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

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

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

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

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

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 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

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

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

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 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

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

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

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

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 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

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

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

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

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

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

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

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 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

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

Complete picture of your ecommerce business

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

Get started