Shopify app: Shopify's Standard Tracking vs Littledata for Shopify Standard Tracking in Shopify The Littledata Shopify App  Essentials Transaction volumes in Shopify match volumes in Google Analytics  ✓ Sales attribution to the source of the visit (marketing channels and other sources)  ✓ Track what pages users see  ✓  ✓ Demographics tracking (age group, interests, etc.)  ✓  Ecommerce behaviour Track what lists are viewed divided by category  ✓ Track the product position in a list  ✓ Track the clicks on products in a list  ✓ Track the product page views  ✓  ✓ Track what products are added to cart  ✓  ✓ Track what products are viewed in the cart  ✓ Analyse the data by product variants (colour, size, etc.) ✓ Track checkout steps (cart, billing, shipping, payment)  ✓ Analyse what order coupons / discount codes perform best  ✓  ✓  Recurring payments Track ReCharge renewals  ✓ Differentiate ReCharge sales from normal sales  ✓  Extra accuracy Deduct returns from total transactions  ✓ Track in Google Analytics by User ID from Shopify  ✓ Exclude traffic from payment gateways like paypal.com  ✓ Exclude spam traffic from Google Analytics data set  ✓ Capture on-site search terms  ✓ Store currency matches Google Analytics currency  ✓ Store timezone matches Google Analytics timezone  ✓ The Littledata app makes all of this remarkably easy. It guides you through the correct Google Analytics setup for your Shopify store, then provides curated reports and analytics to help you make sense of your new stream of reliable data. You don't have to be a Google Analytics expert to use Littledata's Shopify app. In fact, the app works best for product and marketing teams that are eager to learn about the big power of little data. We simplify the setup process and streamline the reporting process. It's that simple. Try it today for free in the reporting section of the Shopify App Store and see for yourself!  

2017-07-14

How to install our Shopify reporting app (VIDEO)

[embed]https://www.youtube.com/watch?v=I3c8OuqDj_8[/embed] Watch this quick video to learn how to install our Google Analytics Shopify app. The popular reporting app makes it easy to get better Google Analytics data about your Shopify store. To install Littledata's Shopify app and start trusting your data, follow the easy steps in the video: Get the app Authorise Google Analytics (GA) access Pick the existing GA data for your site Our app runs the migration process on your store Swap in Littledata's tracker (in your Shopify store admin) Confirm and go live! This video covers the basic setup process for fixing your data collection and setting up accurate tracking. But that's only the beginning of what the app can do for you. Once you've successfully installed the app and fixed your analytics setup, we recommend making daily use of your new analytics dashboard, setting up custom reports and alerts, and checking out relevant ecommerce benchmarks. Shopify stores love how the app automatically shows you the most important metrics for your sales and marketing. With a clear view of the complete user lifecycle -- from marketing channel engagement, to shopping cart activity, to repeat buying -- the sky's the limit!

2017-06-30

Is Google Analytics accurate? 6 common issues and how to resolve them

Our customers come from a range of industries, but when they first come to the Littledata app for help with fixing their analytics, they share a lot of common questions. First of all, is Google Analytics accurate? How do you know if your Google Analytics setup is giving you reliable data? In this blog post we look at common problems and explain what can be done to make your tracking more accurate. Google Analytics is used by tens of millions of websites and apps around the world to measure web visitor engagement. It won’t measure 100% of visitors – due to some users opting out of being tracked, or blocking cookies – but set up correctly, it should be measuring over 95% of genuine visitors (as opposed to web scrapers and bots). What are the common things that go wrong? The six most common issues with Google Analytics -- and how to resolve them 1. Your tracking script is wrongly implemented There are two common issues with the actual tracking script setup: 1) when it is implemented twice on some pages, and 2) when it is missing completely from some pages. The effect of duplicating the script is that you’ll see an artificially low bounce rate (usually below 5%), since every page view is sending twice to Google Analytics. The effect of the tracking script missing from pages is that you’ll see self-referrals from your own website. Our recommendation is to use Google Tag Manager across the whole site to ensure the tracking script is loaded with the right web property identifier, at the right time during the page load. 2. Your account has lots of spam When it comes to web traffic and analytics setup, spam is a serious issue. Spammers send 'ghost' referrals to get your attention as a website owner. This means that the traffic you see in Google Analytics may not come from real people, even if you have selected to exclude bots. Littledata’s app filters out all future spammers and Pro Reporting users benefit from having those filters updated weekly. 3. Your own company traffic is not excluded Your web developers, content writers and marketers will be heavy users of your own site, and you need to filter this traffic from your Google Analytics to get a view of genuine customers or prospects. You can do this based on location (e.g. IP address) or pages they visit (e.g. admin pages). 4. One person shows up as two or more users Fight Club aside (spoiler alert), when the same person re-visits our site we expect them to look the same each time. Web analytics is more complicated. What Google Analytics is tracking when it talks of ‘users’ is a visit from a particular device or browser instance. So if I have a smartphone and a laptop computer and visit your site from both devices (without cross-device linking) I’ll appear as two users. Even more confusingly, if I visit your site from the Facebook app on my phone and then from the Twitter app, I’ll appear as two users – because those two apps use two different internet browser instances. There's not a lot which can be done to fix that right now, although Google is looking at ways to use it's accounts system (Gmail, Chrome etc) to track across many devices. 5. Marketing campaigns are not attributed to revenue or conversions If the journey of visitors on your site proceeds via another payment processor or gateway, you could be losing the link between the sale (or goal conversion) and the original marketing campaigns. You will see sales attributed to Direct or Referral traffic, when they actually came from somewhere else. This is a remarkably common issue with Shopify stores, and that’s why we built a popular Shopify reporting app that solves the issue automatically. For other kinds of sites, the issue can often be resolved by setting up cross-domain tracking. 6. You aren't capturing key events (like purchases or button clicks) Google Analytics only tracks views of a page by default, which may not be meaningful if you have a highly interactive website or app. Sending custom events is the key to ensuring that your tracking is both accurate and relevant. Doing so is made easier with Google Tag Manager makes this easier than it would be otherwise, but you may need to speak to a qualified analytics consultant to decide what to track. If you want more certainty that your analytics is fully accurate, try Littledata's free Google Analytics audit or get in touch for a quick consultation. We <3 analytics and we're always here to help.

2017-06-27

Why are all my transactions coming from Direct or Referral in Google Analytics, with no marketing attribution?

Connecting marketing data with sales data is an age-old problem, and the crowded digital landscape has made this even more complicated. Google Analytics is supposed to give you the power to attribute sales (or purchase transactions) back to marketing campaigns, but this doesn't happen automatically. The good news is that it's entirely possible to get the right marketing channel attribution for sales activities. Accurate marketing attribution starts with the right Google Analytics (GA) setup. Start by asking yourself the following troubleshooting questions. These steps will help you figure out if your GA setup is correct, and how to use GA to get a complete view of user behaviour. Trustworthy GA setup takes a bit of work, but with a smart analytics dashboard like Littledata, much of that work can be automated. In fact, steps 1 through 4 can be checked automatically with our free Google Analytics audit tool. First of all, are you checking the right report? The best way to see the attribution is in the 'Channels' report in Google Analytics, under the 'Acquisition' section: 1. Have you got a large enough sample to compare? Firstly, can you be sure the sales are representative? If you only have two sales, and both are ‘Direct’, that could be a fluke. We recommend selecting a long enough time period to look at more than 50 transactions before judging, as with this example:   2. Is the tracking script on your purchase confirmation page setup? It you are getting some transactions recorded, but not 100%, then it may be possible to optimise the actual tracking script setup. See our technical guide to ecommerce tracking. This can be a particular problem if many of your sales are on mobile, since slower page load speeds on mobile may be blocking the tracking script more often.   3. Have you got a cross-domain problem? If you see many of your sales under Referral, and when you click through the list of referrers it includes payment gateways (e.g. mybank.com or shopify.com), that is a tell-tale sign you have a cross-domain problem. This means that when the buyer is referred back from the payment domain (e.g. paypal.com), their payment is not linked with the original session. This is almost always a problem for Shopify stores, which is why our Shopify app is essential for accurate tracking.   4. Is your marketing campaign tagging complete? For many types of campaign (Facebook, email etc), unless you tag the link with correct ‘UTM’ parameters, the source of the purchaser will not be tracked. So if a user clicks on an untagged Facebook Ad link on their Facebook mobile app (which is where 80 – 90% of Facebook users engage) then the source of their visit will be ‘Direct’ (not Social). Untagged email campaigns are a particular issue if you run abandoned cart / basket emails, as these untagged links will be 'stealing' the sales which should be attributed to whatever got the buyer to add to cart. Tagging is a real problem for Instagram, since currently the profile link is shown in full - and looks really messy if you include all the UTM parameters. We recommend using a service like Bitly to redirect to your homepage (or an Instagram landing page). i.e. The link redirects to yoursite.com?utm_medium=social&utm_source=instragram&utm_campaign=profile_link.  Read Caitlin Brehm's guide to Instagram links.   5. (only for subscription businesses using Littledata) Are you looking at only the first time payments? Tracking the source of recurring payments is impossible, if the tracking setup was incorrect at the time of the first payment. You can’t change Google Analytics retrospectively I’m afraid. So if you are using our ReCharge integration, and you want to track lifetime value, you will have to be patient for a few months as data from the correct tracking builds up.   6. Is a lot of your marketing via offline campaigns, word of mouth or mobile apps? It could be that your sales really are ‘direct’: If a buyer types in the URL from a business card or flyer, that is ‘Direct’. The only way to change this is to use a link shortener to redirect to a tagged-up link (see point 4 above). If a user pastes a link to your product in WhatsApp, that is ‘Direct’. If a user sees your product on Instagram and clicks on the profile link, that is ‘Direct’. Please let us know if there are any further issues you've seen which cause the marketing attribution to be incorrect.

2017-06-13

How goals work in Google Analytics

Every business, in order to grow, needs to set up a certain number of objectives and KPI’s. Once they are set, you can actually track the way your business evolves and see if they are met or you underperformed. This is where Goals come in: the actual definition that Google has on their support page: “A goal represents a completed activity, called a conversion, that contributes to the success of your business.” In this article, I will answer to the following questions: What are Goals in Google Analytics? When should you use the Goals and what for? What are Goals in Google Analytics?   Goals are specific user actions that you can then use in other reports (such as landing pages or channels) to see whether users engaged with your website. It is really important to remember that you have a maximum of 20 goals for each Google Analytics view. Also, once you set up a Goal, it will stay set up forever in the view. You can disable goals, but you will still have them there. You need to make sure that the objectives you set up are relevant to your business and they can be monitored by using the goals. What Goals should you track and why? The four big categories that in which you can track Goals in Analytics are: URLs Time Pages per visit Custom Events The most useful of these are custom events. Here are some of the areas you should definitely consider tracking with custom events: Leads – If your site has a sign-up or contact form for enquiries you'll want to track how many users successfully complete it - especially because you want to convert those leads. Newsletter sign-ups – Newsletters are really important for many businesses, to educate the people that are interested in your business. Tracking what drives users to sign up for these emails will help you do a better at content strategy. White paper and E-book downloads – Make sure your buyers' journey is set up properly and that you make this is a priority in your objectives. By tracking these two really specific actions, you will be able to up-sell your key product, software or service. Trial sign-ups – If you follow a freemium business model or if you use the trial period in order to engage your users, then tracking this area will definitely give you insights into who is your ideal persona. This article from The Digital Marketing Institute will also show you other metrics you should monitor.   Happy Reporting. Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-05-24

How to add account edit permissions for Google Analytics

Being able to edit the Google Analytics account is the 2nd highest permission level. You need this if you want to create a new web property in Google Analytics. To grant permissions to another user you will need the highest permission level yourself: being able to manage users on the account. Step 1: Go to account user settings page First click the admin cog in any view under the account in GA you want to change, and then in the left hand list go to User Settings   EITHER Select an existing user from the list and click the 'edit' checkbox OR Add a new user's email (must be a Google account) and check the 'edit' checkbox. Step 3: Check it's working Your colleague should now be able to see 'Create new property' under the list of properties in the middle of the Admin page.

2017-05-16

Shopify Marketing Events vs Google Analytics

At the Shopify Unite conference today I heard plenty of great ideas such as ShopifyPay but the most interesting for me as a data specialist was the marketing events API. Since we launched our Fix Google Analytics Shopify app earlier this year we’ve known that reporting was a weak spot in Shopify’s platform offering, and they admit that ‘understanding marketing campaign performance’ is one of the biggest challenges of Shopify merchants right now. The ability for other Shopify apps to plug their campaign cost and attribution data into Shopify (via the marketing events API) is a logical step to building Shopify’s own analytics capability, but I don’t believe it will be a substitute for Google Analytics (GA) anytime soon. Here’s why: 1. Google Analytics is the industry standard Every online marketer has used Google Analytics, and many have favourite reports they’ve learned to interpret. Moving them to use a whole new analysis platform will take time– and it’s taken GA 10 years to achieve that dominance. 2. GA provides platform-agnostic data collection For a store using Shopify as their only source of insights, moving away from Shopify would mean losing all the historic marketing performance data – so it would be very hard to make like-for-like comparisons between the old platform and the new. Many of our customers have used GA during and after a platform shift to get continuous historical data. Which ties into my first point that over 85% of businesses have a history of data in GA. 3. Incomplete marketing tagging will still cause issues Making valid analysis on multi-channel marketing performance relies on having ALL the campaigns captured - which is why our GA audit tool checks for completeness of campaign tagging. Shopify’s tracking relies on the same ‘utm_campaign’ parameters as GA, and campaigns that are not properly tagged at the time cannot be altered retrospectively. 4. Google is rapidly developing Google Analytics I’d like to see the Shopify marketing event collection evolve from its launch yesterday, but Google already has a team of hundreds working on Google Analytics, and it seems unlikely that Shopify will be able to dedicate resources to keep up with the functionality that power users need. 5. More integrations are needed for full campaign coverage Shopify’s marketing analysis will only be available for apps that upgrade to using the new API.  Marketing Events has launched with integrations for Mailchimp and Facebook (via Kit) but it won’t cover many of the major channels (other emails, AdWords, DoubleClick for Publishers) that stores use. Those integrations will get built in time, but until then any attribution will be skewed. 6. GA has many third-party integrations Our experience is that any store interested in their campaign attribution quickly wants more custom analysis or cuts of the data. Being able to export the data into Littledata’s custom reports (or Google Sheets or Excel) is a popular feature – and right now Shopify lacks a reporting API to provide the same customisations. You can only pull raw event data back out. That said, there are flaws with how GA attribution works. Importing campaign cost data is difficult and time consuming in GA – apart from the seamless integration with AdWords – and as a result hardly any of the stores we monitor do so. If Shopify can encourage those costs to be imported along with the campaign dates, then the return on investment calculations will be much easier for merchants. I also think Shopify has taken the right pragmatic approach to attribution windows. It counts a campaign as ‘assisting’ the sale if it happens within 30 days of the campaign, and also whether it was ‘last click’ or ‘first click’. I’ve never seen a good reason to get more complicated than that with multi-channel reports in GA, and it’s unlikely that many customers remember a campaign longer than 30 days ago. In conclusion, we love that Shopify is starting to take marketing attribution seriously, and we look forward to helping improve the marketing events feature from its launch yesterday, but we recommend anyone with a serious interest in their marketing performance sticks to Google Analytics in the meantime (and use our Shopify app to do so).

2017-04-21

What are smart goals ?

Smart goals measure the most engaged visits to your website and automatically turn those visits into Goals, even if you don't have conversion or ecommerce tracking. Then use those Goals to improve your AdWords bidding. This article will explain exactly what smart goals are and how to use this feature. I need to start by saying that if you want to use Smart Goals you need to have an Adwords account linked to your Google Analytics in order to enable this feature and you need Edit permission at the view level in order to get the setup done. Also, the linked AdWords account must have sent at least 500 clicks to the selected Analytics view over the past 30 days before you can set up Smart Goals. (If the linked account falls below 250 clicks over the past 30 days for the selected view, Smart Goals will be deactivated until the clicks rise again to 500 or more) Smart goals are recommended to be used when you aren't measuring conversions. Smart Goals is an easy way to use your best sessions as conversions. You can then use Smart Goals to optimize your AdWords performance based on the 'best sessions' pattern. Smart Goals are configured at the view level. Smart Goals feature from Google Analytics is the result of machine learning technologies. These algorithms will examine dozens of signals about your website sessions to determine which of those are most likely to result in a conversion. Each session is assigned a score, with the "best" sessions being translated into Smart Goals. Some examples of the signals included in the Smart Goals model are Session duration, Pages per session, Location, Device and Browser. (Remarketing Smart Lists use a similar machine learning model to identify your best users.) To determine the best sessions, Smart Goals establishes a threshold by selecting approximately the top 5% of the traffic to your site coming from AdWords. Once that threshold is set, Smart Goals applies it to all your website sessions, including traffic from channels other than AdWords. After enabling Smart Goals in Analytics, they can be imported into AdWords. Instructions for setting up Smart Goals If your view is eligible, you can enable Smart Goals by selecting the Smart Goal goal type when following the regular goal setup flow: Sign in to Google Analytics. Click Admin, and navigate to the desired view. In the VIEW column, click Goals. Click + NEW GOAL. Select Smart Goal (if available). Give your Smart Goal a name and click Save. No additional configuration or customization is required. (That's part of the reason why we call them "Smart Goals.") Instructions for importing Analytics smart goals into AdWords After you've activated Smart Goals in Analytics, sign in to your AdWords account, click the Tools tab, and select Conversions. Click Analytics in the left-hand menu. Check the boxes next to the goals or transactions you want to import. Click Continue. On the next page, you'll see settings that will apply to all of the goals or transactions you selected. Make your choices, then click Import goals. Click Close, or to import more goals, click Import more. AdWords will begin importing the data from your Analytics account. Historical data from before the import won't be included. The Smart Goals report To help you see how Smart Goals perform, use the Conversions > Goals > Smart Goals report. This report shows you how your Smart Goals traffic differs from other traffic. You can also include the Smart Goals Completed dimension in custom reports. The Smart Goals report shows you how Smart Goals would perform even before enabling them in your view (assuming you are eligible to use Smart Goals in the first place). This lets you determine if Smart Goals will be of benefit to you before going through all the steps above. Both the Smart Goals report and the Smart Goals Completed dimension are only available in views which are eligible for Smart Goals.   Interested in getting help with any of these features? Get in touch with our experts and we’d be happy to help!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-04-18

How to use Analytics for mobile apps: Google Analytics SDK vs Firebase

This is the third article in the Q&A series. I will be answering some of the most-asked questions about Google Analytics and how it works. If you’ve missed the previous articles, you can access Part 2 (What is the bounce rate in Google Analytics) and see what questions we answered there. In this article, I will give you an answer to the following questions: How Google Analytics works for mobile apps? What are the differences between Firebase Analytics and Google Analytics? How Google Analytics works for mobile apps? Instead of using JavaScript, for mobile apps, you will be using an SDK. That is a Software Development Kit and it’s what collects the data from your mobile application. As most smartphones are either Android and iOS based, you will have different SDK’s based on the operating system. The SDK works similarly as the JavaScript and collects data like the number of users and sessions, the session duration, the operating system, the device model and the location. All of that is packed in hits and sent to your Google Analytics account. Here is an overview from The Google Analytics Help Center. The main difference is that the data is not sent right away. Because a mobile device might not have a connection to the internet at some points in time, the data is stored on the device and is sent when it is eventually connected. The process is called dispatching and it’s done at different time intervals on Android and on iOS. On Android, the hits are dispatched every 30 minutes and on iOS, every 2 minutes. Those numbers can be customised though. Keep in mind that you can customise the code so that you can track different data in case you feel the base code is not sufficient for you. What are the differences between Firebase Analytics and Google Analytics? Firebase Analytics (FA) is another way to collect the event data. While Google Analytics is a general-purpose (and more web oriented) analytics tool, Firebase was built keeping mobile in mind. There are some things that were added in in the later and also things that are missing from GA. Here are some noteworthy points when considering Firebase Analytics: Real-time view is missing for Firebase Analytics (we mainly use this when testing the app for new events). Events are available after 4 to 6 hours in Firebase Analytics. The Behavior Flow is missing from Firebase Analytics (since there are no screen views logged). The Audiences feature is a big advantage that FA has. If you couple this with the Notifications it will allow you to engage with a specific group of users. If users experience a crash, then an audience group will be created automatically when using the Firebase Crash Reporting feature. Funnel analysis based on custom events is easier in FA. However, if you use Littledata, then this problem can be solved for Google Analytics with the custom reports that we can build. Some events are logged automatically in Firebase Analytics (for example the sessions based on the Activity life-cycle). Firebase has a relatively low methods footprint compared to the methods count that Google Analytics uses - making it less processor and network intensive. As a final point there are benefits for using both platforms to track your Analytics, but if you do focus your business on mobile applications, keep in mind that Firebase Analytics was created for mobile apps. Happy Reporting. Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-04-11

Important update to Remarketing with Google Analytics

If you got this email from Google recently, or seen the blue notification bar at the top of Google Analytics, here's what is changing and how it affects your website. The big problem in modern online marketing is that most users have multiple devices, and the device they interact with the advert on is not the same as the one they convert on: [Google’s] research shows that six in ten internet users start shopping on one device but continue or finish on a different one. Facebook has been helping advertisers track conversion across devices for a few years  - because most Facebook ads are served on their mobile app, when most conversion happens on larger screens. So Google has been forced to play catch-up. Here’s the message from the Google Analytics header: Starting May 15, 2017, all properties using Remarketing with Google Analytics will be enhanced to take advantage of new cross-device functionality. This is an important update to your remarketing settings, which may relate to your privacy policy. The change was announced last September but has only just rolled out. So you can remarket to users on a different device to the one on which they visited your site when: You build a retargeting audience in Google Analytics You have opted in to remarketing tracking in Google Analytics Users are logged into Google on more than one device Users have allowed Google to link their web and app browsing history with their Google account Users have allowed Google account to personalise ads they see across the web This may seem like a hard-to-reach audience, but Google has two secret weapons: Gmail (used by over 1 billion people and 75% of those on mobile) and Chrome (now the default web browser for desktop, and growing in mobile). So there are many cases where Google knows which devices are linked to a user. What is not changing is how Google counts users in Google Analytics. Unless you are tracking registered users, a ‘user’ in Google Analytics will still refer to one device (tablet, mobile or laptop / desktop computer).   Could Google use their account information to make Google Analytics cross-device user tracking better? Yes, they could; but Google has always been careful to keep their own data about users (the actions users take on Google.com) separate from the data individual websites capture in Google Analytics (the actions users take on mywebsite.com). The former is owned by Google, and protected by a privacy agreement that exists between Google and the user, and the latter is owned by the website adding the tracking code but stored and processed by Google Analytics. Blurring those two would create a legal minefield for Google, which is why they stress the word ‘temporary’ in their explanation of cross-device audiences: In order to support this feature, Google Analytics will collect these users’ Google-authenticated identifiers, which are Google’s personal data, and temporarily join them to your Google Analytics data in order to populate your audiences.   How can I make use of the new cross-device retargeting? The first step is to create a remarketing audience from a segment of your website visitors that are already engaged. This could be users who have viewed a product, users who have viewed the pricing page or users who have viewed more than a certain number of pages. For more help on setting up the right goals to power the remarketing audience, please contact us.

2017-04-10

How does page load speed affect bounce rate?

I’ve read many articles stating a link between faster page loading and better user engagement, but with limited evidence. So I looked at hard data from 1,840 websites and found that there’s really no correlation between page load speed and bounce rate in Google Analytics. Read on to find out why. The oft quoted statistic on page load speed is from Amazon, where each 100ms of extra loading delay supposed to cost Amazon $160m. Except that the research is from 2006, when Amazon’s pages were very static, and users had different expectations from pages – plus the conclusions may not apply to different kinds of site. More recently in 2013, Intuit presented results at the Velocity conference of how reducing page load speed from 15 seconds to 2 seconds had increased customer conversion by: +3% conversions for every second reduced from 15 seconds to 7 seconds +2% conversions for every second reduced from seconds 7 to 5 +1% conversions for every second reduced from seconds 4 to 2 So reducing load speed from 15 seconds to 7 seconds was worth an extra 24% conversion, but only another 8% to bring 7 seconds down to 2 seconds. Does page speed affect bounce rate? We collected data from 1,840 Google Analytics web properties, where both the full page load time (the delay between the first request and all the items on the page are loaded) and the bounce rate were within normal range. We then applied a Spearman’s Rank Correlation test, to see if being a higher ranked site for speed (lower page load time) you were likely to be a higher ranked site for bounce rate (lower bounce rate). What we found is almost no correlation (0.18) between page load speed and bounce rate. This same result was found if we looked at the correlation (0.22) between bounce rate and the delay before page content starts appearing (time to DOM ready) So what explains the lack of a link? I have three theories 1. Users care more about content than speed Many of the smaller websites we sampled for this research operate in niche industries or locations, where they may be the only source of information on a given topic. As a user, if I already know the target site is my best source for a topic, then I’ll be very patient while the content loads. One situation where users are not patient is when arriving from Google Search, and they know they can go and find a similar source of information in two clicks (one back to Google, and then out to another site). So we see a very high correlation between bounce rate and the volume of traffic from Google Search. This also means that what should concern you is speed relative to your search competitors, so you could be benchmarking your site speed against a group of similar websites, to measure whether you are above or below average.   2. Bounce rate is most affected by first impressions of the page As a user landing on your site I am going to make some critical decisions within the first 3 seconds: would I trust this site, is this the product or content I was expecting, and is it going to be easy to find what I need. If your page can address these questions quickly – by good design and fast loading of the title, main image etc – then you buy some more time before my attention wanders to the other content. In 2009, Google tried an experiment to show 30 search results to users instead of 10, but found the users clicking on the results dropped by 20%. They attributed this to the half a second extra it took to load the pages. But the precise issue was likely that it took half a second to load the first search result. Since users of Google mainly click on the first 3 results, the important metric is how long it took to load those - not the full page load.   3. Full page load speed is increasingly hard to measure Many websites already use lazy loading of images and other non-blocking loading techniques to make sure the bare bones of a page is fast to load, especially on a mobile device, before the chunkier content (like images and videos) are loaded. This means the time when a page is ready for the user to interact with is not a hard line. SpeedCurve, a tool focussed entirely on web page speed performance, has a more accurate way of tracking when the page is ‘visually complete’ based on actual filmstrips on the page loading. But in their demo of The Guardian page speed, the page is not visually complete until a video advert has rendered in the bottom right of the screen – and personally I’d be happy to use the page before then. What you can do with Google Analytics is send custom timing events, maybe after the key product image on a page has loaded, so you can measure speed as relevant to your own site.   But doesn’t speed still affect my Google rankings? A little bit yes, but when Google incorporated speed as a ranking signal in 2010, their head of SEO explained it was likely to penalise only 1% of websites which were really slow. And my guess is in 7 years Google has increase the sophistication with which it measures ‘speed’.   So overall you shouldn’t worry about page load times on their own. A big increase may still signal a problem, but you should be focussing on conversion rates or page engagement as a safer metric. If you do want to measure speed, try to define a custom speed measurement for the content of your site – and Littledata’s experts can work with you to set that custom reporting up.

2017-04-07

What is the bounce rate in Google Analytics

The bounce rate is number of web sessions where the user left your site after viewing just one page. It is a key measure for landing page engagement. This is the second article in the Q&A series. As I previous mentioned, I am going to continue answering some of the most-asked questions about Google Analytics and how it works. If you want to get an idea of how this works, you can visit PART 1(Pros and cons of using Google Analytics) of the series and see what questions we answered there. Here are the questions we will be tackling in this second article of the series: 1) What is the bounce rate in Google Analytics? 2) How is the Bounce rate calculated? 3) What is an ideal bounce rate? 1) What is the bounce rate in Google Analytics? The definition of the Bounce Rate as shown in the Google Analytics Help Centre is “the percentage of single-page sessions. Those are sessions in which the person left your site from the entrance page without interacting with any other page”. Why is this metric important? A high bounce rate shows you may have some problems on your website. Remember that the bounce rate is correlated to the content of your website and should be considered in the context of the purpose of the website. If you have a content website, a services website or an ecommerce website you need to look at the bounce rate in the big picture and analyse it using Advanced Segments to look at a specific category of pages, and see how they’re performing vs other sections. Some reasons for a high bounce rate are: Single page website: where the user never leaves the first page through their whole visit. A high bounce rate, in this case, is actually irrelevant: you should focus on how many visitors. In order to find out how people interact with your website, you can track Custom Events on the page. To get an accurate bounce rate in this case you need to set up the events as "interaction hits". Incorrect implementation: for a multiple page website, in order to track all the pages, you need to add a specific tracking code on all of the pages for a correct read of the data. In case the bounce rate is high, that might show that the tracking code is not correctly applied to all pages of the website. User Behaviour: the people that arrived on your site and left without doing anything else, either because they found the information that they wanted on that page and there was no need to access other pages or they simply entered by accident and didn’t find what they needed. Also if a user has a page bookmarked, enters the page and then leaves, that’s also counted as a bounce. Site design: when the implementation is done properly then you really might have a problem with the way the content is displayed. In this case consider looking at the landing pages, as they might not do justice to the content. Also, the keywords or ads that you use, might not reflect the content of your website and because of that, you need to optimise either the content or the keywords and ads. 2) How is the Bounce rate calculated? In Google Analytics, there are two indicators for the Bounce Rate. There is the Bounce Rate of a Web Page and then there is the Bounce Rate of a Website. The Bounce Rate of a Website is the total number of bounces across all of the pages on the website over the total number of entrances across all the pages on the website (both over the same determined period of time). This is represented in Google Analytics as a percentage shown in the table of all the pages displayed. The Bounce Rate of a Web Page is the total number of bounces on a page over the total number of entrances on the page (Both over the same determined period of time). The image above shows the equation for calculating it. This is also represented as a percentage but it is shown in the table for each page separately. Here is an article from OptimizeSMART in which they show us how to improve our bounce rate. 3) What is an ideal bounce rate? As I previously explained, the bounce rate should be as low as possible. In one of his articles, Avinash Kaushik who is a guru of Analytics tells us what the ideal bounce rate should be: “As a benchmark from my own personal experience over the years, it is hard to get a bounce rate under 20%. Anything over 35% is a cause for concern and anything above 50% is worrying.” To recap, in this article we managed to see what the Bounce Rate is, how it’s calculated and what is the ideal bounce rate we should strive for with our website. Make sure to check part 3 out, in which we will answer more questions about Google Analytics. Happy Reporting. Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-04-04

Pros and cons of using Google Analytics

We've decided to start a series of content to answer basic questions you may have regarding Google Analytics. These are readily available but sometimes over complicated and we're experts at both analytics and making things simple. Hopefully, these blog posts will help clarify any questions you may have. In Part 1, we will be discussing: When to use Analytics? Pros and cons of using Google Analytics. 1. When to use Analytics? These days, everything on the internet is connected. Have you ever questioned how websites know your location and redirect you to the page of that specific location? Or have you ever seen those ads that appear all the time after you visit a specific website? That’s due to cookies, which are a set of parameters that get collected and interpreted. They are part of Digital Analytics or Google Analytics, which is a set of measurements that helps you in understanding which people you reach with your website and how your website performs. By performance, we refer to who is visiting your website, how someone interacts with your website, the decisions they take following those interactions and much more. Ultimately, you want to use Google Analytics whether you own a website, you're an online shop or if you are a marketer, in order to increase the marketing and sales efforts of the platforms you are using. If you are still not convinced that Analytics is a must have for an online property, check this article from AnalyticsNinja and you'll get an even deeper dive into why you should you use Analytics. 2. Pros and cons of using Google Analytics: Google Analytics is one of the most known and used tools to track Digital Analytics. There are definitely a lot of pros to using it but there are also some drawbacks. Pros: It’s free of charge so everyone can use it. You can use it on different digital environments such as websites, mobile applications, kiosks, or anything that has an internet connection There's a Google Analytics Academy, where you can get in-depth information and get educated about how to use it. You can connect your Google Analytics account with your AdWords account. You can also collect data from different platforms and sources. You can create custom goals and you can also track your ecommerce platform. You can create custom reports based on your needs. This way you can track specific information depending on your industry. The cons: In order to understand all the intricacies, you need to learn. The issue with that is that the information is sometimes hard to find, may be confusing, and overwhelming. The academy is also quite time-consuming so if you're on a time frame, it my not be feasible. The overall feel of the platform might also be a little bit overwhelming. There are too many dashboards and too many things to look at. The free version of Google Analytics suit almost anyone, but if your traffic is high and you'd like to upgrade to Premium, the price is $150,000. Another great article on this matter is written by the guys at Eethuu. Hopefully, this has helped give you more insights into Google Analytics! If you'd like more information or have any questions, get in touch. Check out Part 2(What is the bounce rate in Google Analytics) of the series!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-03-22

How Google Analytics works

Google Analytics is a free Web analytics service that provides statistics and basic analytical tools for search engine optimisation (SEO) and marketing purposes. The service is available to anyone with a Google account. As a person that’s at the beginning and trying to get familiar with the field of analytics and data, it’s definitely important to understand how Google Analytics works. There are four components that come together and make Google Analytics work: 1. Collection 2. Processing 3. Configuration 4. Reporting Collection: Data can be collected from different sources, such as a website, a mobile application or pretty much any device that has a connection to the internet. For a website, in order to collect the information, we need to include a Tracking code (JavaScript). This code should be included on every single page of the website in order for Google Analytics to capture the information properly. The JavaScript that we get from Google is okay, but don’t forget that it tracks a limited amount of information. If you are active in a niche field of work, you might want to take a look at adapting that code in order to track the correct data. For a mobile application, we need to use a specific software development kit (SDK), depending on the operating system. In this case, activities will be tracked instead of pageviews. Because we might not always have an internet connection available, the hits will be stored and sent to afterwards to Google’s collection centres. Processing + Configuration: The processing step is the one that takes the longest to finish. It can take anywhere up to 4 hours (24 hours in Google's T&Cs) to turn all the raw data into reports that you are able to interpret and monitor. This doesn’t happen easily, but the only way you can skip the queue is by paying for Google Analytics 360. In Google Analytics, the configuration part comes in and it applies certain filters to the data that is collected. While some of those filters (new or returning users, linking between pages and time spent on certain pages) are pre-configured, you also have the possibility to apply some filters of your own to this process. Remember that you will not be able to change that information once it is stored in the database. Reporting: The final step what the users get to see. By using Google Analytics' own interface, you have access to all the processed information and this is the place where you can manage it from. There is also the possibility of using different applications by creating a custom code in the reporting API. Here is a short list of benefits that you will gain after using Google Analytics: 1. Visitor Segmentation: New vs Returning users, Geographical location and referral source. 2. Page visits: Finding out which pages are the most visited. 3. Locating the website: Finding out how the users got to your website and tracking the keywords they used. 4. Website optimization If you'd like some more information, please get in touch or leave a comment below! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-03-15

How to improve your landing pages with clear CTAs

In the previous blog post, how to improve your landing pages using Google Analytics, we started analysis what makes a good landing page. Some of the ideas were related to call to actions. Your landing page must have a call to action (CTA) correlated with the marketing campaign and the full content of the page. Clear and unambiguous CTA(s) If you are offering app access, go with "Get Started" or "Create account" and don't say “Get your free ebook” or “go” or “submit”. Say short and clear what you want them to do. Don't mislead the users and don't use fancy words. When you're choosing the CTA for your landing page you should consider these three: what you say how your customer will interact with it where to place it What to say is the wording. If you want the customer to subscribe to the newsletter say "subscribe to the newsletter", if you want them to buy say "buy", if you want them to call say "call". Keep it short and clear. If the customer needs to subscribe you need to provide them with the field were to add their email address; If you want them to call you then you should use a dial function for mobile users or show the number for the desktop users; If you want them to buy then the press of the button should redirect them to a page where they can choose the option for delivery and payment. Where to place the call to action in your landing page is simple - where the customers will see it first. I presume you already have event tracking, in place (if no, find out how to set up in this blog post: Set up event tracking in GTM ). Based on some numbers from Google Analytics, let's see how good and bad engagement looks like for a landing page. Find out the level of engagement with the page Bounce rate: This will show you the number of people that entered this page and left without taking any other action (like seeing the second page or clicking on the call to action). The bounce rate will tell you how your whole landing page is engaging with the audience. In the example above, the landing page, /find-more has a bounce rate of 98,8%. This is very bad! On the other side, we have the landing page apps.shopify.littledata with 0% bounce rate. This is the holy grail of landing pages. These means that from an engagement point of view your landing page is perfect. As a rule: You should aim for at least the same bounce rate as you have on the entire website as a medium. Find out if your call to action performed Method 1 - Deducting from landing page report Go into Google Analytics -> in the search bar search landing page -> Choose Site content - Landing pages. Click on your landing page name and now add a second dimension: Second page. Find the link where your call to action redirects and analyse all elements in this report. If you don't have events in place, you will still be able to see how your traffic is clicking through the links on your landing page. If your landing page has more than 1 action then you can add a second dimension on the landing page report and see what was the second page they visited. In the example above, the call-to-action redirected them to the apps.shopify.com/littledata. From the numbers of sessions, we can see that only 10% of the users clicked the call-to-action button. 89% of the people wanted to find more about the product before purchasing. This is the example of bad engagement. The fact that 89% of the people wanted to find more means that we need to provide more details on the landing page and maybe have a clearer call-to-action. Method 2 - Deducting from Top Events report For this, go to Google Analytics and search for Top Events and add a second dimension to the report "Page". You can also build a custom report so you see the number of people that saw the page and the number of people that took the call-to-action. Have any questions? Comment below or get in touch!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-03-14

How to improve your landing pages using Google Analytics

Landing page optimisation is one part of a broader digital marketing process called conversion optimisation, or conversion rate optimisation (CRO), with the goal of improving the percentage of visitors to a website that becomes sales leads/or customers. Let's see how to improve your landing page performance. There are some things to check when you want to improve the conversion rate of a particular page. In order to get the best data, we use Google Analytics and Hotjar. I will start with Hotjar because it is faster! With Hotjar you will understand what users want, care about and interact with on your site by visually representing their clicks, taps and scrolling behaviour. This is shown with nice videos of a user's journey leading to conversion. With Hotjar, you can see what confuses people, what is not clear and if for your customer point-of-view is clear on your landing page. And now the hard and exciting part: Analyse the data collected in Google Analytics. If you think that the home page is a landing page please read this before you go further: Website Homepage vs Landing page - what's the difference? and this: Don’t obsess over your homepage – its importance will decrease over time! When a visitor clicks on a Pay-Per-Click (PPC) ad, they're taken to a landing page — a web page whose sole purpose of existence is to entice people to take an action. If done well, it could be the most effective marketing weapon in your arsenal. The correct analysis of data can save you a lot of money or even your business. If your visitors donʼt know what to do when they land on your landing page, then you are throwing your advertising money out the window. Your call-to-action (CTA) is the primary conversion goal of a visitor to your landing page. Next, I give you some examples of common actions that you might want a customer to do on your landing page: purchasing a product subscribing to a newsletter calling you on the phone downloading an ebook or whitepaper watching a demo requesting information Let's find out, step-by-step if your landing page is a winner using this checklist. Click on them to find out how to analyse and interpret data CTA(s) clear and unambiguous Do what you say and say what you do Don't be like Trump. Leave the Amazing! Awesome! words elsewhere Less is more Keep it where it can be seen Know your clients Twice is better Design matters Choose what matters the most CTA(s) clear and unambiguous Google Analytics report: "Landing pages" with a second dimension added to the report: "Second page" If you are offering an app access go with "Get Started" or "Create account" and don't say “Get your free ebook” or “go” or “submit”. Do what you say and say what you do Google Analytics report: "Landing pages" with a second dimension added to the report: "Second page" analyses the bounce rate on the call-to-action link. Donʼt promise one thing and then deliver something else or even worse nothing at all (a 404 page). To follow the same example, if you have an app and say "30 days free trial" don't let people click 'try for 30 days' and on the next page provide a PayPal form to charge them for a month period. Don't be like Trump. Leave the Amazing! Awesome! words elsewhere Google Analytics report: "Pages" see how many FAQ and Terms pageview you have. Resist the temptation to include bloated adjectives. Such claims are likely to make people think you are overselling and trying too hard. Less is more Google Analytics report: "Top Events" with a second dimension added to the report: "Page" analyses the clicks on your call-to-action versus other clicks in page or scroll actions. Make space for your call-to-action. Let them breathe visually. Using more whitespace will allow your button or statement to stand out on the page. Colour choice is important here also; create a high contrast between the call-to-action and surrounding elements to assert it’s dominance. Keep it where it can be seen Google Analytics report: "Top Events" analyse the scroll tracking. See how far your visitors are scrolling down If you have a long page, donʼt put the call-to-action below the fold. Take into consideration, the different screen sizes and adapt your landing pages for the most common. Most of the users will not scroll far down the page so be sure to put your value proposition and your call-to-action as a first-seen element in the page. Know your clients Google Analytics report: "Demographics - Language" Speak your client's language. Provide different landing pages based on country. Advertise differently based on specific demographics. However good your product or service is, the simple truth is that no one will buy it if they don't want it or believe they don't need it. And you won't persuade anyone that they want or need to buy what you're offering unless you clearly understand what it is your customers really want. Twice is better Google Analytics report: Combine "Top Events" (for scroll tracking) and "All Pages" for the propotion of sessions with FAQ/Terms pageviews Not all customers are ready to engage right away and might need some supporting information to ease their worries or answer their questions. If you are asking someone to buy something, a sensible secondary call-to-action can be to download a product brochure. This keeps them in your realm of influence (as opposed to leaving to do research elsewhere) and builds confidence. Ensure that the safety net CTA doesnʼt compete in size and visual dominance – often a simple text link is adequate, beneath the main big action button. If you are asking someone to purchase online, offering a phone number for phone orders can make a potential customer more likely to convert if thatʼs their preferred contact method. Design matters Google Analytics report: "Source/medium" shows the bounce rate for each campaign Carry your primary call-to-action throughout the entire acquisition and conversion experience, from audience acquisition ads (PPC, email, banner, social media link) through your landing page and on to the final destination page. Choose what represents you the most (maybe some colours or even the call-to-action itself), you should be able to look at the page and have your eye immediately drawn to the action area. Be audience appropriate Google Analytics report: there is no report in Analytics for this. Just remember your experience when reading an email or a Facebook comment Previously, I said to speak the customers' language. Now I'm saying to take care what they can interpret. Reading a statement is different from hearing it. So don't be too pushy, don't use a lot of exclamation signs, don't use a lot of caps lock wording and be a friend when they say what they feel when they see the call-to-action. I recommend reading this blog post from January: How to improve your conversion rate optimisation and this one: Conversion friendly experiences: reducing landing page friction with psychology. These two are related and complementary to the actions you're trying to take. In the next couple of weeks I will go deeper in each section and show you how good and bad engagement looks like for a landing page. Have any questions? Get in touch with our experts!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-02-17

Shine a light on ‘dark’ Facebook traffic

If Facebook is a major channel for your marketing, whether sponsored posts or normal, then you’re underestimating the visits and sales it brings. The problem is that Facebook doesn’t play nicely with Google Analytics, so some of the traffic from Facebook mobile app comes as a DIRECT visit. That’s right – if a Facebook user clicks on your post on their native mobile app they won’t always appear as a Facebook social referral. This traffic is ‘dark Facebook’ traffic: it is from Facebook, but you just can’t see it. Since around 40% of Facebook activity is on a mobile app, that means the Facebook traffic you see could be up to 40% less than the total. Facebook hasn’t shown much interest in fixing the issue (Twitter fixed it, so it is possible), so you need to fix this in your own Google Analytics account. Here are three approaches: 1. Basic: use campaign tagging The simplest way to fix this, for your own posts or sponsored links on Facebook, is to attach UTM campaign tags to every link. Google provides a simple URL builder to help. The essential tags to add are “utm_source=facebook.com” and “utm_medium=referral”. This will override the ‘direct’ channel and put all clicks on that links into the Facebook referral bucket. Beyond that, you can add useful tags like “utm_campaign=events_page” so you can see how many click through from your Facebook events specifically. 2. Moderate: use a custom segment to see traffic What if much of your traffic is from enthusiastic brand advocates, sharing your pages or articles with their friends? You can’t expect them to all use an URL builder. But you can make a simple assumption that most users on a mobile device are not going to type in a long URL into their browser address bar. So if the user comes from a mobile device, and isn’t visiting your homepage (or a short URL you deliberately post), then they are probably coming from a mobile app. If your website is consumer facing, then the high probability is that that mobile app is Facebook. So we can create a custom segment in GA for traffic which (a) comes from a mobile device (b) does not have a referrer or campaign (i.e. direct) (c) does not land on the homepage To start you need to create a segment where source contains 'facebook'. Then add the 'Direct mobile, not to homepage' segment: Next, you can create a custom report to show sessions by hour: You should see a strong correlation, which on the two web properties I tested on resulted in doubling the traffic I had attributed to Facebook. 3. Advanced: attribute micro spikes to Facebook Caveat: you’ll need a large volume of traffic – in excess of 100 visits from Facebook a day – to try this at home The final trick has been proved to work at The Guardian newspaper for Facebook traffic to news articles. Most Facebook activity is very transitory – active users click on a trending newsfeed item, but it quickly fades in interest. So what you could do, using the Google Analytics API, is look for the ‘micro spikes’ in referrals that come from Facebook on a minute-by-minute basis, and then look at the direct mobile visits which came at the same time, and add these direct spikes to the total Facebook traffic. I've played around with this and it's difficult to get right, due to the sampling Google applies, but I did manage to spot spikes over around 5 minutes that had a strong correlation with the underlying direct mobile traffic. Could these approaches work for your site?  I'm interested to hear. (Chart: Dark Social Dominates Online Sharing | Statista)   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-02-09

6 reasons Facebook ads don’t match the data you see in Google Analytics

If you run Facebook Ads and want to see how they perform in Google Analytics, you may have noticed some big discrepancies between the data available in Facebook Ad Manager and GA. Both systems use different ways to track clicks and visitors, so let’s unpick where the differences are. There are two kinds of metrics you’ll be interested in: ‘website clicks’ = the number of Facebook users who clicked on an advert on your own site, and (if you do ecommerce) the transaction value which was attributed to that advert. Website Clicks vs Sessions from Facebook 1. GA isn’t picking up Facebook as the referrer If users click on a link in Facebook’s mobile app and your website opens in an in-app browser, the browser may not log that ‘facebook.com’ was the referrer. You can override this (and any other link) by setting the medium, source, campaign and content attributes in the link directly. e.g. www.mysite.com?utm_medium=social&utm_source=facebook.com&utm_campaign=ad Pro Tip: you can use GA’s URL builder to set the UTM tags on every Facebook campaign link for GA. In GA, under the Admin tag and then ‘Property settings’ you should also tick the box saying ‘Allow manual tagging (UTM values) to override auto-tagging (GCLID values)’ to make this work more reliably. 2. The user leaves the page before the GA tag fires There’s a time delay between a user clicking on the advert in Facebook and being directed to your site. On a mobile, this delay may be several seconds long, and during the delay, the user will think about going back to safety (Facebook’s app) or just closing the app entirely. This will happen more often if the visitor is not familiar with your brand, and also when the page contents are slow to load. By Facebook’s estimation the GA tracking won’t fire anywhere between 10% and 80% of clicks on a mobile, but fewer than 5% of clicks on a desktop. It depends on what stage in the page load the GA pixel is requested. If you use a tag manager, you can control this firing order – so try firing the tag as a top priority and when the tag container is first loaded. Pro Tip: you can also use Google's mobile site speed suggestions to improve mobile load speed, and reduce this post-click drop-off. 3. A Javascript bug is preventing GA receiving data from in-app browsers It’s possible your page has a specific problem that prevents the GA tag firing only for mobile Safari (or Android equivalent). You’ll need to get your developers to test out the landing pages specifically from Facebook’s app. Luckily Facebook Ad Manager has a good way to preview the adverts on your mobile. Facebook Revenue vs GA Ecommerce revenue 4. Attribution: post-click vs last non-direct click Currently, Facebook has two types of attribution: post-view and post-click. This means any sale the user makes after viewing the advert or clicking on the advert, within the attribution window (typically 28 days after clicking and 1 day after viewing), is attributed to that advert. GA, by contrast, can use a variety of attribution models, the default being last non-direct click. This means that if the user clicks on an advert and on the same device buys something within the attribution window (typically 30 days), it will be attributed to Facebook.  GA doesn't know about views of the advert. If another campaign brings the same user to your site between the Facebook ad engagement and the purchase, this other campaign takes the credit as the ‘last non-direct click’. So to match as closely as possible we recommend setting the attribution window to be '28 days after clicking the ad' and no 'after view' attribution in Facebook (see screenshot above) and then creating a custom attribution model in GA, with the lookback window at 28 days, and the attribution 'linear' The differences typically come when: a user engages with more than one Facebook campaign (e.g. a brand campaign and a re-targeting one) where the revenue will only be counted against the last campaign (with a priority for ads clicked vs viewed) a user clicks on a Facebook ad, but then clicks on another advert (maybe Adwords) before buying. Facebook doesn’t know about this 2nd advert, so will attribute all the revenue to the Facebook ad. GA knows better, and will attribute all (or part) of it to Adwords. 5. Facebook cross-device tracking The main advantage Facebook has over GA is that users log in to its platform across all of their devices, so it can stitch together the view of a mobile advert on day 1 with a purchase made from the user’s desktop computer on day 2. Here’s a fuller explanation. By contrast, unless that user logs into your website on both devices, and you have cross-device tracking setup, GA won’t attribute the sale to Facebook. 6. Date of click vs date of purchase In Facebook, revenue is attributed to the date the user saw the advert; in GA it is to the date of purchase. So if a user clicks on the advert on 1st September, and then buys on the 3rd September, this will appear on the 1st on Facebook – and on the 3rd in GA. 7. The sampling problem Finally, did you check if the GA report is sampled? In the top right of the screen, in the grey bar, you'll see that the report is based on a sample.  If that sample is less than 100% it means the numbers you see are estimates.  The smaller the sample size used, the larger the possibility of error.  So in this example, a 45% sample of 270,000 sessions could skew our results plus or minus 0.2% in the best case. As a rule of thumb, Google applies sampling when looking over more than 500,000 sessions (even if you select the 'greater precision' option from the drop-down menu). You can check your own sample using this confidence interval calculator. Conclusion Altogether, there’s a formidable list of reasons why the data will never be an exact match, but I hope it gives you a way to optimise the tracking. Please let us know if you’ve seen other tracking issues aside from these.   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-02-08

Cross Domain tracking for Eventbrite using Google Tag Manager (GTM)

Are you using Eventbrite for event registrations? And would you like to see the marketing campaign which drove that event registration correctly attributed in Google Analytics? Then you've come to right place! Here is a simple guide to adding a Google Tag Manager tag to ensure the correct data is sent to Eventbrite to enable cross-domain tracking with your own website. Many thanks to the Lunametrics blog for their detailed solution, which we have adapted here for GTM. Before this will work you need to have: links from your site to Eventbrite (including mysite.eventbrite.com or www.eventbrite.co.uk) the Universal Analytics tracking code on both your site and your Eventbrite pages. only have one GA tracking code on your own site - or else see the Lunametrics article to cope with this 1. Create a new tag in GTM Create a new custom HTML tag in GTM and paste this script: [code language="javascript"] <script> (function(document, window) { //Uses the first GA tracker registered, which is fine for 99.9% of users. //won't work for browsers older than IE8 if (!document.querySelector) return; var gaName = window.GoogleAnalyticsObject || "ga" ; // Safely instantiate our GA queue. window[gaName]=window[gaName]||function(){(window[gaName].q=window[gaName].q||[]).push(arguments)};window[gaName].l=+new Date; window[gaName](function() { // Defer to the back of the queue if no tracker is ready if (!ga.getAll().length) { window[gaName](bindUrls); } else bindUrls(); }); function bindUrls() { var urls = document.querySelectorAll("a"); var eventbrite = /eventbrite\./ var url, i; for (i = 0; i < urls.length; i++) { url = urls[i]; if (eventbrite.test(url.hostname) === true) { //only fetches clientID if this page has Eventbrite links var clientId = getClientId(); var parameter = "_eboga=" + clientId; // If we're in debug mode and can't find a client if (!clientId) { window.console && window.console.error("GTM Eventbrite Cross Domain: Unable to detect Client ID. Verify you are using Universal Analytics."); break; return; } url.search = url.search ? url.search + "&" + parameter : "?" + parameter; } } } function getClientId() { var trackers = window[gaName].getAll(); return trackers[0].get("clientId"); } })(document, window); </script> [/code]   2. Set the tag to fire 'DOM ready' Create a new trigger (if you don't have a suitable one) to fire the tag on every page at the DOM ready stage.  We need to make sure the Google Analytics tracker has loaded first. 3. Test the marketing attribution With the script working you should see pageviews of the Eventbrite pages as a continuation of the same session. You can test this by: Opening the 'real time' reporting tag in Google Analytics, on an unfiltered view Searching for your own site in Google Navigating to the page with the Eventbrite link and clicking on it Looking under the Traffic Sources report and checking you are still listed as organic search after viewing the Eventbrite page Need more help? Comment below or get in touch!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-02-07

Don’t obsess over your homepage – its importance will decrease over time

Many businesses spend a disproportionate amount of time tweaking copy, design and interactive content for their homepage. Yet they miss the fact that the action is increasingly elsewhere. Homepage traffic has traditionally been seen as a proxy for ‘brand’ searches – especially when the actual search terms driving traffic are ‘not provided’. Now, brand search traffic may be finding other landing pages directly. Our hypothesis was that over the last 2 years the number of visits which start at the homepage, on the average website, are decreasing. To prove this, we looked at two categories of websites in Littledata’s website benchmarks: Websites with more than 20,000 monthly visits and more than 60% organic traffic (227 websites) Large websites with more than 500,000 monthly visits (165 websites) In both categories, we found that the proportion of visits which landed on the homepage was decreasing: by 8% annually for the smaller sites (from 16% of total visits to 13% over two years), and 7% annually for the larger sites (from 13% to 11%). If we ignore the slight rise in homepage traffic over the November/December period (presumably caused by more brand searches in the Christmas buying season), the annual decline is more than 10%. From the larger websites, only 20% showed any proportionate increase in homepage traffic over the 2 years – and those were mainly websites that were growing rapidly, and with an increasing brand. I think there are three different effects going on here: Increased sophistication of Google search usage is leading to more long-tail keywords, where users want a very specific answer to a question – usually not given on your homepage. The increase in mobile browsing, combined with the frustrations of mobile navigation, is leading more users to use search over navigation – and bypass your homepage That Google’s search-engine result page (SERP) changes have made it less likely that brand searches (searching for your company or product names) will navigate to your landing page – and instead browse social profiles, news, videos or even local listings for your company. In conclusion, it seems that for many businesses the homepage is an increasing irrelevance to the online marketing effort. Spend some time on your other content-rich, keyword-laden landing pages instead! And would you like to see if you are overly reliant on your homepage traffic, compared with similar websites? Try Littledata’s reporting suite.   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-01-26

Enhanced ecommerce tracking for travel booking sites

Every online business presence has a goal. These goals (bookings, donations, subscribers, events, or purchases) are the reason for our efforts. But how many of us really track how our goals really perform? In this article, you will find out how to take these business goals and track them on Google Analytics with an ecommerce approach. This article is not about how to set up goals in Google Analytics, but if you are interested in finding out more about the setup or what there are, then read: Setting up a destination goal funnel in Google Analytics. The advantage of using an ecommerce approach for non-ecommerce websites is that after the setup is done, you have a basis to develop correct marketing strategies. You will know what channels brings you money, you will know what channels interact with each other and you can adjust your budget to maximise the ROI. If you're in the business of selling tickets (planes, concerts, conferences), book medical exams or collect donations, this article concerns you! I will show you a step-by-step guide on where to implement the Enhanced Ecommerce features and I will provide links for each to find out how to implement them. Let's say you are Wizz Air. You sell flight tickets and book cars and so on. Promotion impressions and promotion clicks Each time Wizz Air displays a banner with some kind of marketing communication that banner can be tracked as a "promotion" in Google Analytics. In Google Analytics, you can see the performance of each banner and make decisions to replace them, change the order or even make them bigger based on the tracking you implement. The technicalities: implementing via Google Tag Manager or implementing via Google Analytics. After you implement the tracking and create the tags (for GTM) you will be able to see the data in Google Analytics under Ecommerce > Marketing > Internal Promotions Based on the position, click-thru-rate, and revenue gained for each, Wizz Air can then rearrange banners, eliminate some of them or boost their visibility. Ecommerce activities (catalogue views, service page views, click on call to actions) Wizz Air provides multiple sections on the website where you can search for flights. These sections can be mapped as product lists. For WizzAir, the product lists are in the homepage section, timetable section, and maps section. Typically, Google Analytics and Google Tag Manager requests the fields below when sending a product list view (product impressions). I will provide you with a schema that will capture the flight booking particularities but you can use your own business specific examples. When you click on a red point on the map, the customer can see the flights from a particular city. We will send all the flight information from that city as product impressions. 'id': 'LTN - PRG',                          // The departure airport code - The arrival airport code 'name': 'London Luton - Prague',             // City name of departure - City name for arrival 'category': 'Flight',                        // WizzAir offers flight booking along with car booking, and hotel booking 'brand': 'WizzAir',                          // If this would be a tourism agency instead of WizzAir will be other company. 'variant': '010117',                      // If the page has the option to add the date we will add the date as a MMDDYY When the search button is present, you send the action "click". ga('ec:setAction', 'click', {                                    // click action. 'list': 'Maps'                                                          // Product list (string). }); After searching, the client can see the selection page from the product list. For Wizz Air customers, they can search the best price and see the package options. In the case of Wizz Air, these pages can be considered the product pages. The usual structure that needs to be sent to Google Analytics and Google Tag Manager is: 'id': 'LTN - PRG',                                    // The departure airport code - The arrival airport code 'name': 'London Luton - Prague',          // City name of departure - City name for arrival 'category': 'Flight',                                 // WizzAir offers flight booking along with car booking, and hotel booking 'brand': 'WizzAir',                               // If this would be a tourism agency instead of WizzAir will be other company. 'variant': '010117',                             // If the page has the option to add the date we will add the date as a MMDDYY Each time the client changes the day a new detail view should be sent. Clicking on the price box will trigger an Add to cart action. The usual content of an Add To cart activity is: 'name': 'London Luton - Prague',    // The departure airport code - The arrival airport code 'id': 'LTN - PRG',                               // City name of departure - City name for arrival 'price': '61.99',                                  // Selected price for the flight 'brand': 'WizzAir',                          // If this would be a tourism agency instead of WizzAir will be other company. 'category': 'Flight',                        // WizzAir offers flight booking along with car booking, and hotel booking 'variant': '010117',                         //If the page has the option to add the date we will add the date as a MMDDYY 'quantity': 1'                                   // Person number 'dimenstion1': 'LTN13432',           // Flight number 'dimenstion2': 'WizzGO'              // Package option (Basic, Wizz Go, Wizz Plus) Check out steps and booking In the case of Wizz Air, each "continue" button will send a checkout step to Google Analytics. Sending the checkout steps will provide insights about where the customers drop off and what process steps can be improved. Wizz Air has a 4-steps checkout (choose flight, choose passengers, services, and payment). The final thing to send is the transaction (the booking). The structure and implementation details for Google Analytics and Google Tag Manager are in the links and the fields, in this case, will be: 'ecommerce': { 'purchase': { 'actionField': { 'id': 'T12345',                                           // Transaction ID. Required for purchases and refunds. 'affiliation': 'booking.com'                    // Affiliation agent, 'revenue': '35.43',                                 // Total booking value (incl. tax, airport fees etc) 'tax':'4.90', 'shipping': '5.99',                                 //can use this field to capture airport fees or thir party operators fees 'coupon': 'SUMMER_SALE'              //if a discount cupon was used }, 'products': [{                                      //if the flight has a return flight then two products will be sent 'name': 'London Luton - Prague',     // The departure airport code - The arrival airport code 'id': 'LTN - PRG',                                // City name of departure - City name for arrival 'price': '61.99',                                  // Selected price for the flight 'brand': 'WizzAir',                           // If this would be a tourism agency instead of WizzAir will be other company. 'category': 'Flight',                         // WizzAir offers flight booking along with car booking, and hotel booking 'variant': '010117',                          //If the page has the option to add the date we will add the date as a MMDDYY 'quantity': 1'                                   // Person number 'dimenstion1': 'LTN13432',           // Fligh number 'dimenstion2': 'WizzGO'               // Package option (Basic, Wizz Go, Wizz Plus) 'coupon': 'SUMMER_SALE'         // Optional fields may be omitted or set to empty string. }, { 'name': 'Prague -London Luton',    // The departure airport code - The arrival airport code 'id': 'PRG -LTN',                               // City name of departure - City name for arrival 'price': '61.99',                                 // Selected price for the flight 'brand': 'WizzAir',                           // If this would be a tourism agency instead of WizzAir will be other company. 'category': 'Flight',                         // WizzAir offers flight booking along with car booking, and hotel booking 'variant': '150117',                        //If the page has the option to add the date we will add the date as a MMDDYY 'quantity': 1'                                   // Person number 'dimenstion1': 'LTN2143432',        // Flight number 'dimenstion2': 'WizzGO'             // Package option (Basic, Wizz Go, Wizz Plus) 'coupon': 'SUMMER_SALE'        // Optional fields may be omitted or set to empty string. }] } } Sending all these steps to Google Analytics about the customer activity, on any kind of website, will provide you with information about return on marketing spends, improve page layout performance, improve conversion rate, find out insights about customer needs and a lot more. Having the full enhanced ecommerce setup is very powerful and can bring many advantages. You can test the full setup on the Google Analytics demo account. Have any questions or need some help? Please get in touch or comment below!  

2017-01-24

Tips on how to improve your conversion rate optimisation (CRO)

In internet marketing, conversion optimisation, or conversion rate optimisation (CRO) is a system for increasing the percentage of visitors to a website that converts into customers, or more generally, takes any desired action on a web page. Let's find out how you can improve your conversion rate optimisation with some easy to implement ideas. To start improve your conversion rate optimisation you need tools and analysis. Analytics Google Analytics (free) KISSMetrics Mixpanel Segment.io Chartbeat Clicky RJ Metrics Woopra Chart.io Custora Sumall GoodData Omniture There are more, and depending on your business size, type and traffic you’ll need to determine which is best for you. For most companies Google Analytics is plenty. If you want to have a cohort analysis, using a combination of Google Analytics and KissMetrics will do the trick. User Surveys Qualaroo offers online surveys that allow you to ask questions on specific pages or at specific points in your funnel. Survey Monkey is an online survey tool, which helps create surveys, customer feedback and market research via email and social media. SurveyGizmo is a software company focusing on creating online surveys, questionnaires, and forms for capturing and analysing data. PollDaddy is a user-friendly polling software that can be used to get user feedback via email or social media. Survey.io is a fixed survey designed for startups to determine if their product is delivering an irreplaceable must-have experience. User Testing Optimizely is a website optimisation platform focused on A/B and multivariate testing, making them easier to use and understand on your site. Google Content Experiments is integrated with Google Analytics and is Google’s free website testing and optimisation tool. Visual Web Optimiser also focuses on an easier approach to A/B and multivariate testing but includes behavioural targeting, heatmaps, usability testing, as well. Unbounce also offers A/B testing, while focusing predominantly on the efficiency of your landing page. Google Optimize, a new tool from Google will conduct A/B tests for free and it is currently is gradually rolling out. Now, with one of each category, we can run tests and improve our conversion rate optimisation and also our revenue. 1. Site Speed This factor can't be ignored. As the Tag Man blog reports, a single 1-second delay in page-load can result in a 7% decrease in conversions. Pay attention to your site speed to ensure your optimisation efforts aren’t in vain. Use an analytics tool to find your Page Speed. For ecommerce the conversion rate is a closed sale, but for a blog the conversion can be any goal you want. How to fix this: Minimise HTTP Requests. Reduce server response time. Enable compression. Enable browser caching. Minify Resources. Optimise images. Optimise CSS Delivery. Prioritise above-the-fold content. 2. Take advantage of what you have Your website is your salesperson. A good salesperson markets their most appealing and important attributes. Double-check your website and make sure you’re communicating your value and advantages. Also, be sure to track these interactions and how people react. Use an analytics platform to measure the importance. Social proof. Testimonials will give users a feeling of security and trust. Appeals to authority. Try to find a trend, belief, or position that’s advocated by someone of stature in your area of expertise to promote you. Third party validation. A variant of the social proof above, but instead of testimonials you can use trusted brand logos to borrow their brand equity for your brand. Build a community. Users are the main reason to be online. Give them a way to participate in comments, reviews and feedback. Referrals. Try to make your clients your most important advocates. Help them refer you, with incentives like discounts or free gifts to users who recruit others through email, social media, etc. 3. Raise Your Average Order Value (AOV) Here are a few methods of increasing your AOV. You can improve your revenue even without improving your conversion rate. Bundle the products. Combine complementary products, and give the user a discount for purchasing them as a bundle. You can A/B test, measure and survey to find out what has the biggest impact. Promotions. Promotions come in many shapes and forms (free shipping, 1+1, 2+1, etc). Implement Enhanced Ecommerce if you're an ecommerce store and track the promotions interaction and how each contributes to the sale. Rewards. Loyalty programs will keep users returning. In particular, programs that reward higher levels of spending (escalating coupons are an example of this) can positively impact AOV. Track this with an analysis platform as with a user-centred platform. 4. How Friendly is your online presence? Do you have a responsive website? There is a good chance that some of your users will be arriving via their phones and tablets, and almost nothing is more difficult to navigate than a site that's not mobile-friendly. If a user cannot navigate your site, they can’t become customers. Compare your conversion rate with your analytics platform for each device. Does your website work on most browsers? Not all browsers are built the same–that goes without saying, but do you know what browsers are most popular among your users? There is a chance that your site is awesome on Chrome, but a mess on Internet Explorer. Do the research. Load up the browsers and make sure a user’s arrival is always solid. Fixing any browser specific issues could result in a rise in conversions. Do you have a healthy privacy policy? It is good to show users their information is secure: signals, like SSL (https://) lock images, trusted badges, and social proof can all allay fears. Make sure you have a complete privacy policy linked from the footer of every page on your site. Do you speak your client's language? If you're a client based website that accessible worldwide, wouldn't you want to adjust to offer your services to your audience? If you’re ignoring language support, you could be losing vital clients. Did you build your website starting from the user? No user will ever complain that your site is too easy to use, fast or clear. How many clicks does it take for a user to get to your must have experience? Have you ever counted? Make sure you are thinking as the client where less is more. Do you adjust for your customers time? Information on your landing page should be prioritised by importance. You typically have five seconds to convince a visitor to stick around. Make the most of that brief moment in time. How good is your hook, and how well do you deliver on the promise? Are you adapting to the new video trend? A video on your landing page has the chance to drive conversions. Consider YouTube, or other services as long as users do not have to download additional plugins. Can your customers leave ratings and reviews? Having reviews and ratings bring real feedback from real clients. Clients are then more likely to make a decision based on what they read from other perspectives. Have any questions? Get in touch with our experts!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-01-04

Why do I need Google Analytics with Shopify?

If the lack of consistency between Shopify’s dashboards and the audience numbers in Google Analytics is confusing, you might conclude that it’s safer to trust Shopify. There is a problem with the reliability of transaction volumes in Google Analytics (something which can be fixed with Littledata’s app) - but using Shopify’s reports alone to guide your marketing is ignoring the power that has led Google Analytics to become over by over 80% of large retailers. Last-click attribution Let’s imagine your shoe store runs a Google AdWords campaign for ‘blue suede shoes’. Shopify allows you to see how many visits or sales were attributed to that particular campaign, by looking at UTM ‘blue suede shoes’. However, this is only capturing those visitors who clicked on the advert and in the same web session, purchased the product. So if the visitor, in fact, went off to check prices elsewhere, or was just researching the product options, and comes back a few hours later to buy they won’t be attributed to that campaign. The campaign reports in Shopify are all-or-nothing – the campaign or channel sending the ‘last-click’ is credited with 100% of the sale, and any other previous campaigns the same customer saw is given nothing. Multi-channel attribution Google Analytics, by contrast, has the ability for multi-channel attribution. You can choose an ‘attribution model’ (such as giving all campaigns before a purchase equal credit) and see how much one campaign contributed to overall sales. Most online marketing can now be divided into ‘prospecting’ and ‘retargeting’; the former is to introduce the brand to a new audience, and the latter is to deliberately retarget ads at an engaged audience. Prospecting ads – and Google AdWords or Facebook Ads are often used that way – will usually not be the last click, and so will be under-rated in the standard Shopify reports. So why not just use the analytics reports directly in Google AdWords, Facebook Business, Twitter Ads etc.? Consistent comparison The problem is that all these different tools (and especially Facebook) have different ways of attributing sales to their platform – usually being as generous as possible to their own adverting platform. You need a single view, where you can compare the contribution of each traffic source – including organic search, marketing emails and referrals from other sites – in a consistent way. Unfortunately, Google Analytics needs some special setup to do that for Shopify. For example, if the customer is redirected via a payment gateway or a 3D secure page before completing the transaction then the sale will be attributed to a ‘referral’ from the bank - not the original campaign. Return on Advertising Spend (ROAS) Once you iron out the marketing attribution glitches using our app, you can make meaningful decisions about whether a particular form of marketing is driving more revenue that it is costing you – whether there is a positive Return on Advertising Spend. The advertising cost is automatically imported when you link Adwords to Google Analytics, but for other sources, you will need to upload cost data manually or use a tool like funnel.io . Then Google Analytics uniquely allows you to decide if a particular campaign is bringing more revenue than it is costing and, on a relative basis, where are the best channels to deploy your budget. Conclusion Shopify’s dashboards give you a simple daily overview of sales and products sold, but if you are spending more than hundreds of dollars a month on online advertising – or investing in SEO tactics – you need a more sophisticated way to measure success. Want more information on how we will help improve your Shopify analytics? Get in touch with our experts! Interested in joining the list to start a free trial? Sign up! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-12-07

Tracking customers in Google Analytics

If your business relies on customers or subscribers returning to your site, possibly from different devices (laptop, smartphone, etc.) then it’s critical you start tracking unique customers rather than just unique visitors in Google Analytics. By default, Google Analytics tracks your customers by browser cookies. So ‘Bob’ is only counted as the same visitor if he comes to your site from the same browser, but not if he comes from a different computer or device. Worse, if Bob clears his cookies or accesses your site via another mobile app (which won't share cookies with the default browser) then he'll also be counted as a new user. You can fix this by sending a unique customer identifier every time your customer signs in. Then if you send further custom data about the user (what plan he / she is on, or what profile fields they have completed) you can segment any of the visits or goals by these customer attributes. There are 2 possible ways to track registered users: Using Google Analytics’ user ID tracker By storing the clientId from the Google cookie when a new user registers, and writing this back into the tracker every time the same user registers In both cases, we also recommend sending the user ID as a custom dimension. This allows you segment the reports by logged in / not logged in visitors. Let's look at the pros and cons. Session stitching Tracking customers involves stitching together visits from different devices into one view of the customer. Option 1, the standard User ID feature, does session stitching out the box. You can optionally turn ‘session unification’ on which means all the pageviews before they logged in are linked to that user. With option 2 you can stitch the sessions, but you can't unify sessions before the user logs in - because they will be assigned a different clientId. So a slight advantage to option 1 here. Reporting simplicity The big difference here is that with option 1 all of the user-linked data is sent to a separate 'registered users' view, whereas in options 2 it is all on the same view as before. Suppose I want a report of the average number of transactions a month for registered vs non-registered visitors. With both options, I can only do this if I also send the user ID as a custom dimension - so I can segment based on that custom dimension. Additionally, with option 1 I can see cross-device reports - which is a big win for option 1. Reporting consistency Once you start changing the way users are tracked with option 2 you will reduce the overall number of sessions counted. If you have management reports based on unique visitors, this may change. But it will be a one-time shift - and afterwards, your reports should be stable, but with a lower visit count. So option 1 is better for consistency Conclusion Option 1 - using the official user tracking - offers a better route to upgrade your reports. For more technical details on how this tracking is going to work, read Shay Sharon’s excellent customer tracking post. Also, you can watch more about customer tracking versus session tracking in this video. Have any questions? Comment below or get in touch with our team of experts!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-12-06

Comparing 3 time ranges in Google Analytics

Selecting time ranges for comparison in Google Analytics can trip you up. We find comparing 28-day or 7-day (one week) periods the most reliable method. Gotcha 1: Last 4 days with previous 4 days This is comparing the same time periods (4 days) so shouldn't they be comparable? No! Most websites show a strong weekly cycle of visits (either stronger or weaker on the weekend), so the previous four days may be a very different stage of the week. Gotcha 2: Last month compared with the previous month Easy - we can see traffic has gone up by 5% in March. No! March has 11% more viewing time (3 extra days) than February. So the average traffic per day in March has actually dropped by 5.5%. Gotcha 3: Last week compared with the previous week You can see what's coming this time... Certain weeks of the year are always abnormal, and the Christmas period is one of them. But most business / educational sites it is a very quiet period. The best comparison would be with the same week last year. Have any questions? Let us know by commenting below or get in touch with our lovely experts!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-12-01

The referral exclusion list: what it is and how to update it?

The referral exclusion list is only available for properties using Universal Analytics ... so please make the jump and take advantage of the benefits! Let's find out how excluding referral traffic affects your data and how you can correct some of the wrong attributions of sales. By default, a referral automatically triggers a new session. When you exclude a referral source, traffic that arrives to your site from the excluded domain doesn’t trigger a new session. Because each referral triggers a new session, excluding referrals (or not excluding referrals) affects how sessions are calculated in your account. The same interaction can be counted as either one or two sessions, based on how you treat referrals. For example, a user on my-site.com goes to your-site.com and then returns to my-site.com. If you do not exclude your-site.com as a referring domain, two sessions are counted, one for each arrival at my-site.com. If, however, you exclude referrals from your-site.com, the second arrival to my-site.com does not trigger a new session, and only one session is counted. Common uses for referral exclusions list in Google Analytics: Third-party payment processors Cross-subdomain tracking If you add example.com to the list of referral exclusions, traffic from the domain example.com and the subdomain another.example.com are excluded. Traffic from another-example.com is not excluded. Only traffic from the domain entered in the referral exclusions list and any subdomains are excluded. Traffic from domains that only have substring matches are not excluded. How to add domains in the referral exclusion list: Sign in to your Gooogle Analytics account. Click admin in the menu bar at the top of any page. In the account column, use the drop-down to select the Google Analytics account that contains the property you want to work with. In the property column, use the drop-down to select a property. Click tracking info. Click referral exclusion list. To add a domain, click +add referral exclusion. Enter the domain name. Click create to save. The referral exclusion list used contains matching. For example, if you enter example.com, then traffic from sales.example.com is also excluded (because the domain name contains example.com). Need help with these steps? Get in touch with one of our experts and we'd be happy to assist you!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-29

4 common pitfalls of running conversion rate experiments from Microsoft

At a previous Measurefest conference, one of the speakers, Craig Sullivan, recommended a classic research paper from Microsoft on common pitfalls in running conversion rate experiments. It details five surprising results which took 'multiple-person weeks to properly analyse’ at Microsoft and published for the benefit of all. As the authors point out, this stuff is worth spending a few weeks getting right as ‘multi-million-pound business decisions’ rest on the outcomes. This research ultimately points out the importance of doing A/A Testing. Here follows an executive overview, cutting out some of the technical analysis: 1. Beware of conflicting short-term metrics Bing’s management had two high-level goals: query share and revenue per search. The problem is that it is possible to increase both those and yet create a bad long-term company outcome, by making the search algorithm worse. If you force users to make more searches (increasing Bing’s share of queries), because they can’t find an answer, they will click on more adverts as well. “If the goal of a search engine is to allow users to find their answer or complete their task quickly, then reducing the distinct queries per task is a clear goal, which conflicts with the business objective of increasing share.” The authors suggest a better metric in most cases is lifetime customer value, and the executives should try to understand where shorter-term metrics might conflict with that long-term goal 2. Beware of technical reasons for experiment results The Hotmail link on the MSN home page was changed to open Hotmail in a separate tab/window. The naïve experiment results showed that users clicked more on the Hotmail link when it opened in a new window, but the majority of the observed effect was artificial. Many browsers kill the previous page’s tracking Javascript when a new page loads – with Safari blocking the tracking script in 50% of pages opening in the same window. The “success” of getting users to click more was not real, but rather an instrumentation difference. So it wasn’t that more people were clicking on the link – but actually that just more of the links were being tracked in the ‘open in new tab’ experiment. 3. Beware of peeking at results too early When we release a new feature as an experiment, it is really tempting to peek at the results after a couple of days and see if the test confirms our expectation of success (confirmation bias). With the initial small sample, there will be a big percentage change. Humans then have an innate tendency to see trends where there aren’t any. So the authors give the example of this chart: Most experimenters would see the results, and even though they are negative, extrapolate the graph along the green line to a positive result and four days. Wrong. What actually happens is regression to the mean. This chart is actually from an A/A test (i.e. the two versions being tested are exactly the same). The random differences are biggest at the start, and then tail off - so the long term result will be 0% difference as the sample size increases. The simple advice is to wait until there are enough test results to draw a statistically significant conclusion. That generally means more than a week and hundreds of individual tests. 4. Beware of the carryover effect from previous experiments Many A/B test systems use a bucketing system to assign users into one experiment or another. At the end of one test the same buckets of users may be reused for the second test. The problem is that if users return to your product regularly (multiple times daily in the case of Bing), then a highly positive or negative experience in one of the tests will affect all of that bucket for many weeks. In one Bing experiment, which accidentally introduced a nasty bug, users who saw the buggy version were still making fewer searches 6 months after the experiment ended. Ideally, your test system would re-randomise users for the start of every new test, so those carryover effects are spread as wide as possible. Summary For me the biggest theme coming out of their research is the importance of A/A tests – seeing what kind of variation and results you get if you don’t change anything. Which makes you more aware of the random fluctuations inherent in statistical tests. In conclusion, you need to think about the possible sources of bias before acting on your tests. Even the most experienced analysts make mistakes! Have any comments? Let us know what you think, below!    

2016-11-27

5 tips to avoid a metrics meltdown when upgrading to Universal Analytics

Universal Analytics promises some juicy benefits over the previous standard analytics. But having upgraded 6 different high traffic sites there are some pitfalls to be aware of. Firstly, why would you want to upgrade your tracking script? More reliable tracking of page visitors - i.e. fewer visits untracked More customisation to exclude certain referrers or search terms Better tools for tracking across multiple domains and tracking users across different devices Track usage across your apps for the same web property Ability to send up to 20 custom dimensions instead of the previous limit of only 5 custom variables If you want to avoid any interruption of service when you upgrade, why not book a quick consultation with us to check if Universal Analytics will work in your case. But before you start you should take note of the following. 1. Different tracking = overall visits change If your boss is used to seeing dependable weekly / monthly numbers, they may query why the number of visits has changed. Universal Analytics is likely to track c. 2% more visits than previously (partly due to different referral tracking - see below), but it could be higher depending on your mix of traffic. PRO TIP: Set up a new web property (a different tracking code) for Universal Analytics and run the old and new trackers alongside each other for a month. Then you can see how the reports differ before sharing with managers. Once this testing period is over you'll need to upgrade the original tracking code to Universal Analytics to you keep all your historic data. 2. Different tracking of referrals Previously, if Bob clicked on a link in Twitter to your site, reads, goes back to Twitter, and within 30 minutes clicks on a different link to your site - that would be counted as one visit and the 2nd referral source would be ignored. In Universal Analytics, when Bob clicks on the 2nd link he is tracked as a second visit, and 2nd referral source is stored. This may be more accurate for marketing tracking, but if Bob then buys a product from you, going via a secure payment gateway hosted on another domain (e.g. paypal.com) then the return from the payment gateway will be counted as a new visit. All your payment goals or ecommerce tracking will be attributed to a referral from 'paypal.com'. This will ruin your attribution of a sale to the correct marketing channel or campaign! PRO TIP: You need to add all of the payment gateways (or other third party sites a user may visit during the payment process) to the 'Referral Exclusion List'. You can find this under the Admin > Property > Tracking codes menu: 3. Tracking across domains If you use the same tracking code across different domains (e.g. mysite.co.uk and mysite.com or mysite.de) then you will need to change the standard tracking script slightly. By default the tracking script you copy from Google Analytics contains a line like: ga('create', 'UA-XXXXXXX-1', 'mysite.com');. This will only track pages that strictly end with 'mysite.com'. PRO TIP: It's much safer to change the tracker to set that cookie domain automatically. The equivalent for the site above would be ga('create', 'UA-XXXXXXX-1', 'auto');. The 3rd argument of the function is replaced with 'auto'. 4. Incompatibility with custom variables Only relevant if you send custom data already Custom variables are only supported historically in Universal analytics. That means you will need to change any scripts that send custom data to the new custom dimension format to keep data flowing. Read the developer documentation for more. PRO TIP: You'll need to set the custom dimension names in the admin panel before the custom data can be sent from the pages. You can also only check that the custom dimensions are being sent correctly by creating a new custom report for each dimension. 5. User tracking limitations We wouldn't recommend implementing the new user ID feature just now, as it has some major limitations compared with storing the GA client ID. You need to create a separate view to see the logged-in-user data, which makes reporting pageviews a whole lot more complex. Visits a user made to your site BEFORE signing up are not tracked with that user - which means you can't track the marketing sources by user PRO TIP: See our user tracking alternative. Got more tips on to setting up Universal Analytics? Please share them with us in the comments, or get in touch if you want more advice on how to upgrade!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-26

Widget Tracking with Google Analytics

I was asked recently about the best way to track a widget, loaded in an iframe, on a third-party site with Google Analytics. The difficulty is that many browsers now block 3rd party cookies (those set by a different domain to the one in the browser address bar) – and this applies to a Google Analytics cookie for widgets as much as to adverts. The best solution seems to be to use local storage on the browser (also called HTML5 Storage) to store a persistent identifier for Analytics and bypass the need to set a cookie – but then you have to manually create a clientID to send to Google Analytics. See the approach used by ShootItLive. However, as their comment on line 41 says, this is not a complete solution - because there are lots of browsers beyond Safari which block third party cookies. I would take the opposite approach and check if the browser supports local storage, and only revert to trying to set a cookie if it does not. Local storage is now possible on 90% of browsers in use and the browsers with worst 3rd party cookie support (Firefox and Safari) luckily have the longest support for local storage. As a final note, I would set up the tracking on a different Google Analytics property to your main site, so that pageviews of widgets are not confused with pageviews of your main site. To do list: Build a script to create a valid clientID for each new visitor Call ga('create) function, setting 'storage' : 'none', and getting the 'clientID' from local storage (or created from new) Send a pageview (or event) for every time the widget is loaded. Since the widget page is likely to be the same every time it is embedded, you might want to store the document referrer (the parent page URL) instead Need help with the details? Get in touch with our team of experts and we'd be happy to help!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-25

How to link Adwords and Google Analytics

If you are running an AdWords campaign you must have a Google Analytics account. We will show you how to link these two accounts so you can unleash the full reporting potential of both platforms. 1. Why should you link Analytics and AdWords? When you link Google Analytics and AdWords, you can: See ad and site performance data in the AdWords reports in Google Analytics. Import Google Analytics goals and ecommerce transactions directly into your AdWords account. Import valuable Analytics metrics—such as bounce rate, avg. session duration, and pages/session—into your AdWords account. Take advantage of enhanced remarketing capabilities. Get richer data in the Google Analytics multi-channel funnels reports. Use your Google Analytics data to enhance your AdWords experience. 2. How to link Google Analytics and AdWords The linking wizard makes it easy to link your AdWords account(s) to multiple views of your Google Analytics property. If you have multiple Google Analytics properties and want to link each of them to your AdWords account(s), just complete the linking wizard for each property. Sign into your Google Analytics account at www.google.com/analytics. Note: You can also quickly open Google Analytics from within your AdWords account. Click the tools tab, select analytics, and then follow the rest of these instructions. Click the admin tab at the top of the page. In the account column, select the analytics account that contains the property you want to link to one or more of your AdWords accounts. In the property column, select the analytics property you want to link, and click AdWords Linking. Use one of the following options to select the AdWords accounts you want to link with your analytics property. Select the checkbox next to any AdWords accounts you want to link with your analytics property. If you have an AdWords manager (MCC) account, select the checkbox next to the manager account to link it (and all of its child accounts) with your analytics property. If you want to link only a few managed accounts, expand the manager account by clicking the arrow next to it. Then, select the checkbox next to each of the managed AdWords accounts that you want to link. Or, click all linkable to select all of managed AdWords accounts under that MCC. You can then deselect individual accounts, and the other accounts will stay selected. Click the continue button. In the link configuration section, enter a link group title to identify your group of linked AdWords accounts. Note: Most users will only need one link group. We recommend creating multiple link groups only if you have multiple AdWords accounts and want data to flow in different ways between these accounts and your analytics property. For example, you should create multiple link groups if you need to either link different AdWords accounts to different views of the same Google Analytics property or enable auto-tagging for only some of your AdWords accounts. Select the Google Analytics views in which you want the AdWords data to be available. If you've already enabled auto-tagging in your AdWords account, skip to the next step. The account linking process will enable auto-tagging for all of your linked AdWords accounts. Click advanced settings only if you need to manually tag your AdWords links. Click the link accounts button. Congratulations! Your accounts are now linked. If you opted to keep auto-tagging turned on (recommended), Google Analytics will automatically start associating your AdWords data with customer clicks. For a deeper view and debugging you should also read the Google Analytics guide. Have any questions on setting this up? Get in touch and we'd be happy to help!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-24

Who visited my website? Find out with Google Analytics

In every retail business, knowing your customers is vital to succeeding. All decisions you make about business and marketing strategies must begin from the user's perspective. Let's find out how we can build the user persona with the data that lies in Google Analytics. Even though Google's user profile is not as fancy as Facebook's, you can still have a pretty good idea about your customers. Let's start with the basics, and ask the most basic questions: How many of my customers are men or women? What is the age range of my customers? What devices do they use to access my website? How often do they visit my website? What are their interests? What makes them convert? For the first two questions, you should already have enabled Demographics and Interest reports in your Google account. If not, go to Admin > Property Settings > Enable Demographics and Interest reports. The split of age and sex can be seen in Audience > Demographics. The most interesting thing here is that you can add a second dimension to compare and see how people are different based on more than one vector. If you add a second dimension, such as Device Category, you will get a split like this: You can see from the above screenshot that females prefer mobile and are the majority user. Also when females are on desktop, they are more likely to spend more time on the website. You can go into more depth and analyse the conversion rate also. You can find out the behaviour of new vs. returning customers from the report, New vs. Returning under Audience. Add a second dimension "Gender" and you will see who's more likely to come back to your website. Based on the above screenshot, women are returning about 25% of the time, while men return about 21% of the time. Also, men have a higher bounce rate. Under Audience, you will also find the Frequency & Recency report and the Engagement report. If you create two new segments by gender: female and male, you will find who your most loyal visitors are. The interests (Google reads them from the user behaviour online) can be found under Audience > Interests. It is best to split the report based on females and males. You will now have a full view of your customers. And for the final and most important question: what makes them convert?, you can find this out by going to Aquisition > Channels. Add a second dimension by gender, age or interests and analyse the traffic for each channel. Find out what channel brings the most important users. In the screenshot below, you can see that woman are more likely to buy if the website is referred. This means that the reputation of the website is a big factor in their decision. Don't be shy when creating custom reports because you can drill down the data to multiple levels of understanding. Applying second dimensions has its limitations and you can see only a part of the information at once. If you still need a more detailed view of what each customer does on the website, we strongly recommend the User Explorer menu. We found it useful to find out how different touch points are important and how long the path is for a valuable customer. Also, it was useful in debugging and creating a marketing strategy based on the customer's flow. The bottom line is that you can answer "who is your customer?" with Google Analytics through its reports if you learn to see the report from different perspectives. Feel free to drop us a line if you use any other report that is relevant to this article!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-17

What are custom dimensions in Google Analytics?

By default, Google Analytics allows you to segment traffic by standard dimensions such as visitor location, screen size, or traffic source. You can view smarter reports by adding custom dimensions specific for your business. Give me an example Let's say when your members register they add a job title. Would you like to see reports on the site activity for a particular job title, or compare conversion for one job title versus another? In which case you would set a custom dimension of 'Job Title' and then be able to filter by just the 'Researchers' for any Google Analytics report. Or if you run a blog / content site, you could have a dimension of 'author' and see all the traffic and referrals that a particular author on your site gets. How do I set this up? First, you need to be on Universal Analytics, and then you need to tag each page with one or more custom dimensions for Google Analytics. This is more easily done with Google Tag Manager and a data layer. It may be that the information is already on the web page (like the author of this post), but in many cases, your developer will need to include it in the background in a way that can be posted to Google Analytics. Then you will need to set up a custom report to split a certain metric (like page views) by the custom dimension (e.g. author). Please contact our specialists if you want more advice on how to set up custom dimensions!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-16

Exclude fake 'bot' traffic from your site with Google Analytics

Ever wondered why so few visitors convert on your site? One answer is that a big chunk of your traffic is from search engine spiders and other web 'bots' which have no interest in actually engaging with you. Google Analytics has a great new feature to exclude this bot traffic from your site. All you need to do is check a box under the Admin > View > View Settings. The new option is down the bottom, underneath currency selection. It uses the IAB /ABC Bots and Spiders list, which is standard for large publishers, and updated monthly. Warning: you will see a dip in traffic from the date you apply the setting. If you're looking for a more comprehensive method to exclude spam and ghost referrals, check out our how-to guide! Have some questions about this? Get in touch with our Google Analytics experts!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-15

What are Enhanced Ecommerce reports?

In May 2014 Google Analytics introduced a new feature: Enhanced Ecommerce tracking. If you run an ecommerce operation, this gets you much more detailed feedback on your checkout process. What will I see? Shopping behaviour: how are people converting from browsers to purchasers? Checkout behaviour: at what stage of your checkout do buyers abandon the process Product performance: which products are driving your sales, and which have a high return rate Real campaign returns: see your real return on marketing investment including promotional discounts and returns How do I set this up? The bad news is it definitely requires an experienced software developer for the setup. The reports require lots of extra product and customer information to be sent to Google Analytics. You can read the full developer information on what you can track, or our own simpler guide for tracking ecommerce via Tag Manager. However, if you already have standard ecommerce tracking and Google Tag Manager, we can set Enhanced reports up in a couple of days with no code changes on your live site - so no business disruption or risk of lost sales. Is it worth implementing? Imagine you could identify a drop-off stage in your checkout process where you could get a 10% improvement in sales conversion or a group of customers who were unable to buy (maybe due to language or browser difficulties) – what would that be worth? Many businesses have that kind of barrier just waiting to be discovered…   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-14

How to track time on page with Google Tag Manager

Our script for accurate tracking of time on page beats Google's default measurement to give you an accurate picture of how long users are spending on your page open and in focus. This post translates the approach into Google Tag Manager. The setup consists of two tags (one custom), one firing rule and two variables. Step by step: 1. Add the timer script as a custom HTML tag <script><br /> /*<br /> Logs the time on the page to dataLayer every 10 seconds<br /> (c) LittleData consulting limited 2014<br /> */<br /> (function () {<br /> var inFocus = true;<br /> var intervalSeconds = 10; //10 seconds<br /> var interval = intervalSeconds * 1000;<br /> var eventCount = 0;<br /> var maxEvents = 60; //stops after 10 minutes in focus<br /> var fnBlur = function(){inFocus = false; };<br /> var fnFocus = function(){inFocus= true; };<br /> if (window.addEventListener) {<br /> window.addEventListener ('blur',fnBlur,true);<br /> window.addEventListener ('focus',fnFocus,true);<br /> }<br /> else if (window.attachEvent) {<br /> window.attachEvent ('onblur',fnBlur);<br /> window.attachEvent ('onfocus',fnFocus);<br /> }<br /> var formatMS = function(t){<br /> return Math.floor(t/60) +':'+ (t%60==0?'00':t%60);<br /> }<br /> var timeLog = window.setInterval(function () {<br /> if (inFocus){<br /> eventCount++;<br /> var secondsInFocus = Math.round(eventCount * intervalSeconds);<br /> dataLayer.push({"event": "LittleDataTimer", "interval": interval, "intervalSeconds": intervalSeconds, "timeInFocus": formatMS(secondsInFocus) });<br /> }<br /> if (eventCount>=maxEvents) clearInterval(timeLog);<br /> }, interval);<br /> })();<br /> </script> 2. Add two variables to access the data layer variables One for the formatted time, which will feed through the event label And one for the number of seconds in focus since the last event, which will feed through the event value 3. Add the firing rule for the event 4. Add the tag that reports the timer event to Google Analytics Options and further information You can change the timer interval in the custom HTML tag - the reporting will adjust accordingly. Choosing the interval is a trade-off between the resolution of the reporting and the load on the client in sending events, as well as Google's 500 hit per session quota. We've chosen ten seconds because we think the users who are in 'wrong place' and don't engage at all will leave in under ten seconds, anything more is some measure of success. If you'd like assistance implementing this or something else to get an accurate picture of how users interact with your site, get in touch!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-14

Accurate tracking of time on site

There’s a flaw in the way Google Analytics measures ‘time on site’: the counter only starts from the second page visited, so all one-page visits are counted as zero time on site. If a visitor comes to your page, stays for 10 minutes reading – and then closes the window… that’s counted as ZERO time. With landing pages that have lots of interaction, or the call to action is a phone call rather than a click, this can be a real problem. Pasting the Javascript below onto all the pages of your site will fix the problem. The script logs an event to Google Analytics for every 10 seconds the visitor stays on the page, regardless of whether they bounced or not. But it won't affect your bounce rate or time on site for historical comparison *. We suggest you look closely at how visitors drop off after 10, 20 and 30 seconds to see which of your web content could be improved. Paste this into the source of your all your pages, after the Google Analytics script <!-- Time on Site tracking (c) LittleData.co.uk 2014 --><script>(function(e){var t=true;var n=0;var r=true;var i=function(){t=false};var s=function(){t=true};if(window.addEventListener){window.addEventListener("blur",i,true);window.addEventListener("focus",s,true)}else if(window.attachEvent){window.attachEvent("onblur",i);window.attachEvent("onfocus",s)}var o=function(e){return Math.floor(e/60)+":"+(e%60==0?"00":e%60)};var u=window.setInterval(function(){e=e+10;if(t){n=n+10;if(typeof _gaq==="object"){_gaq.push(["_trackEvent","Time","Log",o(n),n,r])}else if(typeof ga==="function"){ga("send",{hitType:"event",eventCategory:"Time",eventAction:"Log",eventLabel:o(n),eventValue:10,nonInteraction:"true"})}}},1e4);window.setTimeout(function(){clearInterval(u)},601e3)})(0)</script> What you'll see In Google Analytics go to Behaviour .. Events .. Top Events and click on the event category 'Time'.                               Searching for a particular time will find all the people who have stayed at least that length of time. e.g. 0:30 finds people who have stayed more than 30 seconds. FAQs Does this affect the way I compare bounce rate or time-on-site historically? No. The script sends the timer events as 'non-interactive' meaning they won't be counted in your other metrics. Without this, you would see a sharp drop in bounce rate and an increase in time on site, as every visitor was counted as 'non-bounce' after 10 seconds. If you prefer this, see below about adapting the script. Will this work for all browsers? Yes, the functions have been tested on all major, modern browser: IE 9+, Chrome, Safari and Firefox. What if I upgrade to Universal Analytics? Don’t worry – our script already checks which of the two tracking scripts you have (ga.js or analytics.js) and sends the appropriate log. Will this max out my Google Analytics limits? The script cuts off reporting after 5 minutes, so not to violate Google’s quota of 200 – 500 events that can be sent in one session Can I adapt this myself? Sure. The full source file is here. Need more help? Get in touch with our experts!

2016-11-13

How to read the frequency report in Google Analytics

Google Analytics engagement reports can provide great insight into user behaviour on your site. However, it’s not obvious how to read them - and when you figure out how to read them, it’s not always obvious what’s good! The visitor frequency report is found under Audience > Behaviour > Frequency and recency in the Google Analytics menu. The report shows the sessions, by the number of visits for each visitor. Google’s explanation of how to read the frequency graph is nice and clear. However, their simplified example leaves out something important: returning users. In the graph, the visit count shown is for the whole user history, not just the period of the report. So if a visitor has come three times before the period their session will show in the three band. Similarly, if a user has visited the site five times in the past and then visits the site twice during the period, they will in count of sessions as 6 and 7. The example below is from a newly released site, where nearly all the visits are from the developers, who have been many times before - so there are no visitors in the 1,2,3,4 or 5 visit bands for this site in this period. The fact that the banding is based on the whole user history, not just behaviour during the period, can make the report much harder to interpret - you can't easily see the drop off in repeat visitation if an unknown number of return visitors at some point in their many visits history are also coming in. Fortunately, Google Analytic's segmentation capability comes to the rescue! For example, you can find out about the returning behaviour of visitors who came the first time during the period in question, with the segment - the settings are in the screenshot below. Note that you need to change the segment to filter users, not sessions, otherwise you will just create a more complex version of the built-in new users segment! Here’s an example of the sort of thing you might see with the segment, showing 3 segments: All Session (built in) 'User sessions = 1'  - custom segment for users having their first session in the period New Users (built in) Note: For the visit count = 1 band, the session count is the same across all three segments, For the visit count = 2 band, (and above) the number for all sessions is higher, because it includes all the users who came in on their second visit during the period. The number for the user sessions = 1 segment, is lower because it includes only the users who had their first visit during the period. The number for new users is zero because users are not new on their 2nd visit As you can see, the custom segment makes it possible to see the real return rate of new visitors in the period, narrowing to visitors who came the first time in the period in question. From this example, you can also see how misleading it would be to naively interpret the default 'all sessions' segment for, say, four sessions as the number who returned four times during the period - clearly there is a large number who have previous visits outside the period of the report. Note that none of the segments in the example actually gives the number of users who returned four times during the period - this is actually really difficult to obtain. Leaving that question aside for now, to extract some real insight from the approach of segmenting in the frequency report, combine that segment condition with a goal, say ‘transactions per user > 0’ - then you can see how many new users went on to a transaction, and how many visits they made during the period. Need help to set this up or have any questions? Get in touch with our team of experts and we'd be happy to answer any questions! This is a valuable segment to monitor and analyse - how many users have gone from first visit to a transaction this week, and how many sessions did they make along the way?

2016-11-11

How to set up ecommerce tracking with Google Tag Manager

Enhanced ecommerce tracking requires your developers to send lots of extra product and checkout information in a way that Google Analytics can understand. If you already use GTM to track pageviews you must send ecommerce data via Google Tag Manager Step 1 Enable enhanced ecommerce reporting in the Google Analytics view admin setting, under 'Ecommerce Settings' Step 2 Select names for your checkout steps (see point 4 below): Step 3 Get your developers to push the product data behind the scenes to the page 'dataLayer'. Here is the developer guide. Step 4 Make sure the following steps are tracked as a pageview or event, and for each step set up a Universal Analytics tracking tag: Product impressions (typically a category or listing page) Product detail view (the product page) Add to basket (more usually an event than a page) Checkout step 1 (views the checkout page) Checkout step 2 etc - whatever registration, shipping or tax steps you have Purchase confirmation Step 5 Edit each tag, and under 'More Settings' section, select the 'Enable enhanced ecommerce features' and then 'use data layer' options: Of course, there's often a bit of fiddling to get the data layer in the right format, and the ecommerce events fires at the right time, so please contact us if you need more help setting up the reports! Step 6 - Checking it is working There is no 'real time' ecommerce reporting yet, so you'll need to wait a day for events to process and then view the shopping behaviour and checkout behaviour reports. If you want to check the checkout options you'll need to set up a custom report: use 'checkout options' as the dimension and 'sessions' and 'transactions' as the metrics. Need some more help? Get in touch with our lovely team of experts and we'd be happy to answer any questions!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.  

2016-11-10

Personalising your site for a local event with Google Optimize

Google Optimize (standard edition) will be released publically at the end of October, allowing free access to powerful AB testing and personalisation features. Here’s a guide to launching your first test, assuming you have the Google Optimize 360 snippet installed on your page. Step 1: Create the experiment I want to trigger a personalisation on Littledata’s homepage, shown only to visitors from London, which promotes a local workshop we have running later this month. It’s not a real AB test, as we won’t have enough traffic to judge whether the banner is a success, but we can use the ‘experiment’ to launch this personalisation for a local audience. First, I need a new test (click the big blue plus sign) and select an AB test. I’ll name my test, and set the editor page as our homepage – which is pre-filled from Google Analytics anyway… Since I have Google Analytics linked, I can select a goal from GA as the objective. In this case, the banner will promote the event (which isn’t tracked on our site) so the only sensible goal is promoting more pageviews – but it’s possible it will also increase signups for our app, so I’ll include that as a secondary objective. Next, I need to add a variant, which is going to load my event banner. I’ve named it ‘add yellow bar’. Clicking on the variant row will take me to the editor. Step 2: Edit the ‘B’ version Note: Optimize’s editor works as a Chrome Plugin, so you’ll need to install that in Google Chrome first. It’s easy to select an element on the page to edit or hide, but my variant will load a new snippet of HTML code which is not already on the page. So I’ll select the element at the top of the page (with ID ‘content’) and then go to the select elements icon in the top left. Now I’ve got the right element to use as a building block, I’m going to add an ‘HTML’ change. And set it to insert the HTML ‘before’ the current element. I’ve pasted in the HTML I’ve recycled from another page. Once I click apply we can see the new element previewing at the top of the page. Next, let’s check it looks OK on mobile – there’s a standard list of devices I can select from. Yes, that is looking good – but if it wasn’t I could click the ‘1 change’ text in the header to edit the code. Lastly, in the editor, you may have noticed a warning notification icon in the top right of the Optimize editor. This is warning me that, since Littledata is a single-page Javascript site, the variant may not load as expected. I’m confident Optimize is still going to work fine in this case. Step 3: Launching the experiment After clicking ‘Done’ on the editor, I go back to the experiment setup. Usually, we’d split the traffic 50:50 between the original and the variant, but in this case, I want to make sure all visitors from London see the message. I’ll click on the weighting number, and then set ‘add yellow bar’ to show 99.9% of the time (I can’t make it 100%). Then, we want to set the geotargeting. The experiment is already limited to the homepage, and now I click ‘and’ to add a 2nd rule and then select ‘geo’ from the list of rules. I want the yellow bar to show only for visitors from London. The city is a standard category, and it recognised London in the autocomplete. As the final step, I need to click ‘Start Experiment’. I can’t edit the rules of any running experiments (as this would mess up the reporting), but I can stop and then copy an experiment which is incorrect. Conclusion Google Optimize makes it really simple to set up tests and personalisations, although it is missing a few features such as scheduling. The premium edition (Optimize 360) will allow more analysis of tests using Google Analytics, and also allow the import of custom audiences from other Google 360 products. This is powerful if you want to launch a customised landing pages experience based on, say, a DoubleClick display ad campaign. So try it out, and if you have any questions, contact one of our experts! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-10-18

How to track forms which don't redirect to a thank you page

Many contact forms now use Javascript to submit and do not redirect to a new page. So to track the form, unless you trigger an event on the submit button, you need to listen for a piece of text (usually saying thank you). We have created a custom HTML script that listens to the changes in the page and triggers an event called 'formSubmitted'. This event can then be used to fire a separate tag with event details to Google Analytics. We've tested this on our contact form at Littledata and here's how you can set it up too. Step 1 The first step is to go through the contact form and see what the steps are in completing it. On ours, you just enter the information in the fields and press "SUBMIT MESSAGE". When the message is sent out, the button will say "SENT!". Here the only thing that changed was the text on the button from 'submit message' to 'sent'. We built this HTML script that listens to the changes on the page, but you'll need to change line 10 to be whatever the message is in your form. You will also need to change line 15 if you have multiple forms on the page. [code lang="js"] &lt;script&gt; // **** Littledata Javascript form tracker **** // Generates a GTM custom event called 'formSubmitted' // When an on-page form is submitted // CHANGE the text to match the message displayed // when the form is successfully completed // It is not case sensitive var text = "sent!" // By default it will search for text within the first form // Set to false if text is outside a form // or change to a higher false if there are multiple forms var formIndex = 0; // OPTIONALLY, restrict the search to an HTML element ID // If you leave this blank, the whole page will be searched; // this causes the script to run more slowly var targetId = "" // **** No changes needed to the script below **** text = text.toLowerCase() dataLayer = dataLayer || []; if (!formIndex &amp;&amp; targetId.length == 0) console.error('Form tracker needs either a form or an element ID') var checkEveryMilliseconds = 500; formTrackerInterval = window.setInterval(function(){ var target = "" if (formIndex &gt;= 0) { var form = document.getElementsByTagName('form') target = (form.length &gt; 0) ? form[formIndex].textContent : ""; } else target = document.getElementById(targetId).textContent target = target.toLowerCase() if (target.indexOf(text) &gt; -1) { window.clearInterval(formTrackerInterval); dataLayer.push({ event: 'formSubmitted' }) } },checkEveryMilliseconds) &lt;/script&gt; [/code] Step 2 Now we need to add the script to listen out for when the form is submitted. Create a custom HTML tag in your GTM container. You can name the tag 'LISTENER Contact form submit event' or anything else you will remember it by. Choose the tag type 'Custom HTML'. Copy and paste your HTML/Javascript into the textbox, and remember to change the var text (line 10) with your own text. Step 3 This tag needs a firing trigger, specifying the rules when it needs to be activated. If you can, only fire on specific pages - the script will slow down the page a little, as it runs every half a second to check the form. Give the trigger a descriptive name - here I've chosen "PAGE About us" Select trigger type as 'Custom Event' and for the event name put " gtm.load ", which means this trigger at page load. We want this trigger to work on a specific page only, so the firing rule goes 'page path equals /about-us', which means that our trigger will work on the www.littledata.io/about-us page only. If you have a number of pages that have the form you're tracking, then you could use 'contains' rule and select part of the link that is applicable to all. For example, if all of your links have word 'contact' in them, then your firing rule would say 'page path contains contact'. Step 4 Now that you have your listener tag set up, you need to create a separate tag to send the event details to Google Analytics. Again, give it a descriptive name so you know what it's for - here I've used 'GA event - contact form submitted'. Select tag type as 'Universal Analytics' and in the tracking ID field, select the variable that contains your GA tracking id. For event category, action and label you have to specify the namings by which this data will be categorised in Google Analytics. Step 5 This tag needs its own trigger to know when to fire, and here you have to use the event created by the listener tag set up during steps 2-3. Here you have to specify that this tag can only fire when event 'formSubmitted' happens. I've called my trigger 'Contact form event', selected trigger type as 'custom event' and entered event name 'formSubmitted. Now you can save it and test in the debugger mode. Try submitting your contact form and see if the event 'formSubmitted' appears. You should also see the tag 'GA event - contact form submitted' fire. If everything's ok, publish the container and do a final test. Make a new form submission and check if you can see the event details come through in Google Analytics real time reports, under events. Need some help setting this up or Google Tag Manger? Why not get in touch by contacting our lovely Google Analytics experts?   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights. Further reading: How to set up event tracking in Google Tag Manager Why should you tag your campaigns? Set up Ecommerce tracking with Google Tag Manager

2016-10-11

Google Optimize versus Optimizely

I’ve been an Optimizely certified expert for a couple of years and have now trialled Google Optimize 360 for a few months, so it seems a good time to compare how they stack up. Optimizely is the current market leader in AB testing (or content experimentation), due to its ease of use and powerful reporting tools. It gives companies an easy way to run many concurrent tests and manage their setup and roll out without the involvement of developers. That was a big step up from Google Content Experiments, where the only way to set up an experiment is to write some Javascript code. The Guardian had some success with Optimizely, where they increased subscriptions by 46%. Google Optimize is an equivalent testing tool, and has copied much of the user interface that made Optimizely popular: you can click on elements within the page to experiment, and change their style, hide them or move them. My only complaint is that the interface is so simple it can take a while to unbury powerful features, such as transform the page via a custom script. There have been many success stories of companies implementing Google 360. Technically, Optimize’s editor is a bit smoother; using a Chrome plugin avoids some of the browser security issues that bugged Optimizely (since internet browsers confused the Optimizely in-page editor with some kind of script hacking). For example, to load Littledata’s homepage in their editor I have to enable ‘insecure scripts’ in Chrome and then navigate to a different page and back to force the editor to re-render. For reporting, Google Optimize 360 gives the ability to see results either in Optimize or as a custom dimension in Google Analytics – so equivalent to Optimizely. Right now Optimize lacks some features for advanced scheduling and user permissions, but I expect those to evolve as the product gathers momentum. The critical difference is with the targeting options Optimizely allows you to target experiments based on the device accessing the page (mobile vs desktop, browser, operating system) and for enterprise plans only to target based on geolocation. The limitation is that every time Optimizely needs to decide whether to run the test, the check for the user’s location may take a few seconds – and the landing page may flicker as a test rule is triggered on not. Google Optimize can target to any audience that you can build in Google Analytics (GA). This means any information you capture in Google Analytics – the number of previous visits, the pages they have previously seen or the ecommerce transactions – can be used in a test or personalisation. For example, in Google Optimize you could serve a special message to users who have previously spent more than $100 in your store. Optimizely has no knowledge of the users’ actions before that landing page, so the only way you could run an equivalent personalisation is to expose this previous purchase value as a custom script on the landing page (or in a cookie). The beauty of Google Optimize is that you are targeting based on information already captured in Google Analytics. There is no technical setup beyond what you were already doing for Google Analytics, and it doesn’t take a developer to build targeting for a very specific audience. Pricing Optimizely starts from under $100/month, but to get access to enterprise features (e.g. for geo-targeting) you will need to spend $2000 plus per month. Google Optimize is currently being sold at a flat rate of $5000 / month for the basic tier of Google 360 customers (which have between 1M to 50M sessions per month), but in future, it could be offered at a lower price to smaller companies. Conclusion Where you’ll appreciate the benefits of Google Optimize is for running personalisations based on complex rules about previous user behaviour, or the campaigns they have seen. The more different tests you are running, the more time and simplicity saving you will get from building the audience in Google Analytics rather than some custom scripts. Google Optimize 360 is currently in beta but you can currently add your email to invite list. For smaller customers, or those with less complex needs, Optimizely still offers better value – but that might change if Google were to offer a limited version of Optimize at a lower price.   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.   Further reading: Create and customise dashboards and widgets in Google Analytics New in Littledata: an improved navigation, trend detection algorithm, and more How to set up internal searches in Google Analytics Image credit: Blastam  

2016-10-05

Create and customise dashboards and widgets in Google Analytics

Every view in Google Analytics comes with a default "My Dashboard". Learn how to customise your dashboards and widgets for the best account overview. Dashboards contain one or more widgets (up to 12 per dashboard) that give you an overview of the KPI’s that you care about most. Create your dashboard To create a dashboard, navigate to your view, then: Go to reporting tab. Click dashboards. Select + new dashboard. In the create dashboard pop-up select blank canvas (no widgets) or starter dashboard (default set of widgets). You can also import dashboard configurations from the solutions gallery, where is most likely that someone thought about some of the KPI’s you are interested and already build a dashboard. Give your dashboard a title, then click create dashboard. Add widgets to your dashboard A dashboard can have up to 12 instances of the following kinds of widgets <piece of information>: Metric—displays a simple numeric representation of a single selected metric. Timeline—displays a graph of the selected metric over time. You can compare this to a secondary metric. Geomap—displays a map of the selected region, with the specified metric plotted on the map. Hover over the map to see the actual metric values. Table—displays up to 2 metrics describing the selected dimension, laid out in a tabular format. Pie—displays a pie chart of the selected metric grouped by a dimension. Mouse over a slice to see the specific metric values. Bar—displays a bar chart of the selected metric grouped by up to 2 dimensions. Mouse over a slice to see the specific metric values. Difference between standard vs. real-time widgets Some of the available widgets can display their data in real-time. These widgets update the metrics automatically (standard widgets, by comparison, update when you load or refresh the dashboard). Real-time widgets can display only the active users or pageviews metrics, depending on the widget. The following widget types are available as real-time widgets: Counter—displays a count of the active users on your site. You can optionally group these users by a selected dimension. Timeline—displays a timeline graph of pageviews on your site for the past 30 to 60 minutes. Geomap—displays a map showing where your active users are coming from. Table—plots a table of your active users against up to 3 selected dimensions. How to add a widget to a dashboard: Create a new dashboard and select blank canvas, or click + add widget on an existing dashboard to open the widget editor. Select the type of widget. Configure the widget’s dimensions, metrics and other options. These vary depending on the type of widget. Scroll or use the search box to locate the specific metric or dimension you want. You can limit the data shown by the widget by clicking add a filter. Filters let you include or exclude data in the specified dimension that match your filter criteria. You can add multiple rows to your filter definition. All conditions must be met for the filter to work. Report and dashboard filters are not the same as view filters. View filters permanently change your data, while report and dashboard filters only limit the data displayed in the report or dashboard. Dashboard filters are specific to the dashboard in which you define them. You can link the widget to a report or a URL. Doing so makes the widget title a link, taking you to the specified report or web page. To link to a report, begin typing a report name. Google Analytics will autocomplete your entry, trying to match it to an existing report. Alternatively, you can copy and paste the report’s URL into this field. Enter a widget title or accept the suggested title. Click save. Add a linked report directly to your dashboard Another way to link a report to your dashboard is to add it directly from the Google Analytics reporting tool. Locate or create the report you want to see in your dashboard. Click add to dashboard below the report title. Select an existing dashboard, or create a new one by clicking new dashboard. Select the check boxes for the dashboard widgets you want to include (e.g., table, pie chart, timeline). You can add up to 2 widgets per report to your dashboard. You can change the widget titles using the click to edit links. Click add to dashboard. Your new linked report widget opens on the dashboard you selected. Use the widget title link to open the underlying report. Linked report limitations Linked reports can’t have metric filters or secondary dimensions. If you try to add a report with a metric filter or secondary dimension, you will see a warning icon. Hover over the icon to see the warning message. You can still add the report, but it will not include the filter or secondary dimension. You can only embed the data view of a report in your dashboard. If you try to add a report that uses another view of the table (e.g., percentage, performance, comparison or pivot), you will see a warning icon. Mouse over the icon to see the warning message. You can still add the report, but it will display only the data view. Linked reports display only the first two metric columns from your reports. If your report contains more than two metrics, additional metrics will not be displayed in the dashboard. Edit a widget To modify an existing widget, mouse over the widget title, then click the edit (pencil) icon. To delete an individual widget, mouse over the widget title, then click the close (X) icon. Clone a widget You can create an exact copy of a widget using the clone widget link. This is convenient when you want to use one widget as a base for another. Add segments to your dashboard In the Google Analytics reports, you can add segments to your dashboard, allowing you to compare and contrast metrics generated by different session or user groupings. To edit an existing segment, click the segment label at the top of your dashboard. To add a segment, click the empty + add segment label. You can learn more about segments. Share your dashboard with other users Dashboards are private to you until you share them. If you develop a dashboard that you think is useful to other users in your account, or to other Google Analytics users in general, you have several options for sharing it. You can also send a snapshot of your dashboard data via email or generate a PDF file you can distribute however you please. Share dashboards with the current view Once you have your private dashboard working the way you like, you can create a copy of it to share with other users. When you do this, anyone with access to this view can see the dashboard’s data and add to or edit any widgets contained in the dashboard. There’s no way to make dashboards read-only; however, changes to the shared dashboard won’t affect your private version of that dashboard. You must have edit permission to share dashboards and data with the current view. If you have only read-only permission, you can still share your private dashboard by sending it as a template link or by adding it to the solutions gallery. See below for more information. To share a dashboard with the current view: View the dashboard you want to share. Click share > share object A copy of the current dashboard will now be available to all other users in that view, located in the shared dashboards section of the reports panel. Note: to unshare the dashboard you must delete it. Share dashboard templates with other views and accounts The share > share template link option generates a URL you can copy and send to other users, embed in a document or host on a website. When you share a dashboard via a template, you share only the settings for the dashboard - you do not share any data. You can send the link to anyone with a Google Analytics account, and that person can then import the settings. Learn more about sharing customizations via templates. Share dashboards in the solutions gallery The solutions gallery lets you share and import custom reporting tools and assets, like dashboards and segments, into your Google Analytics accounts. When you share a dashboard using the share > share in solutions gallery, only the dashboard’s configuration is shared. Your personal information and Google Analytics data stay private in your account. Learn more about the solutions gallery. Send dashboards via email If you need to distribute a snapshot of your dashboard data to people who might not have access to your Google Analytics account, the share > email option is for you. You can send them a customised email with an attached PDF file showing your dashboard to any valid email account. Scheduling dashboard emails Dashboard emails can be sent as “one-offs”, or you can schedule them on a recurring basis. Use the frequency controls to select the timing of the email. By default, recurring emails will be sent for 6 months. The advanced options let you adjust this from 1 month to 1 year. After this period expires, you’ll need to set up the recurring email schedule again. Tip: If there are any previously scheduled emails, you’ll see a link allowing you to add to an existing email. This lets you send out multiple dashboards or reports using the same distribution and timing. Manage all your scheduled emails by navigating to admin > views > select your view > personal tools & assets > scheduled emails. Export dashboards to PDF The export > PDF option saves a copy of your current dashboard exactly as it appears on screen to a PDF file. You can then embed or distribute this exported view in other documents as needed. Get Social! Follow us on LinkedIn, Twitter, and Facebook to keep up-to-date with Google Analytics.   Further reading: Vital Google Analytics custom reports and dashboards for ecommerce Attributing goals and conversions to marketing channels Tips to optimise your ecommerce landing pages

2016-10-04

Unique events metric update in Google Analytics

We're very used to having to explain what the difference is between total and unique events in Google Analytics. Like many other puzzling metrics, this has consistently been a head-scratcher for more people than we can count. Whilst you may have been in this situation like we have, you might not know that this was often due to a problem in the metric! But thanks to the released update, this is now fixed for both internal and clients’ reporting. You can read more about this update in Google's blog post: Improving Google Analytics Events with Unique Events. So what was the issue? The issue was that the unique events metric wasn’t correctly taking into account all event dimensions when calculating your numbers for the reporting. This caused many discrepancies, which probably created those numerous confusing situations around what the number stood for. The unique events metric was always meant to show you the unique count of individual interactions (or events) within a single session. So if someone downloads the same PDF 3 times during 1 session, then that counts as 1 unique event. Instead, it has been counting how many unique numbers of times a specific combination of values is seen in the report per each row. This example by Google is very helpful in getting to grips with what this means. In the example at the top where you can see all three values for various event fields, you get the same count for both new and old unique events metric - each gets 3. In the second example, we exclude one dimension and get a different result. Now, because one set of values is hidden (event label) then the calculation takes into account only what is seen in the report. But the new metric, accurately now, knows that there is a third set of data, which is taken into account in its calculation. What’s the fix? Current unique events metric has been renamed to ‘Unique Dimension Combinations’ (UDC) - a bit of a mouthful - to reflect that it was counting the uniques of dimension combinations, not individual interactions! You’ll still be able to use it as a metric if you need to compare old versus new data or are doing any analysis on the legacy data that it is attached to. The calculation for the unique events metric will now take into account all event dimensions when calculating the number. Due to this change, all event fields are now also required to contain a value - any blanks will get a (not set) value. In your standard Google Analytics reports, you’ll see the new unique events metric with the label ‘NEW’ attached to it. That’s when you know you’ve got the fixed metric in your reports. The new unique events metric will apply to the data as far back as May 2016. BUT, the fix won’t be applied automatically to your custom reports. If you have any custom reports that reference the old unique events metric, the naming will be updated to UDC. Google has provided a neat method to update your custom reports too. So you get a choice whether you want to keep using the deprecated old metric or switch to the updated unique events metric. When making the choice, bear in mind that UDC may eventually be removed so you might want to jump on the fixed uniques metric straight away. Whilst not the most exciting update on its own, this is important for the accuracy of reporting. All of our clients use events tracking so any updates to improving the accuracy of events reporting and analysis are a welcome change. Have any questions about this update? Get in touch with one of our experts! Further reading: What is Google Analytics? Overview for beginners Common reasons for tracking events How to set up event tracking in Google Tag Manager Images: courtesy of Google

2016-09-26

How to set up internal searches in Google Analytics

Learn how to set up site search (internal search) with and without query parameters and see how users search your site. Find what your customers are researching for on your website and improve your website content. The site search reports provide data on the type of content people are looking for on your site. Having site search data is like reading the minds of a subset of your audience. You can easily see what they’re looking for, the words and terminology they are using and how quickly they found what they were looking for (or if they did at all). Site search must be set up for each reporting view in which you want to see user search activity. To set up site search for a view: sign into your analytics account, navigate to a view in which you want to set up site search then click view settings and under site search settings, set site search tracking ON. In the query parameter field, enter the word or words that designate an internal query parameter, such as "term,search,query". Sometimes the word is just a letter, such as "s" or "q". Enter up to five parameters, separated by commas. The simplest way to know what your query parameter is is to go to your site and perform a search for something, anything! On the following page, take a look at the URL – do you see your keyword? If your keyword appears at the end of a URL following a question mark, like this: http://www.yourwebsite.com/?s=your+keyword, this means that your website is using query parameters. If your keyword appears in the middle of the URL, with no query parameters, like this: http://www.yourwebsite.com/search/your-keyword/ then this means you need to use the Page Paths. How to identify search query parameters for Site Search with Queries If you’ve identified that your search keywords show up in the query parameter portion of the site, you’re in luck! This is the easiest way to set up Site Search. When you're searching on your website, you might see the URL like this: http://www.yourwebsite.com/?s=your+keyword, or in this example blog.littledata.io?s=internal+search. The query parameter is the bit between ? and =, which is 's' in this example. So you must use the query parameter ‘s’ when setting up the internal search in Google Analytics settings. Now to set this up in Google Analytics, follow these steps: Select whether or not you want analytics to strip the query parameter from your URL. This only strips the parameters you've provided, not any other parameters in the same URL. Select whether or not you use categories, such as drop-down menus to refine a site search. If you select 'no', you are finished. Click save changes. If you select 'yes': In the category parameter field, enter the letters that designate an internal query category such as 'cat, qc,'. Select whether or not you want analytics to strip the category parameters from your URL. Note that this only strips the parameters you provided, not any other parameters in the same URL. This has the same functionality as excluding the URL query parameters in your main view: if you strip the category parameters from your site search view, you don't have to exclude them again from your main view. Click apply How to set search terms for Page Path Search Terms (No Queries) Another common behaviour of site search is to have the terms appear within the page path instead of a query. Like this: http://www.yourwebsite.com/search/your-keyword/ To track this type of site search, an advanced filter should be used for views that will be using these reports. First, navigate to filters > new filter under your view. (Note: when adding a filter, you must have EDIT rights on the property level!) After choosing the filter name, select ‘custom’ and ‘advanced’ in the filter’s settings. Choose ‘request URI’ for field A since we are getting the information from the URI, or page path. Your site’s page path goes in the text box, so for this example, it would look like this: search/(.*). When we do this, we are telling Google Analytics to look at this page path and extract the characters from within the parentheses. The dot and asterisk are regular expressions representing any character and any number of characters - so we are storing anything after the slash. Field B will be blank since we are only concerned with extracting from the page path and nowhere else. The next field, ‘output yo’, is the one we are interested in. Now that we have stored the keyword from the URI, we need to output it to the correct dimension. In the drop-down menu, select ‘search term’ and type ‘$A1’ into the input box. This tells Google Analytics to grab the user-defined value from field A and output it as a search term. For the checkbox options below, only ‘field A required’ and ‘override output field’ need to be selected. See site search data To see the site search reports: sign into your analytics account, navigate to your desired account, property, and view, then select the reporting tab and under behaviour go to site search. Your report must look like this: Take into consideration that the report will be populated with data from the moment you activate the internal search or add the filter. It is not retroactive and may need 24h to you see the queries in your report. If you'd like to know more about how to set up internal searches in Google Analytics, get in touch with one of our experts! Further reading: Attributing goals and conversions to marketing channels 9 tips for marketers using Google Analytics Trust your Google Analytics data with correct setup Image credit: Image courtesy of hub.3dissue.net  

2016-09-22

What is Google Analytics? Overview for beginners

In this day and age, I find it hard to imagine how an online business is able to ensure its competitiveness without seeing how they're performing digitally. How will you know whether you need to re-design your basket or product page without seeing how these pages are performing? Or which social networks are best at driving your sales? That's why you need analytics tracking. You have a choice between multiple platforms, like Google Analytics, Kissmetrics, Mixpanel, and Heap, each with its strengths and weaknesses. Depending on what you are trying to achieve, a choice of two platforms might be most suitable, and we are able to help if needed. We have done a number of audit sessions with clients where we talked through their requirements and what they wanted to achieve and then suggested which platform or platforms are most suitable for them. Get in touch if you need help determining what is most suitable for driving your business growth! If you're looking for more information about Google Analytics, have already chosen it as your analytics platform and feel lost, then read on and check out the video below. It is the most common analytics tool used worldwide, with an estimated 30 - 50 million websites having installed the tracker (Source: Marketing Land). It is an undisputed leader in website performance tracking and because it is so commonly used, it is much easier to find support and services for it. Not to mention a plethora of plugins, widgets, guides and resources online! Google Analytics helps you find what people are doing on your site or mobile app, which marketing campaigns are driving your traffic, and how people are converting into engaged users or paying customers. You get a number of standard reports by default, but reports around acquisition and conversions, which require additional setup, unlock the true power of Google Analytics. In the video below you'll find out what Google Analytics reporting looks like, what it can do for you and explain some key reporting features by showing you: what a standard report looks like typical reports to look at conversion goals and how to set them up attribution Have a look! Now that you've seen what Google Analytics is about, you should have a play with a fully-implemented Google Analytics demo account. It's a great way to get a practical experience with their various reports, and what you can achieve if you get expert help to set up the advanced reports. Check out the demo account and get in touch with our team if you want to unlock the true power of Google Analytics reporting.   Further reading: Attributing goals and conversions to marketing channels 9 tips for marketers using Google Analytics Trust your Google Analytics data with correct setup Image credit: Image courtesy of hub.3dissue.net

2016-09-19

An (updated) guide to reporting in Littledata's web app

Littledata’s web app gives you simple and actionable insights into your website's performance. Our app scours through hundreds of Google Analytics metrics and trends, in order to give you summarised reports, alerts on significant changes, custom reports and benchmarks against competitor sites.       This guide will give you the ins-and-outs of how we generate those important reports that help you make decisions in driving your business. Here’s a glimpse of what’s below: Free core reporting for unlimited users How to authorise access to your Google Analytics data Picking the right Google Analytics view for reporting Our range of reporting features, including custom reports and industry benchmarks How to check significant changes and page trends Long-term tracking and reporting You’ve signed up - what are the benefits? So you’ve now signed up and you’re ready to get started… but what are the benefits of signing up? Well… You’re getting automated reporting, meaning our web app looks through all of your Google Analytics reports to find significant changes. There are over 100 of them, so it will save you a lot of time not having to look through these manually. We split these findings across 5 different sections so you know quickly what you can find under each. We help you keep your data clean by looking for spam referrals. This has been a common problem for a while and a fix can be complex to set up. So we’ve created a feature that does it automatically for you (or you just need to approve it when you see it). If you get new spam referrals, we’ll spot these and let you know again. We’re also benchmarking your site against other websites, so you know where you have a competitive advantage and where you don’t. On top of all these goodies, there’s no installation needed so you get access to our web app right away! We recently updated a few important aspects of our app and you can read all about them in our blog post: New in Littledata: an improved navigation, trend detection algorithm, and more. Accessing your Google Analytics data As a Google Analytics user, you will already be sending data to Google every time someone interacts with your website or app. Google Analytics provides an API, where our app can query this underlying data and provide you with summary reports. During the signup, you would have seen an authorisation window, asking for permission to view your Google Analytics data. This means you granted us READ access. Be assured, that we will not be able to change any data or settings in your Google Analytics. Your data is viewed only by the algorithms in the web app. You pick which Google Analytics view to report on Once you’ve authorised the access, you will select the Google Analytics view that you want to set up reporting for. Some companies will have multiple views set up for a particular website. They might have subtly different data – for example, one excludes traffic from company offices or focuses on the blog traffic only – so pick the most appropriate one. If you’ve made a mistake in choosing your view or want to set up another one, don’t worry, you can always do it by clicking on the existing view in the top-right corner and selecting the option ‘set up another site’ from the drop-down menu! During the initial signup, we ask for an email where you want to get your alerts. This is because a lot of people don’t necessarily use the same email address to access Google Analytics and check their emails. Don’t worry… we’re not going to spam you, we just want to make sure you don’t miss any of your reports! When you get to the reports list, you might see something like this: Now, don’t be sad if you’re not seeing anything quite yet - we’re still checking and will only let you know when there’s something interesting to check. Just be sure to check back or wait for an email alert from your talented expert! Google Analytics Audit The first thing that we are going to analyse is your setup, in order to see what is working on your website and what is not working properly. There are more than 10 Google Analytics checks that we are verifying. The audit is almost instant and it will give you an idea about what is happening throughout your website. For each correct check, there will be provided a brief description and the dates when it was verified, whereas for the wrong checks there will be a guide on how to fix that issue and also the dates when it was found as faulty setup. Some of the checks include aspects about demographics tracking, excluding spammers, checkout steps, visitors' anonymity, campaign tagging on social and email, exclusion of company traffic, if conversion goals are set up and many other. If you ever have doubts regarding what to do or where to check, you can book a free 30 minutes consultation with our experts. They will offer guidance to set up your account in order to have accurate reporting. Dashboard If you are tired of getting complicated graphs and endless tables, our dashboard will be exactly what you need. This feature will present a clear picture of your online business performance through graphs and stats reporting the most important metrics for your website. Don't forget to set the metrics that best apply to your business from the settings page (see below more details). One of the advantages of the smart dashboard is the ability to compare the current day/week/month with a previous date range. This feature will allow you to contrast the metrics that matter to you in order to target adds or marketing campaigns in periods that generate profit for you. Custom reports Our consultants can create for you easy-to-understand custom reports that reflect the traffic or transactions from your website. The numbers are transformed into tables, pie-charts and graphs that can be interpreted by anyone in your team.   We created some general custom reports - conversion rate by channel or by device, changes in landing page value, product category revenue and purchases by blog post. You can choose one of those custom reports or have us create something entirely new for you. Either way, we review every new custom report by hand to ensure proper setup and accurate data. Free users get one basic custom report with setup by our team of experts. Pro users can take advantage of all custom report templates and even work with our consultants to create something new. Accuracy guaranteed – link to prices. Benchmarks Here, you’ll find the performance of your web analytics compared with aggregated data from other companies. You will be able to compare your web performance, conversion rate, bounce rates and more to a benchmark, which is created by analysing more than 3000 other websites. The data is gathered anonymously from Google Analytics to give you insight into how your digital product or online marketing is performing. For example, you can find out how you compare (whether above or below median) to other websites and adjust your campaigns in order to generate growth. Alerts    This report shows you trends in your data and includes in-app alerts. It will highlight significant changes, giving you details into what they mean and what to investigate. You can always customise the notifications that we will send via email from the settings section. Get the most out of your reporting - adjust your settings We’ve got a few important sections in your settings that we’d like to highlight, to make sure your reporting runs smoothly! First, adjust your revenue settings based on your website’s income generation. This will allow you to receive accurate alerts about how changes in your traffic affected your income. This will be done automatically if you have an enhanced ecommerce setup in your Google Analytics account. Second, you have your metrics and your segments, where you can select which reports you want to see based on the standard, predefined metrics or segments in Google Analytics. This will help you define your goals and see the relevant reports necessary to increase performance. Lastly, you notifications settings, where you can set up  email alert frequency or recipients. In this way to can always be informed and also be able to share with your team the important alerts regarding changes in your website. You always have the option of adjusting this at any time. But, we recommend you get this setup, as this will allow you to get the most relevant reports for your company. You can find this section by pressing the settings icon in the top-right corner of your screen, and then clicking report preferences. Every day we look for significant changes and trending pages There are over 100 Google Analytics reports and our clever algorithm scans through all of them, finding the most interesting changes to highlight. We recently improved that algorithm, and luckily for you, you can read all about how we made the detection of significant trends in your traffic easier to see. It’s been live since August, giving you fewer distractions and more significant alerts tailored to your company’s goals. Every morning (around 4am local time) our app fetches your traffic data from the previous period – broken down into relevant segments, like mobile traffic from organic search – and compares it against a pattern from the previous day, week, or month depending on the type of report. This isn’t just signalling whether a metric has changed – web traffic is unpredictable and changes every day (scientists call this ‘noise’). We are looking for how likely a specific value was out of line with the recent pattern. We are selective about the reports you see in the interface so we’ve set up the algorithm to find changes in trends in which we are 95% sure of the importance of the change. But to adjust which changes you actually get alerted to, you can change the significance to be much more limiting, like 98% or 99%, so that you get email alerts only in those cases We also use smiley faces to help you see quickly which changes are good or bad. If you’re particularly interested in “bad” things happening in your traffic to address potential issues, then you should look out for red sad faces to help you pinpoint these reports on the list. We email the most significant changes to you Every day – but only if you have significant changes – we generate a summary email, with the highest priority reports you should look at. An example change might be that 'the bounce rate from natural search traffic is down by 8% yesterday’ or 'the worst performing mobile device resulted in 59 fewer engaged visits'. If you usually get a consistent bounce rate for natural / organic search traffic, and one day that changes, then you should investigate why. Need to change your email settings? You can always adjust the frequency or add more colleagues so they can stay on top of the changes. If the reports you get are not the ones you need, based on your goals, remember you can always adjust your settings! Every Sunday or first of the month, we look for changes Every week (Sunday) or month (first of the month) we look for long-term trends – which are only visible when comparing the last week with the previous week. You should get more alerts on a Sunday. If you have a site with under 10,000 visits a month, you are likely to see more changes week-by-week then day-by-day. Already signed up? Login and check the setup of your reports. Need help with the process or have any questions? We’re always available to help, whether you need help with existing reports, need help finding the best reports for your company, are interested in the reports we’re currently working on and/or want us to provide feedback. Feel free to contact one of our experts or ask them in the web app. We hope you enjoy the web app and all of the wonderful reports and insights included! Happy analysing! This blog post was last updated in June 2017.  Further reading: New in Littledata: an improved navigation, trend detection algorithm, and more Making the detection of significant trends in your traffic easier to see 9 tips for marketers using Google Analytics

2016-09-15

New in Littledata: an improved navigation, trend detection algorithm, and more

We’ve got some exciting news! We’ve launched some great updates on our web app, which will make your lives a little easier. Find out how the navigation has improved and new in-app messaging will help you find out more, get a glimpse into our trend detection algorithm and new reports on mobile devices! Our mission is to make the way you gain access to important analytics, an all-around easier process and we know we’re heading in the right direction with these updates. We already give you actionable and easier to understand insights of your Google Analytics and now we’ve made the experience more friendly based on your invaluable feedback! Find your reports quicker We’ve improved the navigation of the web app, giving you one new category, and two updated categories on the left-hand side of your profile, which are now simpler to find and easier to understand. There are currently three categories: Dashboard, Benchmark, and Reports, which will be visible to you depending on your Littledata package. Instead of having them in separate locations, we brought them together into one navigation panel so that you can find specific reports and findings quickly based on your current questions or company needs. Under the reports category, we have changed types of reports into tags. Now you can select one or multiple tags, and decide how you prefer to view the different types of insights you get. For example, if you want to view your trends reports with tips you’re getting, then all you need to do is select those two. The benchmark category brings together all the benchmark metrics available for your site, and to see more detail click on the individual benchmark you’re interested in. You can still see the category you are being benchmarked against just above your benchmarks. If your current category is ‘all websites’ then you should make this more specific by updating the category in the settings. The Dashboard is the latest addition to these categories, which we added to be able to provide a flexible and customised solution that is perfect for reporting needs that go beyond standard Google Analytics reports. See below for more detail. Get our custom dashboard This is a new feature, available to clients who are also receiving consulting services on top of our Pro package. Please contact one of our lovely experts if you’d like to know more about these features, and how they can give you the results you strive for. The dashboard category is completely customisable, which we develop through consulting services by going over what your goals and needs are, and then creating these reports for simple and actionable insights of your data. These reports are completely flexible and allow you to see metrics that are difficult to view in Google Analytics, which include: Calculations, such as performance changes in percentages and conversion rates Combined metrics and dimensions from different reports Custom visualisations of trends based on how you prefer to see the data. Want to include a pie or bar chart? Not a problem. A custom schedule for dashboard data refresh. If your reporting requires weekly, quarterly or annual updates, we’ll set it up for you. Customised reports based on your formatting preferences, so if you'd like to include your brand colours, it's a possibility! Our smarter algorithm When we started Littledata, we developed a trend detection algorithm to find significant changes in your data and send you alerts, reducing the time spent wading through data in Google Analytics. But as times change and data gets busier, we needed a better way to serve your reporting needs. So recently we collaborated with mathematicians to improve the algorithm, which is now sensitive enough to pick up small changes in low traffic website, but also specific enough to ignore the random noise of daily traffic. Want to hear more about this intriguing story? Find out more in our blog post: Making the detection of significant trends in your traffic easier to see! Are mobile devices losing you customers? Analytics from mobile devices is extremely important. Through our web app, you will find out how many transaction or users you lost due to poor experience on mobile devices. According to Dave Chaffey at Smart Insights, 80% of internet users own a smartphone. A growing number of people are searching through their phones and as a result, we’ve incorporated mobile devices reports. They will spot and highlight potential issues around responsiveness, layout or bugs. Finding out which devices are the worst will allow you to optimise your website and campaigns to capture all of these individuals. Your personalised communication We completely agree with Intercom’s belief that “customers today want to communicate with the people behind the business, not with a faceless brand”! This is why we’ve integrated their messenger into our web app so that you can chat with us directly and quickly. There’s a great deal of custom features available, including formatting, delivery, and most importantly the different ways to respond. You can choose your own way to chat and react, with images, audio, emojis, video, and more. If you want to know more about the expert you’re talking to, you can view their profile within the app. Our customer experience is key in our business model and we hope this function delivers that. If you have any questions regarding any of the new features, please contact us, or use the in-app messenger!   Image credit: Image courtesy of Smart Insights and Intercom

2016-09-06

Vital Google Analytics custom reports and dashboards for ecommerce

Standard reports are useful to an extent. Custom reports and dashboards, on the other hand, allow you to compile metrics that give you much more useful insights of how your online shop is performing. Monitoring and reviewing the right data is essential for deciding which tactics or initiatives you should try, or marketing platforms to focus on, to help you sell more. If you are very familiar with how Google Analytics (GA) works, then you would set up some custom reports and dashboards to quickly access your key metrics. But if you are not as knowledgeable about the quirks and inner workings of GA then you should take advantage of the many custom reports and dashboards available for import. We can also help you build custom dashboards. There is a huge number of reports available in Google Analytics Solutions Gallery; used, created and shared by experts. They’re all done from scratch and designed to maximise your use of Google Analytics, but the huge amount of solutions from dashboards and channel groupings to segments and custom reports do require some time to find what’s right for your needs. From our experience setting up ecommerce tracking and reports for companies like MADE.com, British Red Cross Training, Pensions and Lifetime Savings Association, these reports and dashboards are valuable when analysing purchase data. Don't lose sight of your conversion rate Keep an eye on your ecommerce conversion rate across five different tabs covering channels, keyword, mobile devices, cities and campaigns. Focussed on high traffic sources, each section shows where it's not up to scratch and needs your attention and tweaking. Get ecommerce conversion rate performance custom report. Find duplicate transactions Duplicate transactions can greatly skew your numbers and affect your reporting, making you doubt the accuracy of your data. Duplicate order data is sent to Google Analytics typically because the page containing such information has been loaded twice. This can happen when the page is refreshed or loaded again. To find whether your data contains duplicate transactions, add our custom report to the view you want to check. Get a custom report to check for duplicate transactions. If you have more than 1 transaction in any row (or per an individual transaction ID), that means you have duplicate transactions stored in your data. It’s worth checking the report on a regular basis, eg monthly, to make sure that there are no duplicates or they’re kept to the minimum. Lunametrics blog has a number of suggestions for how to fix duplicate transactions. Overview of ecommerce performance This overview dashboard brings important top level metrics into one place, so you don’t have to go searching for them in multiple reports. You will quickly see which of your campaigns, channels, and sources are bringing in the most revenue, whilst comparing conversion rates across each. Get ecommerce overview dashboard. How is your store content performing See how your customers are engaging with your site, content and product (or page, depending on the setup) categories. You'll get information on what they search for, and which categories and landing pages bring in the most revenue. Get ecommerce content performance dashboard.   Looking for improving your ecommerce tracking and reporting? Get in touch with our qualified experts.   Further reading: Take your ecommerce website to the next level Attributing goals and conversions to marketing channels Tips to optimise your ecommerce landing pages Image credit: Image courtesy of Juralmin at Pixabay

2016-09-05

Setting up common email software for Google Analytics

Many of the popular email providers make it easy to automatically tag up links in your emails to allow Google Analytics to track them under the 'Email' channel. Without this, the traffic from email links will be dispersed under 'Direct' and 'Referral' channels, and you won't be able to see which emails really drive engagement or sales. Here are the links to set up some common email services: MailChimp Campaign Monitor ActiveCampaign Benchmark Email ConstantContact iContact Emma MadMimi GetResponse Mail Jet If your email provider is not in the list, or you send emails from your own platform, you'll need to manually paste in tagged up email links. Still need some help? Contact us and we'll be happy to answer any questions!

2016-08-24

Why do I need ecommerce tracking?

Only by using Google Analytics ecommerce tracking, can you match real sales data with website usage (including traffic source/medium). This sales analysis is required to understand the performance of your website landing pages and return-on-investment from marketing campaigns. The ecommerce reports allow you to analyse purchase activity on your site or app. You can see which products were bought, average order value, ecommerce conversion rate, time to purchase, discount vouchers used and checkout process funnels. Ecommerce tracking is useful not just for online shops but for all kinds of websites including event booking, courses / education, travel / hotels and so on. To see ecommerce data in Google Analytics, you need to: Enable ecommerce in Google Analytics Add the code to your site/app to collect ecommerce data. To complete this task, you'll need to be comfortable editing HTML and coding in JavaScript, or have help from an experienced web developer. Read how to Set up Ecommerce Tracking with Google Tag Manager. Based on this data, you can develop an understanding of: Which products sell well, and by inference, which products are best suited for your customer base. The revenue per transaction, and the number of products per transaction. For example, if the number of products per transaction is lower than you'd like, you might benefit from offering better quantity discounts, or offering free shipping if customers meet a minimum dollar amount. How long (in time and in the number of sessions) it takes customers to make the decision to purchase. If your sales cycle is stable or fluctuates predictably based on product or season, you can use this information (in conjunction with overall sales forecasts) to make reliable predictions about revenue. If customers routinely make numerous visits before they purchase, you might think about a site design that leads more easily to your purchase pages, or options that let users compare your products and prices to your competitors'. The difference between goals and ecommerce. A goal is only measured once in a visit. Think about it similar to pageviews vs. unique pageviews - once the goal has been 'triggered' to a visit, it can't be triggered again. On the other hand, there are no limitations on the number of transactions being measured during one session. Ecommerce is more powerful in that it allows you to analyse additional metrics.  For example, you can see how many visits occurred before the visitor decided to purchase. Many visitors on my site come back more than 7 times before they finally decide to purchase. Wow, interesting figures! Here is a list of the available metrics for ecommerce: If you have marketing campaigns and have no ecommerce tracking you are more likely struggling to calculate the return on your investment (ROI).  With both goals and ecommerce tracking, you will now have a full understanding of your customer journey and your customer life value (CLV). Analytics goals vs. ecommerce transactions, which to choose? Both of them!  If you have read my post carefully, you will understand that both of them have their strengths and limitations. We strongly advise to implement and configure goals and ecommerce. Need help configuring goals and/or ecommerce on Google Analytics? Get in touch with our experts!  

2016-08-09

Do I need the Google Analytics tracking code on every page?

The script which triggers the tracking events to Google must be loaded once (and only once) on every page of your site. To set up Google Analytics tracking, you’ll usually need either your Analytics tracking ID or the entire Javascript tracking code snippet. This corresponds to your Analytics property. To find the tracking ID and code snippet: Sign in to your Analytics account. Select the Admin tab. Select an account from the drop-down menu in the ACCOUNT column. Select a property from the drop-down menu in the PROPERTY column. Under PROPERTY, click Tracking Info > Tracking Code. The snippet provided here must be implemented on every page, even the pages you are not interested in. If you chose to not include the code on every page then: you will not be able to see the full flow of a client on your website you will have inaccurate data about the time spent on site and actions taken visits to untracked pages will appear as 'referrals' and so will skew the volume of sessions marketing campaigns to the untracked pages will be lost The easy way for an established website to see if the tracking is complete is to go in Google Analytics -> Acquisition -> Referrals and search in the report after the name of your website, as shown below, or you can use Littledata's audit tool. Choose how to set up tracking There are several ways to collect data in Analytics, depending on whether you want to track a website, an app, or other Internet-connected devices. Select the best installation method for what you wish to track. Here is the complete guide from Google. Once you have successfully installed Analytics tracking, it may take up to 24 hours for data such as traffic referral information, user characteristics, and browsing information to appear in your reports. However, you can check your web tracking code setup immediately. If you don’t think it's working correctly Check your Real-Time reports or use Use Google Tag Assistant to verify your setup.   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-08-04

Attributing goals and conversions to marketing channels

On most websites, the conversion journey involves many different routes and across many sessions: few customers buy from the first advert. You may have heard of the ‘rule of 7’. In reality, it varies from maybe 2 or 3 touches for a $20 purchase and definitely more than 10 for an enterprise business service. Your company is buying prospects (or traffic) from a number of online channels, and in many cases, it will be the same potential customer coming from different sources. To be able to report on this in Google Analytics, we need to get the basic setup correct. Tagging campaigns for attribution The first step is to make sure that the different traffic sources can be compared in a multi-channel report are consistent and have complete inbound link tagging. Be sure to tag your campaign correct with our URL Builder. Some tools (such as Bing or Mailchimp) have options to turn on link tagging for GA - although it's buried in the settings. With many others, you will have to add the necessary ‘UTM’ parameters onto the link. Without this tagging, many sources will be misattributed. For example, affiliate networks could send referrals from any of thousands of websites which will appear under the ‘referrals’ channel by default. Facebook ads, since the majority come from the Facebook’s app, will appear under the ‘direct’ (or ‘unknown’) channel. From when full tagging is in effect, the channels report will start to reflect your genuine traffic acquisition source. But don’t expect a 100% match with other tracking tools – see our article on Facebook – GA discrepancies. Importing cost data The cost for any Google AdWords campaigns can be imported automatically, by linking the accounts, but for any third party campaigns, you will need to upload a spreadsheet with your costs on. The benefit is that now you can see the return on investment calculation update in real-time in the multi-channel reports. Model attribution The final step is to decide how you will attribute the value of a campaign if it forms part of a longer conversion pathway. The default for Google Analytics (and most others) is ‘last non-direct click’. That means that the most recent TAGGED campaign gets all the credit for the sale. If the user clicks on 5 Facebook ads, and then eventually buys after an abandoned basket email reminder, that email reminder will get all the sales (not Facebook). This attribution is what you’ll see in all the standard campaign and acquisition reports. You may feel that it is unfair on all the work done by the earlier campaigns, so ‘linear’ (sale equally credited to all tagged campaigns) or ‘time decay’ (more recent campaigns get more credit) may be a better fit with your businesses’ goals. Conclusion Multi-channel marketing performance attribution is not a luxury for the largest companies. It’s available to you now, with the free version of Google Analytics. It will require some setup effort to get meaningful reports (as with any measurement tool) but it has the power to transform how you allocate budget across a range of online marketing platforms. But if this still is not working for you then you may have a problem with cross domain tracking. Need a bit more advice or have any questions? Get in touch with our experts or leave a comment below!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-08-04

Personally Identifiable Information (PII), hashing and Google Analytics

Google has a strict policy prohibiting sending Personally Identifiable Information (PII) to Google Analytics. This is necessary to provide GA reports around the world, yet comply with country regulations about storing personal information.  Even if you send personal information accidentally, Google may be forced to delete all of your analytics data for the time range affected. This policy has recently tightened to state: You may not upload any data that allows Google to personally identify an individual (such as names and email addresses), even in hashed form. A number of our clients are using a hashed email as the unique identifier for logged in users, or those coming from email campaigns.  If so, this needs be a minimum of SHA256 hashing (not MD5 hashing), with a 'salt' to improve the security - check your implementation meets the required standard. If you want to check if personal information affects your analytics, we now include checking for PII in our complete Google Analytics audit. Google's best practice for avoiding this issue is to remove the PII at the source - on the page, before it is sent to Google Analytics.  But it may be hard to hunt down all the situations where you accidentally send personal data; for example, a form which sends the user's email in the postback URL, or a marketing campaign which add the postcode as a campaign tag. We have developed a tag manager variable that does this removal for you, to avoid having to change any forms or marketing campaigns which are currency breaking the rules. Steps to setup 1. Copy the script below into a new custom Javascript variable in GTM [code language="javascript"]function() { // Modify the object below to add additional regular expressions var piiRegex = { //matches emails, postcodes and phone numbers where they start or end with a space //or a comma, ampersand, backslash or equals "email": /[\s&amp;\/,=]([a-zA-Z0-9_.+-]+\@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+)($|[\s&amp;\/,])/, "postcode": /[\s&amp;\/,=]([A-Z]{1,2}[0-9][0-9A-Z]?(\s|%20)[0-9][A-Z]{2})($|[\s&amp;\/,])/, "phone number": /[\s&amp;\/,=](0[0-9]{3,5}(\s|%20)?[0-9]{5,8}|[0-9]{3}-[0-9]{4}-[0-9]{4})($|[\s&amp;\/,])/ }; // Ensure that {{Page URL}} is updated to match the Variable in your // GTM container to retrieve the full URL var dl = {{Page URL}} var dlRemoved = dl; for (key in piiRegex) { dlRemoved = dlRemoved.replace(piiRegex[key], 'REMOVED'); } return dlRemoved; }[/code]   2.Check {{Page URL}} is set up in your GTM container This is a built-in variable, but you'll need to check it under the variables tab.   3. Change the pageview tag to override the standard document location, and use the variable with PII removed   By default, Google Analytics takes the location to be whatever is in the URL bar (document.location in Javascript).  You will over-ride that with the PII-safe variable.  

2016-08-03

Why do you need cross-domain tracking?

What is cross-domain tracking and why do you need to implement in your Google Analytics account? Cross-domain tracking makes it possible for Analytics to see sessions on two related sites (such as an ecommerce site and a separate shopping cart site) as a single session. This is sometimes called site linking. Cross-domain literally means that you are able to see a user in a single Google Analytics account in his journey across multiple domains that you control (e.g. mysite.com and myshoppingcart.com). In the standard configuration of the Google Analytics script, every time a customer loads a page on a different domain a new session is generated, even if the branding looks seamless to the user and, unfortunately, the previous session has ended and this is even if the customer is still active and generates events and page views. Until you have implemented the cross-domain setting on your website you will not be able to have an accurate customer journey. Why? Let’s take, for example, a standard website, www.siteA.com, and it's blog, www.blogB.com. To track sessions, Analytics collects a client ID value in every hit. Client ID values are stored in 1st party cookies, and these cookies are only available to web pages on the same domain. When tracking sessions across multiple domains, the client ID value has to be transferred from one domain to the other. To do this, the Analytics tracking code has linking features that allow the source domain to place the client ID in the link URL, where the destination domain can access it. Fortunately, with the release of Universal Analytics cross-domain tracking, it is easier to implement, and especially so with Google Tag Manager. Setting up cross-domain tracking using Google Tag Manager Add (or edit your existing) a basic page tracking tag (i.e. Tag Type = Universal Analytics; Track Type = Page View). If you are using the same container for siteA.com and blogB.com, under More Settings → Fields to Set, enter the following: Field Name: allowLinker Value: true Under More settings → Cross-Domain Tracking → Auto Link Domains enter "blogB.com" (without the quotes). If you have multiple domains, separate them by commas: blogB.com, siteC.com Leave the 'Use hash as delimiter' and 'Decorate forms' unless you have an unusual web setup. Set the trigger to "All Pages". Save a version of the container and publish it. If you are using a separate container for blogB.com, repeat the steps above but in the Auto Link Domains field add: siteA.com Add both domains to the Referral Exclusion List When a user journey crosses from your first domain to your second domain, it will still appear as a new session in Google Analytics by default. If you want to be able to track a single session across multiple domains, you need to add your domains to the referral exclusion list. Here’s an example Tag Assistant Recordings report that shows what it looks like when cross-domain tracking is not setup properly. Setting up cross-domain tracking by directly modifying the tracking code To set up cross-domain tracking for multiple top-level domains, you need to modify the Google Analytics tracking code on each domain. You should have basic knowledge of HTML and JavaScript or work with a developer to set up cross-domain tracking. The examples in this article use the Universal Analytics tracking code snippet (analytics.js). Editing the tracking code for the primary domain ga('create', 'UA-XXXXXXX-Y', 'auto', {'allowLinker': true}); ga('require', 'linker'); ga('linker:autoLink', ['siteB.com'] ); Remember to replace the example tracking ID (UA-XXXXXX-Y) with your own tracking ID, and replace the example autoLink domain (siteB.com) with your own secondary domain name. Editing the tracking code on the secondary domain ga('create', 'UA-XXXXXXX-Y', 'auto', {'allowLinker': true}); ga('require', 'linker'); ga('linker:autoLink', ['siteA.com'] ); Remember to replace the example tracking ID (UA-XXXXXX-Y) with your own tracking ID, and replace the example autoLink domain (siteA.com) with your own primary domain name. Adding the domain to page URLs using filters By default, Google Analytics only includes the page path and page title in page reports - not the domains name. For example, you might see one page appear in the Site Content report like this: /contactUs.html Because the domain names aren’t listed, it might be hard to tell whether this is www.siteA.com/contactUs.html or www.blogB.com/contactUs.html. To get the domain names to appear in your reports you need to do two things: Create a copy of your reporting view that includes data from all your domains in it Add an advanced filter to that new view. The filter will tell Google Analytics to display domain names in your reports. Follow this example to set up a view filter that displays domain names in your reports when you have cross-domain tracking set up. For some fields, you need to select an item from the dropdown menu. For others, you need to input the characters here: Filter Type: Custom filter > Advanced Field A: Hostname Extract A: (.*) Field B: Request URI Extract: (.*) Output To: Request URI Constructor: $A1$B1 Click Save to create the filter. You can validate that filters are working as you expect using Google Tag Assistant Recordings. Tag Assistant Recordings can show you exactly how your filters change your traffic.   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-08-02

Tips to optimise your ecommerce landing pages

Are your ecommerce landing pages suffering from poor conversion rate because people aren't engaging? First impressions are everything, and more so online, so your task is to figure out which on-site improvements will help you towards your goals. Once you start optimising, it's a continuous process of reviewing, changing, testing and refining - aiming to find out what is most appealing to your customers, what they like and care about, what makes them trust you, what encourages them to purchase. There is always room for refinements so here are some tips on what you should consider when reviewing your pages. What are you trying to achieve? Before starting testing and implementing the changes on your landing pages, you have to be clear about what you want to accomplish. Whilst the end goal for an online store is to increase sales, at times you might also want to get more sign ups, or improve views of or engagement with product pages. Think about what success will look like as that will help with planning your optimisation tests. How are you going to measure it? If you are clear about what you are trying to achieve, it will be easier to set measurable targets. Are you looking to increase your sales by 10% or pageviews of products by 15%? Or maybe you want your potential customers to browse further and spend more time reading content? Further engagement can also be demonstrated by the site visitor scrolling down the page if you have long product or category pages. In which case you'll want to track how far down the page they get to. I believe in keeping reporting straightforward so when testing focus on tracking important metrics only. Ideally just one if you can, or a few if you have to, but that will help focus on measuring what is most important for your business at the time. Assuming you are using Google Analytics, like most of people looking after digital performance, set up goals to monitor how customers are converting. Our web-based software also makes it easy to keep track of on-site changes are by reporting on changes in trends, goals, pages. Who are you targeting? User-focussed content is more effective at engaging your customers and improving your conversion rates. So you should write up your customer personas to be clear about who you are targeting with landing pages. This also applies to general look and feel of your ecommerce site. Most importantly, include with personas what problems your customers are trying to solve or what they are trying to achieve.  Once your team knows who your ideal or typical customers are, then it will be easier to focus on creating more relevant and engaging content on those pages. Do you have a clear value proposition? Value proposition explains why you’re better than or different from your competitors, and what you can deliver that they can’t. When writing it up, focus on benefits not features. It’s not always about the product looking top notch (unless you’re the industry or company where that matters of course) so it is more about how you can alleviate their problem. Check out how to write your value proposition by following Geoffrey Moore’s model. Does your copy reflect your value proposition? Once you have your customer personas and value proposition, review existing content on the site against how you describe what your clients are looking for. Check if it fits with what they are looking for, explains how you can solve their problems or fulfill their desires. The copy on your site has to reflect how you can improve your potential customers lives through what you offer. A great copy informs, compels, captivates, reflects what people search for and promotes key benefits. Econsultancy have compiled a great set of advice from experts on writing copy for product pages. Also, check out Copyblogger Demian Farnworth’s articles for superb advice on writing copy. Have you found your winning call to action? This is very important – test your call to action until you find the best performing one. Your call to action is like a visual sign that guides the buyer towards a specific action you want them to complete. Different things work for different sites. Start off with trying simple changes like different text, colour, shape, size or placement of the button to figure out what is most effective for your page. If small changes aren’t helping, then try a more drastic change of the button or page. Do your pages load fast? This is pretty self-explanatory. Slow page loading speed might drive your potential customers away from your online shop, so you should regularly check whether they can view your products within 3 seconds (Source: Radware). If you’re using Google Analytics, you can use Site Speed reports to check how you’re performing and get advice on where to improve. If you don’t have Google Analytics, you can use their online tool PageSpeed Insights. Other tool worth checking out is GTMetrix where you can grade your site's speed performance and get a list of recommendations. Do you need to optimise for mobile? It’s a very common fact that more and more people are using mobile devices to browse and buy online. But unless you have unlimited budget for ensuring that your ecommerce site is optimised for mobile, it is best to check in Google Analytics first whether you need to do it now. If you go to Google Analytics > Audience > Mobile > Overview report, you will get a breakdown of device categories that buyers are using to visit your online store. Here you can see that the majority of customers, almost 93% are using desktop so in this case (assuming you have a limited budget) you might want to make sure you have a responsive site at the very minimum, and leave a full optimisation for mobile device for later when there is a sufficient need. Now, if results were different and let’s say you had 60% of people visiting your site via mobile devices, then you would want to ensure that they’re getting the best experience on their device and don’t leave the site to buy from a competitor instead. Are your test results statistically significant? Evaluating your AB test results isn't quite as simple as looking at the highest conversion rate for each test, which would be an incorrect way to interpret the outcome. You want to be confident that results are conclusive and changes you tested will indeed improve your conversion rates (or not, depending on the outcome of testing). That's where statistical significance comes in. It gives you assurance about the results of your tests whilst taking into consideration your sample size and how confident you want to be about the importance of the results. By reaching over 95% statistical confidence in testing results, you can be sure that the winning variation performed better due to actually being an improved version, and not simply due to change. You can easily find a calculator online that tells you if your AB testing results were statistically significant and you should conclude the test or not - for example, try the calculator by Kissmetrics or Peakconversion. There is no one winning formula for how to make your pages more effective, but you have to be pro-active to figure out what they are  - so keep testing until you do. Have any questions? Leave a comment below or get in touch with our experts!   Image Credit: Stocksnap.io

2016-07-27

9 tips for marketers using Google Analytics

Setting up Google Analytics to collect data on your website visitors’ behaviour is step one. But are you getting the insights you need? Web analytics tools like Google Analytics can provide a wealth of information about what people do on your site, but it becomes powerful when you do more than just look at trends going up or down. It’s about measuring and improving. Here are some tips on how to use your data for informed marketing decisions for your company. Make analysis a regular habit Checking analytics to evaluate website and marketing performance varies from business to business. Some do it multiple times a day or only when it’s time to do their monthly reporting or end up getting hooked on real-time analytics. Make it a regular habit to analyse your Google Analytics metrics and before you know it, you won’t need the constant reminders to do so and it'll feel less like a chore. You can start off with doing it a few times a week and if you find that there aren’t enough changes to come to any conclusions, then do it less frequently. Whilst for smaller businesses the results won’t change much hour to hour or even day to day, for the bigger businesses changes can be significant on a daily basis. Form your questions Before sifting through your Google Analytics reports, come up with a set of questions that you are looking to answer with your data. You might want to know: What are users searching for? (requires site search to be set up) Which pages are they spending the most time on? Which pages have the highest bounce rate and might need further tweaking? How are my marketing campaigns performing? Is my spending on Adwords justified? Which traffic sources bring the best converting traffic and are worth investing into? Are my call to actions working? (this is where goals come in handy) Know where to measure Think about which reports and metrics will be most suitable to answer your questions. Knowing what you're looking for will minimise the amount you spend wandering aimlessly through numerous reports hoping that you'll find something interesting. It’s said that there are over 100 standard reports available in Google Analytics, so it’s handy to know where to look. The reports are split into 4 main categories: Audience is about the users – where are they, what devices are they using, Acquisition is about how users get to your site – how are your campaigns performing, where do they come from Behaviour is about user interaction with your site – which landing pages get the highest traffic, which pages have the highest bounce rate Conversions is about users completing certain actions (requires further setup to get the most out the reports) – which goals did they complete, what is their shopping and checkout behaviour Pages with high page views and bounces / exit rate Check how your individual pages are performing in All Pages and Landing Pages reports (under Behaviour > Site Content). If your page is getting a lot of page views and has a high bounce / exit rate, then whilst it might be a valuable or attractive piece of content it’s not doing a great job at getting your users to another page. Can you provide some other relevant content on that page? Link to them where appropriate. This will help improve the visitor journey through the site and reduce the bounce rate. Know your user journeys You can use Google Analytics flow reports to view which paths users take through your site and where they drop off. Evaluate the pages with the biggest drop offs  - can you improve these pages to encourage users continue their journey? You've put a lot of work into the pages that are meant to convert your site visitors, but it's a waste of all that effort if your journey to the converting page doesn't work. Goal flow report is especially handy for seeing users' paths towards the goals you have set up. Not sure how to set up a goal funnel? Here's how. Segment your users Use Google Analytics segments to view and analyse a separate subset of user data. You could view your reports for users from a specific location, eg Spain, or with a specific device, eg Apple iPad, or by certain behaviour, eg made a purchase. Check out Google's guidance on using segments. Evaluate your tagged campaigns Custom campaign tracking is important for organising your campaigns so you can review the performance effectively. If you're not tagging your campaigns yet, check out our blog post on how to tag your campaigns. Share findings with the team It’s great if you get into the habit of reviewing Google Analytics data on a regular basis to inform your actions. What's even better is if you create a team culture where you share findings with each other. You can email around individual reports, share insight at team meetings, set up custom alerts or sign up to our web-based tool to do that for you. For those less geeky or knowledgeable about data, make sure you translate the findings into plain English statements (PS. our tool already does that too). Continuos improvement When Dave Brailsford became the head of British Cycling, he implemented the concept of marginal gains within cycling. He believed that by breaking up the process of competing and improving every step by 1%, they would see a big improvement in their team. And he was right. All the small changes accumulated into a massive performance boost, and Team GB surpassed everyone’s expectations by going on to some big wins at Olympics and Tour de France.  This can apply to many other areas as well - customer satisfaction, improving service quality, doing minor updates to marketing campaigns. Rather than focussing on one big improvement and spending weeks or months on it, before even knowing if it'll work, look at the potential small changes you could make. You will spot much more quickly which of these changes are of benefit and which are not. There's a lot of information stored in your Google Analytics, when used correctly and regularly you will start getting the insight you need to guide your marketing efforts. Suggestions above will help you do just that. Something else on your mind? Let us know in the comments below or get in touch!   Images: Courtesy of Suriya Kankliang, pannawat at FreeDigitalPhotos.net

2016-03-17

How to use the lookup table variable in Google Tag Manager

A lookup table in Google Tag Manager makes it much simpler to manage lots of values in your tracking setup. It can drastically reduce the number of tags required and turn your messy GTM into a neat environment. It's especially useful with larger setups where you have multiple tracking requirements and flexible to accommodate new tracking needs as they arise. You can easily add or remove values from your lookup tables, and not worry about having to change any codes. The lookup table variable allows you to define a set of key-value pairs where the output variable (the value that you are sending to Google Analytics) is linked to the identifier (the key). It works like this: When [input variable] equals to  _______, set [this output variable] to_______. For example, you could use the lookup table for: Assigning different Google Analytics property IDs for various domains/hostnames, eg. when [website hostname] equals to littledata.co.uk, set [property ID] to UA-010101 (see example below) Setting different pixel or conversions IDs for different country websites, eg when [website country code] equals to 2, set [pixel ID] to 88779 (requires having website country code variable defined) Defining your event categories, actions and labels (see example below) Remember! There’s no limit to how many values you can have in the lookup table, but the fields are case sensitive. So if you have multiple capitalisations of some input, then include all of them in the lookup table and assign the same output for each. I have previously explained setting up the tracking of user actions as events in GTM, but when you need to track multiple events, one tag just doesn't cut it anymore. And instead of creating several tags to cover each event or action, here's how you would create the lookup table to cover multiple values in one place. Creating lookup table variable for event parameters In the Littledata software interface, you get an option to switch between different report types or view them all. I want to track when people click on different report types, so instead of creating 5 different tags for each user action, I will set up a lookup table to cover all of them in one place. But firstly I need to know which variable to use as the input. You can only have one type of input variable per the lookup table so you want to pick a variable type that applies to each (ideally). For this, I will check how each report type option has been set up in the code by inspecting the element (inspect/inspect element depending on the browser you're using and usually accessible via right click). Here's how each report type has been set up: <a href="/report-list/m2i4MnmXcewDSzZ3c/all" class="current" id="ga-all">All <span class="count">120</span></a> <a href="/report-list/m2i4MnmXcewDSzZ3c/trends" class="" id="ga-trends">Trends <span class="count">80</span></a> <a href="/report-list/m2i4MnmXcewDSzZ3c/pages" class="" id="ga-pages">Pages <span class="count">37</span></a> <a href="/report-list/m2i4MnmXcewDSzZ3c/tips" class="" id="ga-tips">Tips <span class="count">3</span></a> <a href="/report-list/m2i4MnmXcewDSzZ3c/benchmark" class="" id="ga-benchmark">Benchmark <span class="count">0</span></a> Looking at the above, I can see that each report type has a unique ID - here that's the best one to use. Now to set this up, go to Variables, click ‘New’ and select 'Lookup Table' as your variable type. For the input variable, I will use {{Click ID}} as explained above, but you, of course, use whatever unique identifier you have available. For your output, you want to define the event action you are going to send to the Events report in Google Analytics. Should you set the default value? You can set a default value for the output when there is no match found in your table. With the event tracking, I sometimes find it useful to enable to identify if I set up my tag correctly. If my trigger ends up being too broad, the default value option will pick up additional values not defined in the table. I will then see these values in Google Analytics reports and this way I can tidy up the trigger to be more accurate. So this is what your variable should look like now. Click ‘Create Variable’ and there you have it. In your GA event tag, the newly created variable would look like this. Other uses Multiple Google Analytics properties If you have a single GTM container installed on multiple domains but you're tracking them across different Google Analytics properties, you want to ensure that you're sending the data to the correct one. Instead of having multiple variables to store different property IDs, you can have them all neatly in the same table defined by the hostname. This way any tracking activity on each site will go to its own dedicated property. Excluding test or other data If you want to make sure that any data outside of your main site goes to a test or other Google Analytics property, you can do so by setting the default value. The default value is the output that is not found in the table. With this setup, any activity tracked on www.mainsite.com goes to property ID UA-121212. If the activity wasn't on www.mainsite.com, then it sent to property ID UA-121212-2. Use lookup tables for something else? Confused? Get in touch or comment below!

2016-03-09

How to set up event tracking in Google Tag Manager

Events in Google Analytics are important for understanding how people interact with your website. They give you additional insight into their behaviour and how effective your pages are for leading users towards a conversion. With event tracking you could see how many users clicked on a button or played a video, scrolled down a page or clicked on your contact and social media icons. I mostly use Google Tag Manager (GTM) for analytics setup so I will show how to set up event tracking for clicks on buttons with GTM. Instead of hard coding events in the code, GTM allows you to create, test and amend tags within its interface. Before you go ahead creating your event tags, make sure your built-in pages and clicks variables are enabled. This will avoid you having to go back and forth between different sections. The setup below covers only one action - a click on a specific button - but if you have multiple actions to track, then look into implementing a lookup table variable. Tracking button clicks Here's my scenario. I want to track our BENCHMARK YOUR SITE button that allows users to sign up to our free software plan and get benchmarked against competitors.   And here's how to set it up. 1. Create a tag It will be a Universal Analytics tag type where tracking ID is a constant string variable (you need to create this variable before using it) and track type 'Event'. Think of your event tracking parameters as a way to organise the events into a hierarchy: Category – the main aim of the button or its placement Action – what the user clicked or the action Label – provides additional information like on what page the button was clicked or the outbound link they clicked on Value – if you have a numerical value to set for your click (not in my case tho) In my example, the category is ‘Get started’ because we have a number of similar buttons across the site with the same purpose to get the user started with the signup, so all of them have the same event category. For action, I specify the type of button that was clicked on so I can compare how these different buttons perform - 'Benchmark your site' in this case. My event label is the {{Page Path}} where they clicked on the button. The buttons take the user to the same place so I’m more interested in which pages these buttons were clicked on. Alternatively, if you have buttons that take people to different URLs you might want to track that instead. Is it a non-interaction hit? This is an important one to keep in mind. By default this is set to False. If you don’t want this event to impact your bounce rate, then change it to True, which you would do if the click or action didn’t take the user to the new page, or if you didn't want it to be included in your bounce rate calculations. Now click 'Continue' to go to the trigger setup. 2. Create a trigger Trigger is like a rule that allows you to tell the tag, ie specify the conditions, when it should fire. Under 'Fire On' select ‘Click’ as your trigger type and then ‘New’. For configuring the trigger, you have a choice between two types: Just Links – use this when the target is a link or anchor tag <a> All Elements – use this when the target is any other element that’s not a link To determine what’s best for your purposes you need to have a look at how your button is set up. You can do this by selecting ‘inspect element’ or simply ‘inspect’ depending on what browser you’re using. It’s usually available when you right click on the button or element.   Our button has been set up the following way: <a href="https://littledata.uk/signup" class="btn btn-ltd btn-green">benchmark your site</a> It has a link so I will use 'Just Links' for targets and I have a choice between three elements to use in further configuration: https://littledata.uk/signup as click url btn btn-ltd btn-green as click class benchmark your site as click text It is best to use a unique condition if you can. This way, if similar class or click url gets reused in other parts of the website you don't have to go back to this trigger to update it. With 'Just Links' you will get additional configuration options: Wait for tags - delays opening of links until all other tags have fired or the wait time has lapsed, whichever happens first Check validation - fires the tag only when opening the link was a valid action, without the tag will fire whenever the user clicks on the button/link Enable when - this options is shown only when either of the above is ticked so you can be specific about where you want the trigger to be active If you want the trigger to listen to the interactions on all pages, then set that section to be  URL or Page Path matches regex .*. (without that very last full stop - that one's for the sentence) In my case, I only want it to work on benchmark pages and all of them start with /benchmark/. The very last step in trigger setup is specifying on which actions or clicks the tag should fire. As said above, I'm using the button's click class here. All done? This is what your tag should now look like. Click 'Create Tag'. 3. Test Test your tag in GTM's preview mode by checking two things: the tag fires in the preview interface, and the tag is seen in Google Analytics real time view under 'Events' with the event parameters you specified   I hope you got on with the setup above just fine, but if you have questions or clarifications, feel free to ask below.   Further reading: Know who converts on your site with Google Analytics goals Using lookup table variable in Google Tag Manager Intro to Google Tag Manager's key concepts and terminology Image: Courtesy of suphakit73 at FreeDigitalPhotos.net  

2016-03-02

How to trust your Google Analytics data setup

Google Analytics is a powerful tool… when implemented correctly. I can’t even count the number of times we've had enquiries from and spoken to companies who don’t trust the data in their reports because it's incorrect or incomplete. And it all comes down to wrong configuration and setup. Checking and amending correctly the very basics of your analytics setup will provide you with data you can rely on and an accurate foundation for further more advanced configurations, like Enhanced Ecommerce tracking. So here's a list of questions you should be asking whilst checking your Google Analytics (GA) property and view settings. This is assuming you're on Universal Analytics (analytics.js) so not all setup options may apply if your site is on Classic analytics (ga.js). I'll also cover a few common setup issues at the end. GA property settings Go to Admin > Property > Property Settings. Is your default URL set up correctly? The default URL is used in Content and in-Page Analytics reports to display page previews. Do you have a correct default view picked? By default, this will be the first view created at the time of initial GA setup. If you're using AdWords Express or Google Play, then you want to check the view here is the one you want to connect to either of the services. The default view will also show you all the custom and advanced segments you've created in other views. Have you set your industry category? Pick whatever matches your property most closely if you want to be included in the benchmark reports. Have you enabled demographics reports? Demographics and interests reports give you additional insight into your users. Recently I explained how to set this up in Google Analytics and Google Tag Manager V2. Do you need enhanced link attribution? Enable this if you have pages with multiple links that take people to the same destination or a page element that has multiple destinations, eg internal search. This will help with identifying which particular elements or links were clicked. In addition to enabling this in the property settings, you also need to add a line of code to your GA tracking code, or, if using GTM, toggle Enhanced Link Attribution to true in your pageview tag under Advanced Configuration settings. Should you link with your Search Console? Link your Search Console site with your Google Analytics property to see Search Console data in your GA reports, and access GA reports directly from the Links to your site and Sitelinks sections in Search Console. GA view settings Property settings sorted? Great, now go to View > View Settings. Is your view name descriptive? Use easy to understand naming to describe what the view is for, eg excluding admin, domains included, ecommerce data only. Have you set your default URL? Similarly to the property settings, make sure you use the correct default URL here to improve your Content and in-Page Analytics reports. Have you set a correct time zone? The beginning and end of each day for your reports is calculated based on the time zone you have set. If you need to update this, you may see a flat spike in your data caused by the time shift. Do you need a default page? Setting a default page is useful when you have two separate URLs loading the same homepage. Here you can configure those pages to be considered as the same URL. This will affect your reports so make sure you do this correctly Should you exclude URL query parameters? Specify any parameters you don’t want to see in your reports. I've found a blog post from Lunametrics useful for understanding when and how to exclude URL query parameters. Is your currency correct? Especially relevant for sites with ecommerce tracking for making sure that the reports show your order values and revenues in the currency you operate in, and not in $ that it converts to by default. Have you ticked bot filtering option? Whilst this option doesn't help with eliminating all of the spam referrals, ticking this box will exclude at least a few of them. To get rid of all of your fake referrals, here's a thorough guide on how to exclude them with two filters. Get yourself a cuppa if you're going to clean up your data. Does your website have a search function? Enabling the site search is useful for understanding what your website visitors are looking for. It should be pretty painless to set up if you have a query included in the URL, and we've covered the steps to set up internal site search tracking in one of our blogs. Other common setup issues Here are also a few very common setup problems that I keep coming across again and again. Have you got an unfiltered view? It's good practice to have an unfiltered view that you keep clean from any filters and customisation. This way you can always double-check your data if anything goes wrong in another view. Is your bounce rate less than 10% whilst your pageviews have doubled? This may be happening due to pageviews firing multiple times. You can use Tag Assistant plugin for Chrome to check if that's true. Are you getting referrals from your own domain and your payment gateway? This is skewing your data so checkpoints 3 and 4 on how to exclude referrals from your domain and payment provider. Tracking multiple subdomains in the same view? By default, you see only request URI in your reports without a domain, which isn't very helpful if you are tracking more than one domain in the same GA view. You can improve this by adding a hostname to URLs with a custom filter. Check Google's guidance for how to do it. Are you filtering out internal traffic? To minimise your data being skewed by internal colleagues or partner companies you may be working with, exclude their IPs with the help of filters. Are you on top of website traffic changes? OK, so this one isn't quite about the problem with the setup but if data has an important role in your business, you can make your analysis more efficient. Google provides you with the ability to set up alerts for important changes in your data, but our software does the work for you. Instead of trawling your data for hours or spending further time on configurations, you can set up alerts and personalised reports within minutes.   Have you experienced other setup problems that aren't covered above? Let me know and I'll include them. Image Credit: Images courtesy of vectorolie and ratch0013 at FreeDigitalPhotos.net

2016-02-18

Know who converts on your site with Google Analytics goals

Wouldn't you want to know how well people convert on your site? Setting up basic conversion goals will enable you to measure site engagement – based on time on site, destination page or particular events - and what drives that. Below I’ll cover the reasons why you should set up goal tracking in your Google Analytics, different types of goals available, goal value, and then explain how to set them up. So why should you track goals? Goals are great for tracking important actions that are crucial for your business and understanding how people convert on your site. Once you set up goals, you will be able to analyse conversion rates in the Goals reports. Conversion data will also appear in other Google Analytics reports, like the Attribution and Acquisition reports. This will help you identify which marketing campaigns and channels get users to complete the goals you have previously defined. The destination goal also allows you to set up a funnel to visualise the path people take through your site towards completing a purchase, signing up or another conversion. Seeing how people navigate through your site in a visual way makes it easier to identify where they drop off. If you see a lot of exits on particular pages, then review those pages to see if you can improve them to minimise the exits and guide more people towards converting. If you see a lot of people skipping certain pages, then your path to conversion might be too long or contain unnecessary steps. For more info on flow visualisation reports, check Google’s help pages. What kind of goals can you set up? You can set up a destination goal to track how many users reached a certain page, eg thank you, purchase confirmation or pre-order request pages. Then there’s a duration goal that tracks how many users stayed for a specific amount of time, eg for at least 15 minutes. You can also set up a pages/screens per session goal to see how many users view a specific number of pages during a session. An event goal is for when a user triggered certain events on the site that you have already set up, eg clicked on an ad, submitted a form or saved a product. What else should you know about goals? Goals have a few limitations in Google Analytics: You can set up only 20 goals per view. If you need more, you can either create another view or repurpose existing goals. Goals apply to the data after you’ve created them. Goals can’t be deleted; but you can turn them off if you don’t need them. Use names that make sense so that anyone using your Google Analytics data can understand what the goals are for. Keep track of when you changed the goal by adding annotations to your reports. Do you need the goal value? Setting up a goal value is optional. You should set a monetary value for your goal when you want to track how much you earned from converting users and you’re able to calculate the worth of each lead. If you know that 5% of people who sign up on your site end up buying your service, and the average value of your service is £1000, then you can set £50 as your goal value (5% of 1000). When setting up a goal value, make sure the currency corresponds to what you use on the site or are familiar with. You can do this in Admin > View > View Settings. Are you an ecommerce site? If you’re an online retailer, then instead of using goal values you should be using Ecommerce or Enhanced Ecommerce tracking for Google Analytics. These reports will be much more insightful for tracking your store performance. So how do you set up goals? You need to set these up at the view level. Go to Admin > View > Goals, and click New Goal. Google has added some goal templates that you can choose from if you’re happy to use their naming. Alternatively, select 'Custom' at the end of the list and click ‘Continue’ to the goal description. For your goal name use something that is easily understood by others using your Google Analytics account, and the goal details will depend on the type of goal you're setting up. Setting up destination goal You can follow the blog I've previously written on setting up the destination goal and funnel. Setting up duration goal Click ‘Continue’ and specify the minimum amount of time you want to track. Setting up pages/screens per session goal Here you specify the number of pages someone viewed per session. Setting up event goal Set the event you want to track as a goal by using exactly the same category, action, label and value as in the event. If you want to use a goal value here, you have the option to use the event value you’ve already set. Verify your goal - click ‘Verify’ to check if it works. If the goal has been set up correctly, you should see an estimation of the conversion rate your goal would get. If you’re not getting anything, check each step carefully and Google's help pages on why your conversion tracking might not be working. Once you’re happy with the setup, click ‘Create goal’ and check the results in your analytics reports after a few days or weeks, depending on the amount of traffic you get.   If you need help with the setup above or have another way of using goals, I’d love to hear about it in the comments below.

2016-01-28

A win for the UK digital sector: UK sites perform better than US sites in benchmark

UK-based websites are 5 percentage points better than their US peers at keeping mobile users engaged (with a lower bounce rate), and 2.5 percentage points better at keeping the users from desktop / laptop computers engaged. For bounce rate from email marketing, the difference was also 5 percentage points (a 14% better performance from UK websites). The comparison is based on the Google Analytics data from 209 UK companies and 95 US companies collated by Littledata. The British web industry has benefited from earlier smartphone adoption in the UK (81% vs 75% in the US; source: MarketingLand), and overall greater internet usage from UK consumers (source: Econsultancy). That should put UK-based developers in a great position to sell their experience to other countries with increasing internet adoption An example is MADE.com, a London-based furniture retailer which has used superior online customer acquisition to drive growth across the UK and continental Europe. Littledata founder, Edward Upton, explains: “It’s usually hard to get a hold of industry data to compare digital product performance against similar companies, but Littledata’s benchmarks provide a simple way for companies to find website features that are underperforming.” If your website beats those benchmarks that should not stop you improving. Whilst it’s great to know you’re doing well in a particular area, there are many comparative metrics you can check with our benchmarks to fully understand your performance overall. If your site is struggling with engaging users, then check out our suggestions on improving your bounce rate . Want to know how your site performs? Head over to Littledata Benchmark page and click 'Benchmark your site' to check your performance against others. How Littledata benchmarks work? We gather data from thousands of Google Analytics profiles, and anonymise them in a series of benchmarks, to give insight into how your marketing efforts are paying off. With this benchmark data, you can stop being in the dark about how your website performs and sign up to see how your site compares. Our customers also receive daily insight into site or app performance with our actionable trends reports. You can explore these and other benchmarks via Littledata Benchmark index page.   How would you use benchmarks in your daily work? Leave your comments below.

2016-01-14

Why should you tag your campaigns for Google Analytics?

Google Analytics custom campaign tracking is essential for measuring the effectiveness of your marketing efforts. Let's say you were promoting your new ebook across social media and emails, how would you know which social post or email blast was the most effective? That’s where Google campaign parameters come in (also referred to as UTM). You simply add them to your URLs, which are then used in your web-based, email or ad promotions. When someone clicks on them, the custom information linked to these URLs via parameters is sent to your Google Analytics reports. If you don’t tell Google the specifics of your campaigns, then they will be rolled into existing buckets without the ability to identify them. This most commonly happens with emails and social posts that by default get classified as referrals. But once you start tagging your campaigns, you will see those social initiatives and email newsletters separated by campaign names and other information you provided. Tagged up links can also be used in email signatures, listings on other sites and social media profiles. By using campaign tagging you will understand better which URLs have been most effective in attracting users to your site or content, for example you'll see which: Email newsletter brought you the most traffic Ad was best at bringing you converting visitors Facebook post engaged the most users If you have goals set up, then you will also see how visitors from individual campaigns convert on your website. Using custom campaign data in reports You can access custom campaign data in Acquisition > Campaigns > All Campaigns report, where you will see your various campaigns based on the parameters used in URLs. You can also switch between viewing your campaigns by source and medium tags that you’ve used. Another report you can use is the Assisted Conversions (under Conversion > Multi-Channel Funnels) that summarises how your channels, or campaigns, contribute to your conversions. To see the campaigns, you need to click on 'Other', find 'Campaign' and select it. Now you will see data related to your campaigns only. Check Google's guidance on understanding the Assisted Conversions report. Be consistent Consistency is very important in campaign tagging so make sure that the parameters you use in your campaigns are exact. For example, if you use email, Email and E-mail, Google Analytics will record them as three different mediums in your reports. So, set your naming conventions and if you have a bigger team, then agree on what they are and make sure everyone is aware of them. What tags can you use in your campaigns? There are five types of information you can pass on with the tags/URLs. Three of them should always be used: Campaign source (utm_source) - identifies where the traffic comes from, eg newsletter, google. Campaign medium (utm_medium) – advertising or marketing medium, eg cpc, email. Campaign name (utm_campaign) – what the campaign is called whether it's a promo code or specific promotion, eg winter sale. The other two, whilst not required by Google, are useful for tracking additional information: Campaign term (utm_term) - identifies paid search keywords if you’re manually tagging your paid keyword campaigns, eg red shoes. Campaign content (utm_content) – helps differentiate between same type of content or links, useful when doing AB testing or using multiple calls to action, eg logo or text link. How to tag your campaigns? It’s easier than you might think. You can do it manually if you know how, but the available URL builder tools online make it super simple to tag your links correctly. But if you're using Adwords or Bing then you can enable auto-tagging so you don't have to worry about tagging them. For websites use the Google URL builder tool to append URL parameters. For Android, use the Google Play URL builder tool to append URL parameters. You also must have Google Play Campaign Attribution set up in your Android SDK. For iOS, use the iOS Campaign Tracking URL Builder to append URL parameters. You must use Google Analytics iOS SDK v3 or higher for this to work. For manual tagging, you need to enter a question mark after the URL and before adding your parameters. Then pair up the parameters with their values, eg utm_source=newsletter, and separate campaign parameters with an ampersand. After the question mark, parameters can be placed in any order. You'll end up with a link that'll look something like this: http://www.littledata.io/?utm_source=newsletter&utm_medium=email&utm_campaign=welcome, which is ready for use in your promo activities. Auto-tag your campaigns To make campaign tracking and tagging simpler, we have created a tool in Google Sheets that automatically creates a tagged up link. You'll need to fill the values for parameters and the formula will do the rest for you. To use it, you'll need to make a copy to store in your own Drive (via File option). Get campaign tracking sheet with URL builder   Got questions? Comment below or get in touch!

2016-01-06

How to set up demographics and interests reports in Google Analytics

Demographics and interests reports in Google Analytics give you additional insight about your users, allowing you to do analysis based on age, gender and interest categories. You get a much better idea of who your users are and the setup is so quick to do, there's no reason not to. To get this information, you need to do minor tweaks to your Google Analytics and Google Tag Manager. Those changes will allow Google to share anonymised data about your site or app visitors, and once set up, you can use this information to understand the behaviour patterns of your users by different profiles. You will be able to see: If a particular age group converts more Whether you get more visits from males or females from a particular country or city If your users are more into travelling, movies or social media You'll also be able to: Build remarketing lists Build segments for more detailed information about your users Target your ads to specific users What reports will you get? Demographics Overview: snapshot view of your users by age and gender Age: Acquisition, Behaviour and Conversions metrics by age group (below 18 are not included) Gender: Acquisition, Behavior and Conversions metrics by gender Interests Overview: top 10 interests of your users in 3 areas: Affinity Categories, In-Market Segments and Other Categories Affinity Categories (reach): view of users by their lifestyle with Acquisition, Behaviour and Conversions metrics broken down by Affinity Categories In-Market Segments: view of users by their product-purchasing interests with Acquisition, Behaviour and Conversions metrics broken down by In-Market Segments Other Categories: more specific view of users with Acquisition, Behaviour and Conversions metrics broken down by Other Categories How does Google get this data? Google collects demographics and interests data from the third-party DoubleClick cookie for web traffic and anonymous identifiers for mobile app activity, like the Android Advertising ID and the iOS Identifier for Advertisers. But Google is unable to collect this data if the cookie or anonymous identifier isn't present, or if there's no profile information available. As a result, this data may only be available for a subset of your users. This will be shown on the report as a % of traffic the report represents. When is threshold applied? There are occasions when data is withheld from your reports to ensure the anonymity of users. For example, this might happen when you don’t have enough data for a particular age range or gender. When the threshold has been applied, you will see a notification below the report title. 3 simple steps to set this up 1. Enable the feature in Google Analytics Go to Admin > Property > Property Settings. Scroll down to Advertising Features, and set the option to Enable Demographics and Interests Reports to ON. Now save. 2. Enable the feature in Google Tag Manager Go to edit your GA pageview tag > Configure Tag. Under the tracking ID, tick the Enable Display Advertising Features box. Save the tag, and you've got one last step to do. 3. Enable the report in Google Analytics For this go to Audience > Demographics > Overview report. Click Enable, and you're all set. You should see your demographics and interests data within 24 hours of enabling the feature. We also provide consultancy services if you need help with more advanced setup.   Further reading: Tracking registered users with Google Analytics and GTM V2 How to use demographic targeting in AdWords  

2015-12-18

7 ways to reduce your bounce rate

Wondering why your bounce rate is so high and people are not sticking around? Here are some methods you should consider to improve user engagement with your content, conversion rates and sales. Bounce rate is the percentage of single page sessions or visits where the person didn’t engage further than the one page within your site. You shouldn't worry about the high bounce rate if your site visitors are meant to find what they were looking for on a single page. But if it's important for your site that users stick around for either reading more content or going through further pages that lead towards conversion, then you should review your options for reducing the bounce rate. By decreasing the bounce rate you can improve your ability to engage more users and eventually get them to convert. There are a few reasons why you would have a high bounce rate: Single page site or landing page Incorrect setup Wrong audience Design Usability User behaviour Low quality content You can identify your worst performing content by looking at the bounce rate in the Landing Pages report (under Behaviour > Site Content). If there is a high percentage of people leaving the pages without continuing their journey, then review those pages with the suggestions below. You should also check the average time spent on those pages. If users are leaving after a short amount of time, then you should look closely at what may be driving them away and if there are any improvements you can make to keep the visitor on the page for longer, or how to encourage them to visit other pages. Guide users through your website with additional links Users might leave your site after seeing a single page that contained the information they were looking for. If they got what they wanted and don’t care about actively exploring your website, then think about similar pages within your site that might be of interest to your users, and link to them within the content. You could link to: Another blog post covering similar topic from a different angle A case study to increase the credibility of your work Related blog post that the reader might like Best practices of using your product Case studies on how others have achieved results with your product Your product demo or webinar This can be applied to any pages from product and features to blog and about your team. Blog posts on Moz Blog are a great example of providing additional links that are useful and relevant. Improve your page load Your page loading time has a major impact on how quickly people will leave your site, which should be obvious to everyone. Slow site speed can be very discouraging to your potential customers and drive them away. How long would you wait for a page to load, before going elsewhere to a quicker website? 47% of users online expect the page to load in two seconds or less. The study cited in an article by Econsultancy is several years old so it is highly likely that people are even more impatient now, making the number of people abandoning the site even higher than the 40% it used to be. Check Google PageSpeed Insights for more detail. Make content readable It is difficult to read large chunks of text that consists of long paragraphs, too much jargon and bad formatting. With our shorter attention span and higher impatience, the more user friendly you can make the text, the better for your site performance. There are a number of ways you can improve the readability of your content: Large headings Bold subheadings Bullets and lists Shorter sentences and paragraphs Less or no jargon Write like you talk Use images Bold keywords where appropriate Add a relevant call to action on the landing page If you have a landing page for converting visitors, whether it is for getting them to enquire or sign up, you need a relevant and prominent call to action (CTA). At Littledata we use CTA in two places on the landing page - top and bottom - to help the user enquire about our services much quicker. I also like Intercom product pages, which have some awesome animations and illustrations, and a call to action that fits the theme. Your CTA could be focussed on getting your users to: Call the company to talk about the product Fill out a form Sign up for a trial Click on banner ad Watch video Subscribe to a newsletter Visit another page within your site or external site Econsultancy has collected examples of some awesome calls to action so check them out for inspiration. Check your landing pages meet visitor expectations If people expect to sign up for a free trial of your software product, but are instead taken to a homepage without a visible way of doing so, then expect a lot of bounces. Invision uses Adwords to promote its free platform plan. Once you click through, you see immediately the content you expect and the option to sign up. If you purchase some of your traffic, make sure you check what information visitors see on your partners’ site before clicking on the link. When we recently ran a number of tests to improve the bounce rate for our client, we were baffled by some of the improvements not having much effect. After further investigation it came out that the visitors on the partner sites were getting the wrong information about what they were clicking on. No surprise then that they were leaving the site so soon. For search engine results, review your page titles and meta descriptions, and make sure they match what the person will see on the page when they click on it. Set external links to open in a new window By providing an external link that opens within the same window, you are forcing your users to leave your site. This will not only affect your bounce rate, but you will also be increasing your exit rate. Instead of interrupting their journey this way, set any external links to open in a new tab. Avoid distracting users from the content Whilst some popups can be relevant to the content of the page and important for your aims, a badly timed popup can be very off-putting for your site users. Your landing page is there to convince the visitor to stay so if your popup displays instantly, you're not letting them see your content that they came for in the first place. Test different timings to see what works best for your users, but I'd be surprised if quickly displaying popups reduce your bounce rate. Autoplaying random songs can also be highly annoying. Especially when it's not the kind of song you listen to, on full blast, and hidden somewhere so it takes you ages to find the music to pause it. Just no.   There are no quick wins when it comes to improving your bounce rate. Keep making the improvements with your reader in mind and testing which changes work for you best. So I hope this has been helpful. If you have any experiences with methods mentioned above, do share in the comments below. Further reading: A win for the UK digital sector: UK sites perform better than US sites in benchmark 5 common Google Analytics setup problems to look out for How to accurately track time on site with Google Analytics or Google Tag Manager Stuck with reducing your bounce rate? Contact our certified Google Analytics specialists for help with your bounce rate or other advanced tracking.  

2015-11-25

7 quick wins to speed up your site analysis techniques in Google Analytics

Analysis and reporting are the most time-consuming aspects of site or app performance tracking in Google Analytics. If you ever wished or thought if only it was quicker, then this post is for you. There are a number of techniques you can implement to speed up your data analysis and number crunching. Here I’ll cover 6 of them. Schedule email reports Google Analytics dashboards are a great way to monitor metrics that are important for your business. But instead of logging in every day or week, or however often you tend to check them, schedule automated email reports instead. At Littledata, we have a select few metrics that we keep track of on a weekly and monthly basis. The whole team gets an email report on a specified day, allowing everyone to get the latest stats without someone on the team having to get those numbers manually every time. To set this up, go to the dashboard that you want emailed to others (or yourself), click ‘Email’ and fill in the details. If you're scheduling the email to go to your team on a regular basis, why not add a nice message in the email body. To edit the scheduled emails you've set up previously, go to Admin > View > Scheduled Emails (towards the bottom of the list). Access your reports quickly Shortcuts in Google Analytics allow you to quickly view the reports you use most often. Even better, they remember the settings you applied to any report. So if you apply an advanced segment or another customisation to the report, saving it as a shortcut will remember your preferences. Except for the date range - that won't be remembered. You can find the shortcut option just below the report title, and once added, you'll find your shortcut reports at the top of the reports list in the left panel. Search for reports you can’t find If you find yourself wondering where a particular report is, use the search found at the very top. Instead of having to go through an extensive report list trying to find something you vaguely remember seeing last month, you get suggestions of what you might be looking for as you type. So you only need to remember or guess part of the report title that you're looking for. Use keyboard shortcuts Did you know Google Analytics has keyboard shortcuts? They allow you to move around the report much quicker and the date range keyboards make a big difference to a workflow. Picking date ranges can be tedious and annoying so I've found these to be the best. If you're already using keyboard shortcuts on your devices, you won't need convincing of their usefulness. To view this complete list of shortcuts in Google Analytics at any time, use a shortcut: ? Set up goals to understand your website visitors Goals are valuable in understanding how well your site or app helps you achieve your objectives. Unfortunately, we see a lot of businesses who either find it too complicated to set up or have done it incorrectly. Speaking from personal experience, it only takes a little practice to get the hang of it, and once setup, you get essential conversion data in your reports. You'll be able to evaluate your marketing efforts and campaigns much more effectively. Check out Google's guidance on goals and my guide on how to set up a destination goal funnel. See trends quickly with Littledata reports We have a clever tool that looks through all of your Google Analytics data and finds the most interesting changes to report on. There are over hundred of GA reports so getting automated summaries that you can act upon will save you hours of work. Littledata tool doesn't require installation and it's quick to set up - all you need is an existing Google Analytics account to sign up with for free. The reports you'll get are also great for presenting to colleagues in meetings, as other users have said. To get your reports, go to Littledata homepage, enter your website into the box and click 'Get started.' We're also working on bringing you benchmarking information, customised tips on how to improve your Analytics setup and what you should be tracking. Pro tip: Manage complex data with query explorer tool Whilst, not the quickest to get used to, Google's query explorer tool can be powerful for those working with large and complex datasets. Some of our biggest clients' websites get millions of hits a month, which can cause discrepancies in data analysis (especially when data is sampled). So I use the query explorer tool to verify the data that clients ask for. To use this tool, you will need to know your metrics from dimensions and learn more about how to use segments, filters and query building.   If you've got questions on any of the above, don't hesitate to comment below or get in touch!  

2015-10-15

How to track registered users with Google Analytics and Google Tag Manager V2

Wondering if Samsung Galaxy is more popular than iPhone when engaging with your content? Then set up the User-ID view to see your logged in users’ activity and evaluate behaviour by the device. With the activity data you collect in the registered users view, you can improve the analysis of your customers' behaviour by seeing which devices are used to sign up and access your website. To summarise the benefits: You get access to the Cross-Device reports, which allow you to analyse which devices your users use to engage with your content. See what the Cross-Device reports look like. You improve your understanding of logged in users who often engage with the site's content differently than those who aren't registered. You get a more accurate user count. In your standard analytics view, a new user is counted every time your site visitor switches to a new device or starts a new session. With the registered user view, you give each user a unique ID, which helps to stitch together various activities carried out by the user. You can find out which devices users prefer for different engagement activities across multiple sessions. This helps with tailoring your campaign and content to different devices and activities. To set this up, you need to have the user ID stored in the data layer. If you don't have it set up, scroll to the bottom for an advanced hack. Now let’s look at how to set up the tracking by using Google Analytics and Google Tag Manager V2. Looking to implement the User-ID in your tracking code? Check Google’s guidance. Enable the feature in Google Analytics Firstly, enable the User-ID feature by going to  Admin > Property > Tracking info > User-ID. Read through the short policy on what you’re allowed to track and not. Google is very strict about tracking personally identifiable information so you are not allowed to send any personally identifiable information, such as names and email addresses. But numbered IDs or hashed emails are fine to use. To agree to the terms, follow the steps and click ‘create.’ Create the variable in Google Tag Manager Now go to GTM variables and click 'new'. Select Data Layer Variable type and use the name stored in your data layer, e.g. uid or user ID Add the variable to your pageview tag Go to edit your pageview tag and click on More settings > Fields to set. Click Add field, enter the field name as &uid and select the variable you’ve just created - eg {{uid}} or {{userID}}. Test you're seeing activity in the newly created registered users view with your login, or a test one if you have it. Don't forget to publish your GTM container for tracking to work. Advanced hack If for some reason you can't get your developer to store a user ID in the data layer, there is a way around it. We've created a javascript variable to get a username off the page and hash it prior to sending it to GA. For this, you need to pick a custom Javascript type variable and enter the script below into the custom javascript field. This javascript requires either your developer or you to customise it to work on your page (see the notes in the second and third lines). function() { //dependent on using Jquery selectors //replace '.menuTitle small a' with the selector for your username var name = $('.menuTitle small a').text(); var hash = 0, i, chr, len; if (name.length == 0) return hash; for (i = 0, len = name.length; i < len; i++) { chr = name.charCodeAt(i); hash = ((hash << 5) - hash) + chr; hash |= 0; // Convert to 32bit integer } return hash; }; If you need help with any of the above, don't hesitate to comment below or get in touch!

2015-08-19

How to remove referral spam from historical data in Google Analytics

This is a quick follow-up to my guide on how to exclude referral spam from your Google Analytics data. Filters exclude or modify the data from the time you add them and don't have any effect on previous traffic. This is where segments are very useful. Not only can you use a segment to view a cleaner version of your historical data but you can also test the setup of your filters. I've also found the Google's filter verification option quite unreliable but with the segment, you can verify the results yourself and see results straight away. Here I am going to show how to add segments to include valid hostnames and exclude spam referrals from your data. Add a segment to include valid hostnames Creating a filter to include visits from valid hostnames only is the first step you need to take to exclude spam referrals from your Google Analytics data. Test your valid hostnames regex by firstly going to Audience > Technology > Network > Hostname. Create a filter by clicking on ‘Add segment’ and then ‘New Segment’. Now select the conditions tab on the left, under advanced. Set up your filter with the following conditions: Sessions Include Hostname Matches regex (and your regex, eg yoursite|googleusercontent, in our case it's littledata|googleusercontent) Click on ‘Preview’ button on at the top to check which hostnames you are left with. Your list should look much cleaner and only display domains you used in regex. Add a segment to exclude referral spam Like before, you want to test this trigger when viewing a relevant report so go to Acquisitions > All Traffic > Referrals. Create a segment with the following details: Sessions Exclude Medium exactly matches referral AND Source matches regex (and your regex) Whilst filters have a limit of 255 characters, the advanced segment has much more character space to use. I've bundled all spam referrals into one long regex of 900 characters. But as explained in the guide on removing spam traffic you might have to break it up into multiple expressions or filters to fit them all in. By adding those two segments you can not only test that your filter setup is accurate but also view your historical data without fake traffic. If you need help with any of the above, leave a comment below or get in touch!

2015-07-30

How to remove referral spam from Google Analytics

The issue with the referral spam in Google Analytics exploded in May when we saw an average of 620 spam sessions per GA property and just the other week, I saw an account where spam accounted for 95% of the traffic! Spam referrals are greatly skewing your Google Analytics traffic and becoming a headache for a larger number of people. Why are these spam sessions appearing in your Google Analytics traffic? To get you click through to their site and ads (never ever do that, by the way). By targeting thousands of GA accounts like this, you can imagine how much traffic they get from those more curious about their new source of visits. There are two different types of spam referrals you are getting: Ghost referrals send fake traffic to your GA account by “attacking” random GA property IDs. Crawler referrals crawl your website to leave a mark in your traffic. The spam referrals are getting more persistent and clever by targeting other non-referral reports, like www.event-tracking.com appearing in events. How can you tell it's spam? By seeing unusual activity, odd referral sources, substantial changes in your metrics, and lots of (not set) values in various dimensions, eg hostname and language. So how do you remove spam referrals from your Google Analytics traffic? There are two filters you need to set up to remove both ghost and crawler spam referrals. Filters change your traffic permanently so if you don't have an unfiltered view of your data, then create one now. It's a good practice to have an unfiltered view that you don't modify and it allows you to check your filters are working correctly. We are also working on our own spam filter tool to help people get rid of pesky spam referrals with just a few clicks of a button. We have already released a beta version via our Littledata analytics reporting tool and are developing it further to make it more robust and comprehensive. But if you'd rather do it yourself, keep reading. Create a filter to include valid hostnames Since ghost referrals never actually visit the site, the best way to get rid of them is by creating a valid hostname filter. This filter will allow visits from “approved” websites that you consider valid. First, you will need to identify your valid hostnames by going to the report in Audience > Technology > Network > Hostname. Hostnames report shows domains where your GA tracking code was fired and helps to troubleshoot unusual traffic sources. Valid hostnames on the list will be the websites where you inserted the GA tracking code, use additional services, eg transactions, or reliable sites used by people to access your site, eg Google Translate. Your reliable hostnames could look like this: www.yoursite.com yoursite.com blog.yoursite.com translate.googleusercontent.com (user accessing your site via Google Translate) ecommercepartnersite.com webcache.googleusercontent.com (user accessing translated cached version of your site) Any other website that you do not recognise or looks suspicious, you can safely assume to be a hostname you want to exclude. Beware of any domains that appear as “credible sources", eg Google, Amazon and HuffingtonPost. They are used to mask the spammers. If you see (not set) hostname on your list, this could be because you're sending events to GA that don't have pageviews, for example tracking email opens and clicks. If you are sure you are not sending any such events to GA, you can also exclude any (not set) hostnames. Now that you have got your valid hostnames, you need a regular expression for a filter that will include your valid hostnames (and thus, exclude all other fake ones). It'll look like this: yoursite|googleusercontent|ecommercepartnersite In the regex above, the vertical bar | separating each domain means OR.  This will match any part of the string, so 'yoursite' will match 'blog.yoursite.com' as well as 'www.yoursite.com'. You can test your regex at http://regexpal.com/ by inserting your expression at the top and all the URLs at the bottom. All matches will be highlighted so you can see straightaway whether you have included all your valid hostnames correctly. Before adding the valid hostname filter in the settings, test it with an advanced segment. The results on the screen should now be only of your valid hostnames and without all the spammers. If all looks good, create a filter by going to Admin > View > Filters > New Filter. This will add a filter for that specific view only. If you want to add the same filter to more than one view, then check the details below. Select 'Include', pick a custom filter and select 'hostname' from the filter field menu. Now enter your regex into filter pattern field and click save.   Want to apply a filter to multiple views? Then go to Admin > Account > All Filters > New Filter.   The setup is exactly the same as above, except now you will see a section at the bottom titled 'Apply Filter to Views'. Select views you want to apply the filter to and move them to the right hand side box by clicking button 'add' in the middle. You're all set so click save. Add a filter to exclude campaign source Some of the known spam referrals are free-social-buttons, guardlink.org, 4webmasters.org and, most recently, the ironically named howtostopreferralspam.eu. Excluding spam referrals with campaign source filter is one of the most commonly mentioned methods online. This filter will exclude any referrer spam from the moment you add the filter (not from your historical data). The downside is that every time you have a new spam referral appear in your Google Analytics data you will have to add them to the existing filter, or create a new one if you’ve ran out of character space (allows only 255 characters). You can identify your spam referrals by going to referrals report found in Acquisition > All Traffic > Referrals. To save you some time, I have included the regex's we use below so you can copy them. Make sure you double check your referrals report against our list to see if there are any that haven't appeared in our reports yet. If you find a source not listed below, simply add it to the end and let us know in the comments. Similarly to setting up the filter to include valid hostnames only, now you need to add a filter to exclude spam referrals. We use the following regular expressions to filter out spam (yes, that's four filters): guardlink|event-tracking|vitaly rules|pornhub-forum|youporn-forum|theguardlan|hulfingtonpost|buy-cheap-online|Get-Free-Traffic-Now|adviceforum.com|aliexpress.com|ranksonic kabbalah-reg-bracelets|webmaster-tools|free-share-buttons|ilovevitaly|cenoval|bestwebsitesawards|o-o-6-o-o|humanorightswatch|best-seo-offer|4webmasters|forum69.info|webmaster-traffic|torture.ml|amanda-porn|generalporn depositfiles-porn|meendo-free-traffic|googlsucks|o-o-8-o-o|darodar|buttons-for-your-website|resellerclub|blackhatworth|iphone4simulator.com|sashagreyblog|buttons-for-website|best-seo-solution|searchgol|howtostopreferralspam 100dollars-seo|free-social-buttons|success-seo.com|videos-for-your-business.com The reason majority of the websites above do not have org/com/etc is that for these sites I have concluded that there are no other genuine sites with similar site names (or none that I could find) that would send our site traffic. So it is safe to exclude these sites by name only.  For example, there are many sites with adviceforum in their name so to avoid excluding any potentially genuine sites that are called adviceforum, I only exclude the one spam referral I saw in my traffic - adviceforum.com. If you notice that you have referral traffic from addons.mozilla.org but don't actually have an addon on Mozilla, then you should add addons.mozilla.org (more commonly known as ilovevitaly) to the list above in this format - addons.mozilla.org Select Campaign Source in the filter field menu and enter your regex into the filter pattern field. Repeat the process until you have got all four (or more) filters created.   This will help to clean up your Google Analytics data but you have to keep checking for any new spam referrals to add to the exclude filter. You can use advanced segments to view your historical reports without spam referrals. If you need help with any of the above or have further questions, don't hesitate to let me know in the comments.   Further reading: 5 common Google Analytics setup problems How to remove referral spam from historical data

2015-06-25

5 myths of Google Analytics Spam

Google Analytics referral spam is a growing problem, and since Littledata has launched a feature to set up spam filters for you with one click, we’d like to correct a few myths circulating. 1. Google has got spam all under control Our research shows the problem exploded in May – and is likely to get worse as the tactics get copied. From January to April this year, there were only a handful of spammers, generally sending one or two hits to each web property, just to get on their reports. In May, this stepped up over one thousand-fold, and over a sample of 700 websites, we counted 430,000 spam referrals – an average of 620 sessions per web property, and enough to skew even a higher traffic website. The number of spammers using this tactic has also multiplied, with sites such as ‘4webmasters.org’ and ‘best-seo-offer.com’ especially prolific. Unfortunately, due to the inherently open nature of Google Analytics, where anyone can start sending tracking events without authentication, this is really hard for Google to fix. 2. Blocking the spam domains from your server will remove them from your reports A few articles have suggested changing your server settings to exclude certain referral sources or IP addresses will help clear us the problem. But this misunderstands how many of these ‘ghost referrals’ work: they are not actual hits on your website, but rather tracking events sent directly to Google’s servers via the Measurement Protocol. In this case, blocking the referrer from your own servers won’t do a thing – since the spammers can just go directly to Google Analytics.  It's also dangerous to amend the htaccess file (or equivalent on other servers), as it could prevent a whole lot of genuine visitors seeing your site. 3. Adding a filter will remove all historic spam Filters in Google Analytics are applied at the point that the data is first received, so they only apply to hits received AFTER the filter is added. They are the right solution to preventing future spam, but won’t clean up your historic reports. To do that you also need to set up a custom segment, with the same source exclusions are the filter. You can set up an exclusion segment by clicking 'Add Segment' and then red 'New Segment' button on the reporting pages and setting up a list of filters similar to this screenshot. 4. Adding the spammers to the referral exclusion list will remove them from reports This is especially dangerous, as it will hide the problem, without actually removing the spam from your reports. The referral exclusion list was set up to prevent visitors who went to a different domain as part of a normal journey on your website being counted as a new session when they returned. e.g. If the visitor is directed to PayPal to pay, and then returns to your site for confirmation, then adding 'paypal.com' to the referral exclusion list would be correct. However, if you add a spam domain to that list then the visit will disappear from your referral reports... but  still, be included under Direct traffic. 5. Selecting the exclude known bots and spiders in the view setting will fix it Google released a feature in 2014 to exclude known bots and spiders from reports. Unfortunately, this is mainly based on an IP address - and the spammers, in this case, are not using consistent IP addresses, because they don't want to be excluded. So we do recommend opting into the bot exclusion, but you shouldn't rely on it to fix your issue Need more help? Comment below or get in touch!

2015-05-28

Setting up a destination goal funnel in Google Analytics

Destination goal funnels in Google Analytics track how well certain actions on your website contribute to the success of your business. By setting up a goal for each crucial activity you will get more focused reports on how visitors are using your website, and at what stage they are dropping out of the conversion funnel. The first time I tried to set up a destination goal was daunting, but after some practice, I am now seeing valuable information on how well visitors are interacting with our clients' websites. If like Teachable you have different subscription packages, then you might want to track how each subscription is converting. For this, set up the purchase confirmation page of each subscription plan as a goal, with a funnel to get additional insight into where people drop off. Step 1: Create a new goal To set up a destination goal go to Google Analytics Admin settings > View > Goals. Click ‘new goal.’ Step 2: Fill in destination goal details Google has some goal templates that provide set-up suggestions. They will only display if you have set your industry category in property settings. Selecting any of the given templates will only populate the name and type of the goal, but not the conversion details, which are more complicated for some. This is not very useful for me so I will ignore this: select ‘custom’ and click ‘next.’ Goal name Give your goal a descriptive name. You will later see it in various reports in Google Analytics so use whatever makes sense for you. Here I am going to use the name of the subscription plan I am tracking - Basic Subscription. Goal slot ID Goal slot ID is set automatically and you might want to change it if you want to categorise your goals. Select ‘Destination’ and click ‘next step.’ Step 3: define your destination goal Destination type You have a choice between 3 different match types. If you have an exact URL that does not change for different customers (without '?=XXX'), then use ‘Equals to’ for an exact match. If the beginning of your converting URL is the same, but there are different numbers or characters at the end of the URL for various customers, then choose ‘Begins with.’ Use ‘Regular expression’ to match a block of text within the URL. For example, if all your subscriber URLs have 'subscriber_id=XXX' somewhere then type 'subscriber_id=' into the text field. You can also use 'regular expression' if you need to match multiple URLs and know how to use special characters to build regex. One of our favourite tools to test regular expressions is Regex Tester. The match type you select here will also apply to the URLs in the funnel, if you choose to create one. Destination page Destination page is the URL where the conversion occurs. For Teachable, and most other websites that sell something online, the destination is usually a ‘thank you' page that is displayed after successful purchase. You might also have a thank you page for contact forms and newsletter signups, which you would track the same way as a payment thank you page. Here you insert the request URI, which is the URL part that comes after the domain address. It would look something like this: /invoice/paid /thank you.html /payment/success Step 4: Should you set a goal value? (optional) You can set a monetary value to your goal if you want to track how much it contributes. e.g. If the goal is visitors completing a contact form, and you know the average lead generates you £100, then you can put the value at 100. If you are an ecommerce site and want to track exact purchases, then set up enhanced ecommerce tracking instead. Step 5: Should you set up a funnel? (optional) If you have several steps leading up to the conversion, you should set up a funnel to see how many people move through each defined step and where they fall out. If you do not set the first step as 'required', Google Analytics will also track people coming into funnel halfway through. i.e. If the first stage of your funnel is the homepage, then it will still include visitors who land straight on your contact page. Verify Now that you have set up your destination goal, click ‘verify the goal’ to check it works. If all is set up correctly, you should see an estimation of the conversion rate your goal would get. If you do not get anything, then check each step carefully. Once all is well, click ‘create goal’ and check it is working after a few days or a week, depending on how much traffic you get. If you set up a funnel, you will see it in Conversions > Goals > Funnel Visualisation. This is what a typical funnel would look like. Because I did not set the first step as 'required' you can see people entering the funnel at various steps.   Need more help? Get in touch or comment below!

2015-04-06

How to audit your Web Analytics Ecommerce tracking

Most companies will see a discrepancy between the transaction volumes recorded via web analytics and those recorded via internal sales or financial database. This article focuses on how to find and reduce that discrepancy, to give greater credibility to your web analytics data. Following on from our article on common Google Analytics setup problems, we are often asked why Google Analytics ecommerce tracking is not a 100% match with other records, and what is an acceptable level of difference. Inspired by a talk from Richard Pickett at Ensighten, here is a checklist to run through to reduce the sources of mismatch. The focus here is Google Analytics Ecommerce tracking, but it could apply to other systems. In summary, you wouldn’t ever expect there to be a 1:1 match, due to the different paths the two events take over the internet. The general consensus is that anything less than 4% of difference in transaction volumes is good, but could sometimes persist up to 10%. Factors that affect this target rate include how many users have got ad blockers or disable Google Analytics (popular in Germany, for example), what proportion are on mobile devices (which suffer from more network interruptions) and how the purchase thank you / confirmation page is built. So on to the list. 1. Are other Javascript errors on the page blocking the ecommerce event in certain situations? The most common reason for the tracking script not executing in the browser is that another bug on your page has blocked it (see GDS research). The bug may only be affecting certain older browsers (like Internet Explorer 7), and have missed your own QA process, so the best approach is to use Google Tag Manager to listen for any Javascript error events on the confirmation page and send these to Google Analytics as custom events. That way your users do the testing for you, and you can drill into exactly which browsers and versions the bugs are affecting. 2. Is the tracking code as far up the page as it could be? If the user drops their internet connection before the whole page loads then the ecommerce event data won’t get a chance to fire. The best approach is to load the script at the bottom of the <head> element or top of the <body>.  The Google Analytics script itself won't block the page load, and arguably in this one purchase confirmation page, the tracking is more important than the user experience. 3. Is the tracking code firing before all the page data has loaded? The inverse of the previous problem: you may need to delay firing the tracking code until the data is ready. This is particularly an issue if your ecommerce transaction data is ‘scraped’ from the HTML elements via Google Tag Manager. If the page elements in question have not loaded before the ecommerce tracking script runs, then the product names, SKUs and prices will be empty – or returning an error. 4. Is the problem only your ecommerce tracking script or just page tracking is general? It could be that the way you are sending the transaction data (e.g. product name, price, quantity) is the problem, or that the page tracking overall is failing in some cases. You can pinpoint where the problem lies by comparing the pageviews of the confirmation page, with the number of ecommerce events tracked. Caveat: on many sites, there’s another route to seeing the purchase confirmation page, which doesn’t involve purchasing (for example as a receipt of a historic purchase). In that case, you may need to capture a unique purchase event, which only fires when a new purchase is confirmed – but without any information on the transaction or products. 5. Are events from your test site excluded? Most companies will have a development, staging or user acceptance testing server to where the website is tested, and test users can purchase.  Are you blocking the tracking from these test sites? Some possible ways to block the test site(s) would be: Set up sub-domain specific blocking rules in Google Tag Manager (or better) Divert the tracking from your test subdomains to a test Google Analytics account, using a lookup macro/variable Set up filters in the Google Analytics view to exclude 6. Is your tag set with a high priority? Tag manager only. If you use Google Tag Manager and have multiple tags firing on the tracking page it’s possible that other tags are blocking your ecommerce data tag from firing. Under ‘Advanced settings’ in the tag editor, you can set a higher priority number for tag firing; I assume the ecommerce data to Google Analytics is always the first priority. 7. Are any strings in the product name properly escaped? A common problem is apostrophes: if your product name contains a quote mark character, then it will break the following Javascript. See Pete’s bunnies – the strings in yellow are valid, and everything after the stray apostrophe will be misinterpreted. The solution is to run a script across any text field to either strip out the quotation marks or replace any quotes with their HTML equivalent (eg &quot;). 8. Are your quantities all integers? One of our clients was selling time slots, and so had the ‘quantity’ of the ecommerce tracking data equivalent to a number of hours. Timeslots sold in half-hours (e.g. 1.5 hours) were not tracking… because Google Analytics only recognises a quantity which is a whole number, so sending ‘1.05’ will not be recognised as 1. 9. Are any possible ‘undefined’ values handled? It may be that the data on your products is incomplete, and some products that people buy do not have a name, price or SKU. The safest approach is to have some fall-back values in your Javascript tracking code to look for undefined or non-text variables and post a default value to Google Analytics. E.g. If ‘product name’ is undefined then post ‘No product name’, or for price, the default should be ‘0.00’. These will then clearly show up in your Ecommerce Product performance reports and the data can be cleaned up. 10. Are users reloading the page and firing duplicate tracking events? Check whether this is a problem for your site by using our duplicate transactions custom report to see multiple events with the same transaction ID. A solution is to set a ‘has tracked’ cookie after the ecommerce tracking has been sent the first time, and then check whether the cookie is set before sending again. 11. Are users going back to the page and firing the tracking at a later date? The sessions column in the transactionID report in step 9 should give you an idea of whether the problem is repeat page loads in one session, or users revisiting the page in another session. If you see duplicate transaction IDs appearing in other sessions there are a couple of possibilities to investigate: Could users be seeing the page again by clicking on a link to an email, or from a list of historic orders? Are there any back-end admin pages that might link to the confirmation page as a receipt? In both cases, the solution is to have a different URL for the receipt that the one where the ecommerce tracking is fired. If there are any other troubleshooting steps you have found helpful, please let us know in the comments or get in touch!  

2015-03-17

5 common Google Analytics setup problems

Can you rely on the data you are seeing in Google Analytics? If you use it daily in your business you should really give some time to auditing how the data is captured, and what glitches could be lurking unseen. The notifications feature in Google Analytics now alerts you to some common setup problems, but there are more simple ones you could check today. Here are 5 aspects of your Google Analytics account to check now. Are you running the latest Universal Analytics tracking code? Is your overall bounce rate below 10%? Are you getting referrals from your own website? Are you getting ‘referrals’ from your payment gateway? Have you got the correct website default URL set in GA? Are you getting full referring URL in reports? 1. Are you running the latest Universal Analytics tracking code? You may have clicked upgrade in the Google Analytics admin console, but have your developers successfully transferred over to the new tracker code? Use our handy tool to test for universal analytics (make sure you copy your URL as it appears in the browser bar). 2. Is your overall bounce rate below 10%? The 'bounce rate' is defined as sessions of only one page. It’s highly unlikely to be in single digits unless you have a very unique source of engaged traffic. However, it is possible that the tracking code is firing twice on a single page. This double counting would mean Google Analytics sees every single page view as two pages – i.e. not a bounce This is more common on template-driven sites like Wordpress or Joomla, where you may have one tracking script loaded by a plugin – and another pasted onto the main template page. You can check if you have multiple pageviews firing by using the Google Tag Assistant plugin for Chrome. 3. Are you getting referrals from your own website? A self-referral is traffic coming from your own domain – so if you are www.acme.com, then a self-referrals would be appearing as ‘acme.com’. Have a look at the (recently moved) referrals list and see if that is happening for you. This is usually caused by having pages on your website which are missing the GA tracking code, or have it misconfigured. You can see exactly which pages are causing the problem by clicking on your domain name in the list and seeing the referring path. If you are on universal analytics (please use our tool to check) you can exclude these referrals in one step with the Referral Exclusion list.  For a fuller explanation, see the self-referral guide provided by Google. 4. Are you getting ‘referrals’ from your payment gateway? Similar to point 3: if you have a 3rd party payment service where customers enter their payment details, after they redirect to your site – if you are on Universal analytics – they will show up as a new visit… but originating from ‘paypal.com’ or ‘worldpay.com’. You need to add any payment gateway or similar 3rd party services to that referral exclusion list.  Just add the domain name - so PayPal would be 'paypal.com' 5. Have you got the correct website default URL set in GA? When Google Analytics was first set up for your website you may have set a different domain name than what you now use. Or maybe you have switched to run your site on https:// rather than http://. So you need to change the default URL as set up in the admin page. For this go to Admin > Property > Property Settings. Once that is setup correctly, the ‘All Pages’ report becomes a lot more useful – because you can click through to view the actual page using the open link icon. Advanced: Are you getting full referring URL in reports? If you run your website across different subdomains (e.g. blog.littledata.co.uk and www.littledata.co.uk) then it can be difficult to tell which subdomain the page was on. The solution to this is to add the hostname to the URL using a custom filter. See the guide on how to view full page URLs in reports. What other setup issues are you experiencing? Let us know in the comments or by tweeting @LittledataUK.

2015-02-18

Under the hood of Littledata

Littledata tool gives you insight into your customers' behaviour online. We look through hundreds of Google Analytics metrics and trends to give you summarised reports, alerts on significant changes, customised tips and benchmarks against competitor sites. This guide explains how we generate your reports and provide actionable analytics. 1. You authorise our app to access your Google Analytics data As a Google Analytics user you will already be sending data to Google every time someone interacts with your website or app. Google Analytics provides an API where our app can query this underlying data and provide summary reports in our own style. But you are only granting us READ access, so there is no possibility that any data or settings in your Google Analytics will change. 2. You pick which view to report on Once you've authorised the access, you pick which Google Analytics view you want to get the reports on. Some people will have multiple views (previously called ‘profiles’) set up for a particular website. They might have subtly different data – for example, one excludes traffic from company offices – so pick the most appropriate one for management reports. We will then ask for your email so we know where to send future alerts to. 3. Every day we look for significant changes and trending pages There are over 100 Google Analytics reports and our clever algorithms scan through all of them to find the most interesting changes to highlight. For all but the largest businesses, day-by-day comparisons are the most appropriate way of spotting changing behaviour on your website. Every morning (around 4am local time) our app fetches your traffic data from the previous day – broken down into relevant segments, like mobile traffic from organic search – and compares it against a pattern from the previous week. This isn’t just signalling whether a metric has changed – web traffic is unpredictable and changes every day (scientists call this ‘noise’). We are looking for how likely that yesterday’s value was out of line with the recent pattern. We express this as signal bars in the app: one bar means there is a 90% chance this result is significant (not chance), two bars means a 99% chance and three bars means 99.9% certain (less than a 1 in 1000 chance it is a fluke). Separately, we look for which individual pages are trending – based on the same probabilistic approach. Mostly this is change in overall views of the page, but sometimes in entrances or bounce rate. If you are not seeing screenshots for particular pages there are a few reasons why: The website URL you entered in Google Analytics may be out of date Your tracking code may run across a number of URLs – e.g. company.com and blog.company.com – and you don’t specify which in Google Analytics The page may be inaccessible to our app – typically because a person needs to login to see it 4. We look for common setup issues The tracking code that you (or your developers) copy and pasted from Google Analytics into your website is only the very basic setup. Tracking custom events and fixing issues like cross-domain tracking and spam referrals can give you more accurate data – and more useful reports from us. Littledata offers setup and consultancy to improve your data collection, or to do further manual audit. This is especially relevant if you are upgrading to Universal Analytics or planning a major site redesign. 5. We email the most significant changes to you Every day - but only if you have significant changes - we generate a summary email, with the highest priority reports you should look at. You can click through on any of these to see a mobile-friendly summary. An example change might be that 'Bounce rate from natural search traffic is down by 8% yesterday'. If you usually get a consistent bounce rate for natural / organic search traffic, and one day that changes, then it should be interesting to investigate why. If you want your colleagues to stay on top of these changes you can add them to the distribution list, or change the frequency of the emails in My Subscriptions. 6. Every Sunday we look for changes over the previous week Every week we look for longer-term trends – which are only visible when comparing the last week with the previous week. You should get more alerts on a Sunday. If you have a site with under 10,000 visits a month, you are likely to see more changes week-by-week than day-by-day.   To check the setup of your reports, login to Littledata tool. For any further questions, please feel free to leave a comment below, contact us via phone or email, or send us a tweet @LittledataUK.

2015-02-05

6 helpful Google Analytics guides

I've been improving my knowledge of Google Analytics this month but found that documentation provided by Google and other heavy research can be difficult to absorb. So here are 6 guides and tools that I found useful in the last month. How to set up campaign tracking Expertise level: Newbie Social media analytics: How to track your marketing campaigns by Cory Rosenfield. When you run an ad, email or social promotion, you want to see which channel is most effective in acquiring visitors. By gathering this information through tracking your campaigns you will be able to focus on winning strategies and make adjustments to less performing ones. Cory’s how to guide takes you through the basics of how to set up campaign tracking with relevant explanations and practical examples. It’s as easy as it gets. What metadata needs fixing Expertise level: Beginner Introducing the Meta and Rich Snippet Tester by Bill Sebald. This tester from RankTank compares your site’s meta and rich snippet data to what you have in your site’s code. You will be able to see mismatches between how you have set your titles and descriptions against what is actually displayed in search results. Want to make sure rich snippets are working correctly or Google doesn’t replace missing meta tags with something unsuitable? Then this tool is for you. How to do keyword research effectively Expertise level: Intermediate Keyword research in 90 minutes by Jeremy Gottlieb. Keyword research for improved content targeting can take a lot of time but it doesn’t have to. Jeremy’s plan splits it into a 4-stage process, full of handy tips on how to spend your time effectively. Especially useful for when planning topics for your blog posts and finding words that are most relevant to include in your product descriptions. Setting up alerts for site errors Expertise level: Intermediate Google Analytics custom alerts which you must always use by Himanshu Sharma. How can you find errors and problems on your website with minimum manual labour? Set up custom alerts in your Google Analytics account with Himanshu's guide. You can create notifications for tracking and shopping cart issues, and any unusual changes in your bounce rate and traffic. How to improve multiscreen experience Expertise level: Advanced Enabling multiscreen tracking with Google Analytics by James Rosewell. This step by step guide by James shows how to get better data on the use of your site across various mobile devices. You will be able to make informed decisions on optimising your site whilst taking into consideration screen sizes and layouts. This means improved experience for customers on bigger smartphones and smaller tablets. Source: Infinium.co What were the different variables again? Expertise level: Advanced Variable guide for Google Tag Manager by Simo Ahava. Variables in Google Tag Manager can be powerful, once you get to grips with them. Simo's comprehensive guide is a useful reference that covers everything you need to know from technical details to set ups and debugging. Source: SimoAhava.com Need some help with Google Analytics? Get in touch with our experts!

2015-01-30

What's new in Google Analytics 2014

Google has really upped the pace of feature releases on Analytics and Tag Manager in 2014, and we’re betting you may have missed some of the extra functionality that’s been added. In the last 3 months alone we’ve counted 11 major new features. How many have you tried out? Official iPhone app. Monitor your Google Analytics on the go. Set up brand keywords. Separate out branded from non-brand search in reports. Enhanced Ecommerce reporting. Show ecommerce conversion funnels when you tag product and checkout pages. Page Analytics Chrome plugin. Get analytics for a particular page, to replace old in-page analytics. However, it doesn’t work if you are signed into multiple Google Accounts. Notifications about property setup. Troubleshoot common problems like domain mis-matches. Embeded Reports API. So you can build custom dashboards outside of GA quickly. Share tools across GA accounts. Now you can share filters, channel groupings, annotations etc easily between views and properties Tag Assistant Chrome plugin. Easily spot common setup problems on your pages using the Tag Assistant. Built-in user tracking. See our customer tracking guide for the pros and cons. Import historic campaign cost and CRM data (premium only). Previously, imported data would only show up for events added after the data import. Now you can enter a ‘Query Time’ to apply to past events, but only for Premium users. Get unsampled API data (Premium only - developers). Export all your historic data without restrictions Better Management API (for developers). Set up filters, Adwords links and user access programmatically across many accounts. Useful for large companies or agencies with hundreds of web properties.

2014-07-21

Pulling Google Analytics into Google Docs - automated template driven reporting

The Google Docs library for the analytics API provides a great tool for managing complex or repetitive reporting requirements, but it can be tricky to use. It would be great if it was a simple as dropping a spreadsheet formula on a page, but Google’s library stops a few steps short of that - it needs some script around it. This sheet closes that gap, providing a framework for template driven analytics reports in Google Docs. With it you can set up a report template, and click a menu to populate it with your analytics results and run your calculations - without needing to write a line of script - the code is there if you want to build on it, but you can get useful reports without writing a line of script. Prerequisites While you don't have to write code to use this, there are some technical requirements. To get the most out of it you'll need to have: your Google analytics tagging and views set up familiarity with Google’s reporting API familiarity with Google Docs spreadsheets - some knowledge of Google apps scripting is an advantage If you are looking for something more user-friendly or tailored to your needs, contact us and book a consultation to discuss - we can help with your analytics setup and bespoke reporting solutions. Getting started Setting this up takes a few steps, but you only need to do this once: Open the shared Google spreadsheet Make a copy Enter a view ID in the settings sheet - get this from the Google Analytics admin page. Authorise the script Authorise the API - in the API console - this is the only time you need to go into the script view using Tools|Script Editor Once in script editor select Resources|Advanced Google services On the bottom of the Advanced Google services dialogue is a link to the Google Developers Console, follow this and ensure that Google analytcs API is set to On You're done. You can go back to the spreadsheet and run the report (on the Analytics menu). From now on all you need to do is tweak any settings on the template and run the report.   Setting up your own report template You can explore how the template works using the example. Anywhere you want to retrieve value(s) from Google Analytics, place this spreadsheet function on the template: = templateShowMetric(profile, metric, startdate, enddate, dimensions, segment, filters, sort, maxresults) This works as a custom spreadsheet function, for example =templateShowMetric(Settings!$B$2,$B7,Settings!$B$3,Settings!$B$4,$C7,$D7,$E7,$F7,$G7) Note that in the example, several of the references are to the settings sheet, but they don't have to be, you can use any cell or literal value in the formula - it's just a spreadsheet function. To get the values for the API query, I'd suggest using Google’s query explorer. To set this up for a weekly report, say, you would have all the queries reference a single pair of cells with start and end dates. Each week you would change the date cells run the report again - all queries will be run exactly as before, but for the new dates. Using spreadsheet references for query parameters is key. This opens up use of relative and absolute references - for example if you need to run the same query against 50 segments, you list your segments down a column, set up segment as a relative reference, and copy the formula down spreadsheet style. You can use this to do calculations on the sheet and use results in the analytics API, for example you might calculate start and end dates relative to current date. Future posts will cover setting up templates in more detail. Under the hood The templateShowMetric function generates a JSON string. When you trigger the script, the report generator copies everything on the template to the report sheet and: runs any analytics queries specified by a templateShowMetric function removes any formulas that reference the settings sheet (so you can use the settings sheet to pass values to the template, but your reports are not dependant on the settings staying the same)

2014-04-21

Analytics showing wrong numbers for yesterday's visits

We've noticed a few issues with clients using Universal Analytics this last month, when visits for the last day have been double the normal trend. It then corrects itself a few hours later - so seems to be just a blip with the data processing at Google. Others have noticed the same problem. The temporary fix is to only generate reports with time series ending the day before yesterday. i.e. ignore yesterday's data. Now Google have officially acknowledged the problem Looking forward to seeing that one fixed!

2014-04-15

Measuring screen resolution versus viewport size

There’s a difference between the ‘screen size’ measured as standard in Google Analytics and the ‘browser size’ or ‘browser viewport’. Especially on mobile devices, there are pitfalls comparing the two. Browser viewport is the actual visible area of the HTML, after the width of scroll bars and height of button, address, plugin and status bars has been allowed for. Desktop computer screens have got much bigger over the last decade, but browser viewports (the visible area within the browser window) are not. The CSS tricks site found only 1% of users have their browser viewing in the full screen. While only 9% of visitors to his site had a monitor less than 1200px wide in 2011, around 21% of users have a browser viewport of less than that width. Simply put, on a huge monitor you don’t browse the web using your full screen. Therefore, 'screen resolution' may be much larger than 'viewport size'. The best solution is to post browser viewport size to GA as a custom dimension. P.S. Google Analytics does have a feature within In Page Analytics (under Behaviour section) to overlay Browser Size, but it doesn’t work for any of the sites I look at.

2014-04-14

How many websites use Google Analytics?

Google Analytics is clearly the number one web analytics tool globally. From a meta-analysis of different surveys, we estimate it is currently installed on over 50% of all websites or 80% of operational websites using any kind of analytics tracking. We looked at the following sources for this chart: Datanyze survey of Alexa top 1m sites (04/2014) BuiltWith survey of all websites (04/2014) MetricMail survey of Alexa top 1m sites Pingdom survey of Alexa top 10k sites (07/2012) W3Techs survey of their own sites (04/2014) LeadLedger survey of Fortune 500 sites (04/2014)

2014-04-10

What's included in Analytics traffic sources?

The Channel report in Google Analytics (under 'Acquisition' section) splits out into 6 or more types of visit channel: Direct Where a visitor has: typed the URL into the address bar clicked on a link which is NOT in another web page (e.g. in a mobile app) visited a bookmarked link Organic Search All visits from search engines (i.e. Google, Bing, Yahoo) which were not an advertisement. You used to be able to filter out people searching for your brand (which are more like Direct visits), but now the search terms are not provided. Paid Search Visits from search engines where the visitor clicked on an advert. Referral Where a visitor has clicked on a link in another website (not your own domain), but not including search engines or social networks. Social Networks Specifically links from known social network websites (including Facebook, Twitter, LinkedIn etc) Email From links tagged as medium = 'email'. Your email software needs to be configured correctly to add this tag. Display Links tagged as 'display' or 'cpm'. FAQs Can I change the channel groupings? Yes, you can change this under Admin .. (Selected View).. Channel Grouping. But we recommend you don't do this for your default view, as you won't be able to compare the historical data.

2014-03-30

Complete picture of your ecommerce business

From marketing channels to buying behaviour, Littledata is the ultimate Google Analytics toolbox.

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