Category : Analytics Setup
How to set up Enhanced Ecommerce tracking via Google Tag Manager
Enhanced Ecommerce (EEC) is a Google Analytics plug-in that provides merchants with better insights for the shopping behavior of users. Enhanced Ecommerce tracking requires your developers to send lots of extra product and checkout information in a way that Google Analytics can understand. So why use it? Why use Enhanced Ecommerce? The main benefit of EEC over standard ecommerce implementation is the sheer number of valuable reports merchants have access to with EEC. Not only that, but merchants can segment data based on ecommerce events — which users visited your product pages, where the customer journey hit a roadblock (e.g. a customer pondered a product but didn’t add it to cart, etc.) or which steps of the checkout process a user abandoned their cart. Ultimately, this kind of data helps merchants zoom in on their sales funnel and alter the parts of the process that don’t lead to conversion. [subscribe] Enchanced Ecommerce implementation is no small feat, but it also depends on a number of factors — the size of your store, the number and type of Google Analytics custom dimensions you need to add, etc. Without question, Google Tag Manager is the simplest and best way to enable Enhanced Ecommerce in Google Analytics. If you already use Google Tag Manager (GTM) to track page views, you must send ecommerce data via Google Tag Manager. If you don’t already use GTM, it’s simple to set up: just activate EEC within your Google Analytics tags and use a dataLayer as an ecommerce data source. Just make sure the dataLayer contains all ecommerce data. 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 listing view Product detail view Add to cart event Remove from cart event Checkout step 1 (views the checkout page) Checkout step 2 etc – whatever registration, shipping or tax steps you have Purchase confirmation Refund Step 5 Send the data to Google Analytics using the Data Layer. Instruct the tags to look into the Data Layer and pull the key-value pairs from the eCommerce object pushed most recently into dataLayer by selecting the correct Google Analytics variable. Step 6 This step involves checking the setup. After you have configured everything in place, you'll need to check your entire. What you should be looking for is: Are all the keys configured in the dataLayer.push() getting picked up and being sent to Google Analytics? Is the payload length too long? Is there a risk of data duplication with some hits? To debug these, you really only need three tools: GTM's own Preview mode, the Google Analytics Debugger browser extension, and Google Chrome browser's DevTools. Yes, there are plenty of other tools you can use, but these have proven to be more than enough in my own experience. Wrapping up Need some more help? Get in touch with our lovely team of Google Analytics experts and we'd be happy to answer any questions! At Littledata, our Google Analytics connection is the easiest way for you to automate GA for ecommerce sites. With the connection, you also get: Smart audits to check for accurate tracking Seamless connections with apps like ReCharge and CartHook Benchmarks against over 12,000 ecommerce sites Raw data that remains available in Google Analytics Shopify tracking you can trust consistently You can also try our Google Analytics app for Shopify free for 30 days.
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?
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!
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.
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
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 [subscribe] 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.
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. [subscribe] 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!
How to set up demographics tracking in Google Analytics (VIDEO)
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. [embed]https://www.youtube.com/watch?time_continue=5&v=PAeCubNxoKI[/embed] 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. [subscribe] 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.
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