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

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

2018-10-25

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

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

2018-10-16

Ecommerce trends at Paris Retail Week

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

2018-10-09

What's the real ROI on your Facebook Ads?

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

2018-09-20

Intro to the Littledata app (VIDEO)

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

by Ari
2018-08-14

How auditing Google Analytics can save you money

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

2018-08-01

CartHook integration for tracking one-page checkouts and upsells

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

by Ari
2018-07-24

The World Cup guide to marketing attribution

It’s World Cup fever here at Littledata. Although two of the nationalities in our global team didn’t get through the qualifiers (US & Romania) we still have England and Russia to support in the next round. And I think the World Cup is a perfect time to explain how marketing attribution works through the medium of football. In football (or what our NYC office calls 'soccer'), scoring a goal is a team effort. Strikers put the ball in the net, but you need an incisive midfield pass to cut through the opposition, and a good move starts from the back row. ‘Route one’ goals scored from a direct punt up the pitch are rare; usually teams hit the goal from a string of passes to open up the opportunity. So imagine each touch of the ball is a marketing campaign on your site, and the goal is a visitor purchasing. You have to string a series of marketing ‘touches’ together to get the visitor in the back of the net. For most ecommerce sites it is 3 to 6 touches, but it may be more for high value items. Now imagine that each player is a different channel. The move may start with a good distribution from the Display Ads defender, then a little cut back from nimble Instagram in the middle. Facebook Ads does the running up the wing, but passes it back to Instagram for another pass out to the other wing for Email. Email takes a couple of touches and then crosses the ball inside for AdWords to score a goal – which spins if off the opposing defender (Direct). GOAL!!! In this neat marketing-football move all the players contribute, but who gets credit for the goal? Well that depends on the attribution model you are using. Marketing attribution as a series of football passes Last interaction This is a simplest model, but less informative for the marketing team. In this model the opposing defender Direct gets all the credit – even though he knew nothing about the end goal! Last non-direct click This is the attribution model used by Google Analytics (and other tools) by default. In this model, we attribute all of the goal to the last campaign which wasn’t a Direct (or session with unknown source). In the move above this is AdWords, who was the last marketing player to touch the ball. But AdWords is a greedy little striker, so do we want him to take all the credit for this team goal? First interaction You may be most interested in the campaign that first brought visitors to your website. In this model, Display ads would take all the credit as the first touch. Display often performs best when measured as first interaction (or first click), but then as a ‘defender’ it is unlikely to put the ball in the net on its own – you need striker campaigns as well. Time decay This model shares the goal between the different marketing players. It may seem weird that a player can have a fraction of a goal, but it makes it easy to sum up performance across lots of goals. The player who was closest to the goal gets the highest share, and then it decays as we go back in time from the goal. So AdWords would get 0.4, Email 0.5 (for the 2 touches before) and Instagram gets 0.1. [subscribe] 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

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