How Google Analytics dropping Service Provider & Network Domain info affects your Shopify tracking
On February 4th, Google Analytics removed two standard dimensions from reporting – Service Provider and Network Domain – and replaced them with the dreaded (not set) label. Although there’s been cries of anguish from some analytics companies, my view is that Google has sound reasons to remove the dimensions – and there are ways around many of the limitations. Shortly after Google added the above alert to the hover tip within the Google Analytics interface, data in reports stopped reporting the information. Moving forward (and unless Google reverses course on this decision in the coming days), you’re going to start seeing (not set) under the Service Provider and Network Domain dimensions: What are Service Provider and Network Domain? Every time a visitor is tracked on your website, Google captures the IP address in order to geolocate the user (generating Country, State and City dimensions). It also does a reverse DNS lookup to see which networks this IP address is linked with. Service Provider is either the ISP (for a consumer) or the corporate network (for a business internet user). Network Domain is the main domain by which the traffic was routed (e.g. Verizon, Amazon AWS etc.) So why did Google drop them? There’s been no official announcement from Google, but it’s likely to be a combination of three factors. CCPA Storing of any California consumer’s network details is a violation of the California Consumer Protection Act (CCPA). This is much more specific than previous regulations, and as a California-headquarted company, Google wants to stay safely within the law. [tip]Here's everything Shopify merchants need to know about CCPA compliance[/tip] Fingerprinting Even if the Service Provider itself is not identifiable to any individual, it may well be used to generate a unique fingerprint for an individual user, in combination with other dimensions in Google Analytics (browser version, operating system, screen size, pages visited, etc.). Fingerprinting is user identification by covert means, and as such Google also wants to clamp down on in. Lack of usage In ten years of advising high-growth businesses on Google Analytics setup, I've never seen a good use for these reports. Google tracks what are the most common reports used, and apparently they were already flagged for deprecation based on lack of usage. How the change affects your Shopify tracking Some analytics companies (and agencies) are worried about this change for a few reasons : Reason 1: Service Provider and Network Domain dimensions helped filter out spam and bot traffic, which meant less legwork for those doing the reporting. It was easier to sniff out bounce rates that looked too high (or low) to be "real". Take the screenshot below — which Service Provider do you think is probably legitimate and which one is probably a bot/spam? In short, most analytics companies would say before this change, it was easy to uncover bots/spam, and now it's not. Reason 2: Some larger stores used Server Provider and Network Domain dimensions as a quick & easy way to filter out internal traffic from monthly reports. And unfortunately, this change has killed these dimensions' ability to filter. Reason 3: Companies such as Leadfeeder and Leadberry used the Network Domain, plus a database of which companies and people used that domain, to offer a list of sales leads who visited your site. They can mostly work around the limitations by getting their clients to push another tracking script on the site, and looking up IP address themselves — which is OK, providing your website visitors are aware you are doing this in your terms and conditions. In other words, if you're filtering your GA views by network provider, it's possible you'll see internal traffic in your reporting this month. And it might not be obvious, since it's mixed in with all of your site traffic. That is, unless you look at the GA data with better tracking. How can you work around this? For those that really need the lost dimensions there are two solutions: Use Google Tag Manager and an IP lookup service to pass network onto Google Analytics as a custom dimension. Use the recently launched ipmeta.io service to do this.* What now? For some stores using Google Analytics, this sudden change will go unnoticed and won't really impact reporting. For stores that rely on these dimensions to filter out bots/spam and internal traffic for more accurate reporting, the loss of these dimensions will have somewhat of a negative impact. Of course, we'll continue to monitor these changes (and any other surprises that Google may have in store). Don't pay too much attention to the initial outcry — every change has a solution. Littledata users can rest easy — with our Google Analytics app for Shopify, your tracking won't be impacted by these dimensions. You'll continue to see accurate data for better reporting. 🚀 *The current version of ipmeta.io is free and will remain free. The premium version will add more custom dimensions with data on the company behind the visit (if its not an ISP or spider). For example, adding dimensions such as industry codes, company size, revenue, etc. In comparison to similar services, ipmeta.io will be much (about 10x) more affordable to cater to the SMB segment.
What's new in our Shopify apps for Google Analytics and Segment
Littledata is always improving. Over the last 6 months, we’ve worked on numerous features to enhance the accuracy and availability of our ecommerce data analysis for Shopify merchants. Littledata's smart connections make it easy to get accurate data in Google Analytics or Segment. The changes below affect both of our Shopify apps (Segment and Google Analytics for Shopify), marking the biggest major update to our Shopify tracking script and server-side tracking since we released V8 last year. [tip]Check out our release notes for regular updates![/tip] Attribution for email marketing signups In order to provide enhanced email attribution, we've linked 'customer created' and 'customer updated' events back to the original source. Stores building a customer email list can now analyze where those email signups originally came from. By linking customer creation or update events on Shopify’s servers to the original campaign or referrer to the store, Littledata customers can now accurately track the source of email signups. Merchants can now also segment these signup events by whether or not the customer opted into marketing. Checkout steps Tracking checkout steps is essential for ecommerce analytics, but Shopify's native tracking is incomplete and inaccurate. Littledata's Shopify connections solve checkout tracking issues automatically. With recent updates, we’ve made the tracking of checkout steps even more reliable, coping with situations where a user is already logged in, or abandons the cart and then returns later. [note]Did you know by sending the data to Google Analytics, you can easily track your Shopify payments gateway during checkout?[/note] With the help of the full Enhanced Ecommerce specification, you can: track exactly which products follow in each step calculate the value of opportunities to improve each step [subscribe] ReCharge connection, recharged As subscription ecommerce sites continue to scale, they need even more detailed data about the user journey, especially lifecycle events. [tip]Do you trust your subscription tracking in Shopify? Learn how to get accurate tracking for repeat orders[/tip] With our new ReCharge v2 connection, subscription stores can now track the full subscription lifecycle including: subscription updates cancellations failed payments product edits customer profile / information edits [note]See the full slate of ecommerce events you can now track with ReCharge v2[/note] Geolocation of server-side events Stores need accurate information on the location of their customers to retarget campaigns around top-performing regions or cities. The extra events above, plus all the standard order data, are sent from our servers in Virginia, US. But, of course, in your analytics, you want to see them linked to the customers' real location. We now have a belt-and-braces solution for correctly geolocating customer events, passing on the browser's IP address where known, or else sending the shipping address (default customer address) to Google Analytics as a 'Geographical Criteria ID'. CartHook and Bold Cashier We've always supported other checkouts for Shopify, as we know some stores need flexibility with payment, upsell and recurring billing options. And for the most popular checkout solutions, we're always looking at ways to provide advanced tracking automatically. So in the past 6 months Littledata has launched more robust integrations with CartHook and Bold Cashier. New Google Optimize connection Google Optimize is a powerful A/B testing and personalization platform used within and beyond ecommerce. [note]Connect your Shopify store to Google Optimize to test your product pages, store content and messaging with 100% accuracy.[/note] Now, we have an out-of-the-box setup for Shopify, including an anti-flicker snippet. And coming soon... In Q1 2020, we're working on connections for Iterable's email marketing platform, plus a more consistent way of handling Segment's anonymous ID for stores which don't use Google Analytics. Is there something you're eager to see in Littledata? We're always happy to hear feature suggestions — get in touch with our team today!
How to calculate customer lifetime value in Google Analytics for Shopify subscription stores
Many of Littledata's subscription customers come to us with a similar problem: how to calculate return on advertising spend, considering the varying customer lifetime value (CLV) of subscription signups. Calculating marketing ROI for subscription ecommerce is a big problem with a number of potential solutions, but even the initial problem is often misunderstood. In this post, I'll break down what the problem is and walk through two proven solutions for getting consistent, reliable customer lifetime value reporting in Google Analytics. [tip]Get accurate tracking for repeat orders with the ultimate Shopify ReCharge guide.[/tip] What is customer lifetime value? I work with all kinds of subscription ecommerce businesses: beauty boxes, nutritional supplements, training courses and even sunglasses-by-the-month. All of them want to optimise customer acquisition costs. The common factor is they are all willing to pay way MORE than the value of the customers' first subscription payment. Why? Because they expect the customer to subscribe for multiple months. But for how many months exactly? That's the big question. Paying for a marketing campaign which bring trial customers who cancel after one payment – or worse, before the first payment – is very different from paying to attract sticky subscribers. A marketing director of a subscription business should be willing to pay WAY more to attract customers that stay 12 months than customers who only stay one month. 12 times more, to be precise. So how do we measure the different contribution of marketing campaigns to customer lifetime value? In Google Analytics, you may be using ecommerce tracking to measure the first order value, but this misses the crucial detail of how long those shoppers will remain subscribers. [subscribe] As you can see below, with lifetime customer value segments, we can: make more efficient use of media tailor adverts to different segments find new customers with lookalike audiences and target loyalty campaigns There are two ways for a marketing manager to see this data in Google Analytics: the first is a more difficult, manual solution. The other is an easier, automated solution that ties recurring payments back to their original campaigns. Manual solution: segment orders and assign a lifetime value to each channel It's possible to see the required data in GA by manually segmenting orders and assigning a lifetime value to each channel. For this solution you'll need to join together: (a) the source of a sample of first orders from more than a year ago, by customer number or transaction ID and (b) the CLV of these customers The accuracy of the data set for A is limited by how your Google Analytics is set up: if your ecommerce marketing attribution is not accurate (e.g. using Shopify's out-the-box GA scripts) then any analysis is flawed. You can get B from your subscription billing solution, exporting a list of customer payments (and anonymising the name or email before you share the file internally). To link B to A, you'll need to either have the customer number or transaction ID of the first payment (if this is stored in Google Analytics). Then you can join the two data sets in Excel (using VLOOKUP or similar function), and average out the lifetime value by channel. Even though it's only a sample, if you have more than 100 customers in each major channel it should give you enough data to extrapolate from. Now you've got that CLV by channel, and assuming that is steady over time, you could import that back into Google Analytics by sending a custom event when a new customer subscribes with the 'event value' set as the lifetime value. The caveat is that CLV by channel will likely change over time, so you'll need to repeat the analysis every month. If you're looking to get away from manual solutions and excessive spreadsheets, read on... Calculating customer lifetime value Subscription ecommerce is huge, and continuing to grow around the world. But Shopify Plus stores (in particular) selling products by subscription have a unique problem: how do I link the recurring customer payments back to the marketing campaign or channel that led to them subscribing? Unlike standard ecommerce, it’s not enough to track the payment upon a first signup. It is the customer lifetime value (CLV) which counts in any marketing calculation. “The great thing about a subscription businesses is that you don’t have to rely on one-time purchases.”-- Rob Hoxie, co-founder of Tiege Hanley (read the full interview). I've already laid out the time-intensive manual solution for subscription tracking. But before we get into the automated solution, let's discuss why you need to track customer lifetime value in the first place (and the various problems with tracking it in Google Analytics). I'll also get into Littledata's solution, which will work for you whether you’re using Bold or ReCharge as your Shopify subscription app. [note]Check out my ReCharge talk in full from the ChargeX conference in September 2019, where I discussed a similar topic.[/note] Why you must track lifetime value Let’s imagine a simple store selling a single subscription product for $50 per month. On average, it takes them $70 to acquire a new subscriber via Google Ads. Now let’s think about 3 fictional customers of that store: Claire, Eric and Luke. These 3 offer very different values to the business, and differentiating them (or the customer segment they represent) in Google Analytics is critical to business success. In the graphic below, you’ll see that Claire is costing the business money, as her lifetime value is less than the cost of acquiring her as a customer. Eric pays something, possibly buying twice from the business, but still has a short ‘lifetime’. Only Luke continues for a reasonable time (and may continue subscribing). Which of them brings the company profit? The answer is only Luke. Many subscription businesses only make money on customers who subscribe for over 3 months — but the loyal customers are immensely valuable, and may go on to pay for years. This also speaks to the immense value of customer retention among Shopify Plus stores. The problem with subscription analytics Measuring and attributing this lifetime value is hard. The events happen in three different places, and need to be linked back to give a net value to be properly tracked: Unfortunately, by default, the customer who chooses a subscription in Shopify may not be linked to the user that actually commits to a payment in ReCharge. In other words, transactions are often left improperly tracked and attributed; in many cases, the refund or cancellation is not tracked at all. Take this Google Analytics screenshot from one of our customers (before fixing): Although 19% of the traffic comes from paid search, none of the ecommerce transactions are attributed to paid search. Instead, they are linked to a totally different group of users from ‘direct’ traffic. Littledata’s solution Littledata’s Shopify app combines the three steps in the customer lifecycle to bring together a unified view of the customer in Google Analytics. Once that customer has gone through the checkout, we can also track each subsequent recurring payment back to that same pre-checkout user journey (including the marketing campaign from which they came), along with other custom dimensions in Google Analytics to help you analyse lifetime value. How subscription stores can use data to optimise marketing campaigns Once you have recurring payments feeding into Google Analytics, you can begin segmenting your marketing channels by those that bring a higher quality customer — ones that nearly or exactly match your target personas. By using Littledata’s smart PPC connections for Facebook Ads and Google Ads, you can also pull in advertising costs to calculate Return on Advertising Spend (ROAS). So do you know which marketing channels bring you customers with the highest lifetime value? Maybe you have one stand-out channel that brings the majority of Lukes, and some that only bring you Claires. A better, automated solution: tie recurring payments back to the original campaign(s) What if you could import the recurring payments into Google Analytics directly, as they are paid, so the CLV is constantly updated and can be segmented by campaign, country, device or any other standard GA dimension? This is what our Google Analytics connection for ReCharge does. Available for any store using Shopify or Shopify Plus as their ecommerce platform and ReCharge for recurring billing, the smart connection (integration) ties every recurring payment back to the campaigns in GA. Then, if you also import your campaign costs automatically, you can do the Return on Investment (ROI) calculations directly in Google Analytics, using GA's new ROI Analysis report (under Conversions > Attribution), or in your favourite reporting tool. [note]Now, with a revamped ReCharge connection — ReCharge v2 — you can track subscription lifecycle events with ease![/note] Do you have a unique way of tracking your marketing to maximise CLV? Are there other metrics you think are more important for subscription retailers? Littledata's connections are growing. We'll be launching integrations for other payment solutions later this year, so let us know if there's a particular one you'd like to see next. Quick recap Customer Lifetime Value (CLV) is the one metric that matters for a subscription business To scale using metrics like Return on Advertising Spend (ROAS), you need to have accurate CLV calculations first Getting that data into Google Analytics allows you to segment by marketing channel or campaign Littledata’s advanced Google Analytics integration for ReCharge stores provides an easy way to stitch the data together
Do App + Web Google Analytics properties work with Shopify?
The short answer is no: until these new properties come out of Beta testing and support Enhanced Ecommerce reporting, we can’t recommend you use them with your Shopify store. This past July, Google brought out a public beta forcombining website and native mobile app tracking called ‘App + Web’ properties. These are technically a big step forward for Google Analytics (GA), combining some of the flexible, event-based tracking from Firebase Analytics for mobile with the deeper reporting and journey analysis tools in GA. However, we believe these are not yet suitable for use with ecommerce stores because they miss the Enhanced Ecommerce reporting that makes GA so powerful for Shopify analysis. Where’s this new reporting going? What Google appears to be doing is rebuilding Google Analytics from the ground up, so it’s fair to call it ‘GA v2’. Some of the long-term problems they are addressing include: Flexible ways of building funnels based on a series of events and page views (i.e. no more limitation on ONLY page views or event funnels) More powerful ways to build ‘audiences’ used in other Google tools (such as Google Ads), instead of the clunky and error-prone advanced segment builder True event-level views of the data with ‘stream view’ Enabling events to be sent with many properties, rather than just event ‘action’ and ‘label’ See Krista Seiden’s excellent post for more information on the features. What’s next with App + Web? We hope that (eventually) ecommerce events are added to that list, but it might be in a more flexible way to how GA v1 copes with events. We’ll keep watching the progress, as this ‘App + Web’ setup is going to be the future it seems.
Black Friday discounting increases next season’s purchasing
Black Friday Cyber Monday appears to be big business for ecommerce merchants. But what happens after the promotions? I knew Black Friday had reached ‘late adopter’ stage when a company I’d bought fencing panels from – fencing panels – emailed me their holiday season promotions. But the real question is this: will all these promotions actually drive customer loyalty, or only attract bargain hunters? Looking at the data At Littledata, we looked at aggregate data from 143 retailers who participated most in 2016 Black Friday, versus 143 retailers who did not. For the first 23 days of November 2017 – before Black Friday – the median year-on-year increase in sales was 13% for those pushing discounts the previous year, versus only 1% growth for those avoiding Black Friday discounting *. Our conclusion is that retailers who discounted most heavily on Black Friday 2016 saw a lasting benefit in extra sales a year after the sales period. However, we don’t know whether these extra sales were profitable enough to pay for the seasonal promotions. Another possible explanation is that higher-growth retailers are more active in marketing Black Friday, but in either event the discount season has done them no harm over the following year. Looking at 2016, it seems Black Friday was bigger than the year before for our cohort of 270 UK retailers – but at the expense of sales later in the season. Yet in the UK, we are not close to US levels of hysteria yet, where a much greater proportion of the last quarter’s sales are done on that weekend. What sectors does Black Friday affect? The other interesting question is what sectors does Black Friday affect? It may be a surprise that the biggest boost of over 100% average increase in sales comes for Health & Beauty stores, whereas technology and computer stores saw an average boost of 40% for the week. The graph below shows the difference with the average sales volumes in November & December 2016, by sector, for 3 selected weeks: Perhaps I shouldn’t have been surprised by those fencing panels: business and industrial sites saw a big boost too! Interested in tracking online sales activity for your own site this holiday shopping season? Littledata's ecommerce analytics software provides accurate data and automated reporting to help you track promotions and drive conversions and customer loyalty. [subscribe] *The statistical detail I took a group of 573 retailers we have tracked for at least 2 years, and looked at the ratio of Black Friday weekend sales (Friday, Saturday, Sunday, Monday) to the 2 month average for November and December. Those in the top quartile (trading 2.6 times above average during the Black Friday season) were deemed to have participated; those in the bottom quartile, showing a dip in trading over that weekend were deemed not to have participated. I then looked at the year-on-year growth in revenue between November 2016 (first 23 days) and the same period in November 2017, for the discount versus non-discount group. A t-test between the groups found an 18% probability that the two groups had the same mean, not allowing us to dismiss the null hypothesis. [note]This Black Friday ecommerce strategy post was originally published in November 2017 but has since been updated.[/note]
My ReCharge talk: which marketing channels bring you profitable customers?
In September 2019, our team attended #ChargeX in LA, an annual conference for the Shopify ReCharge community. There were agencies, app developers, and a ton of Shopify Plus stores using ReCharge to power their subscription ecommerce. Day 1 of the conference was mainly focused on partner agencies looking to build off the ReCharge platform, while day 2 was designed for Shopify merchant success. We saw plenty of familiar names and faces, but perhaps the highlight of the weekend was sharing my talk which orbited around lifetime value: which marketing channels bring you profitable customers? [note]Now, with a revamped ReCharge connection — ReCharge v2 — you can track subscription lifecycle events with ease![/note] Check out my full talk below. If you have questions, be sure to get in touch with support or our team of Google Analytics experts. If you have questions about our advanced Google Analytics connection for ReCharge stores, we're here to answer those too. Enjoy the talk! https://youtu.be/ubFDTY1M6HU Main points How agencies can measure which marketing channels bring in the most profitable customers Current fragmentation for marketing attribution – customer purchase data and browsing data in seperate silos Overview of the many channels that all funnel into calculating lifetime value from pre-checkout through subscription renewal How to predict which visitors will be high value, and experiment with what content you push to them Three levers that impact Customer Lifetime Value (LTV) [tip]Get accurate tracking for repeat orders with the ultimate ReCharge guide for Shopify and Shopify Plus.[/tip]
How Shopify Plus stores can set up multi-currency reporting in Google Analytics
An increasing number of ecommerce brands are using Shopify Plus to manage international stores and sell in multiple currencies. Since there are a few setups you may have, here are my recommendations in each case to get the most versatile reporting in Google Analytics (GA). For a single store accepting multi-currency Littledata’s enhanced Shopify tracking already handles multi-currencies at all stages of the shopping journey. We recommend you have just a single web property and single view in Google Analytics. Our audit checks will make sure the currency you have set up for this view matches your Shopify store currency. For multiple stores, with different default currencies (GA standard) I recommend you set up a single web property, but with different Google Analytics views for each country store. You can create one ‘All countries’ view in the same currency as your company’s default reporting, and then each country store would need filters set up to include traffic only from that country. Here's how to set up the filters: Go to the Admin section in Google Analytics, and click Filters under the View settings Then click to ADD Filter Then set up a filter to include traffic only from this store’s hostname Then Save the filter This could be tricky if you use a third-party checkout, where the hostname will be shared across stores (see below). Each country view in Google Analytics would have the same currency and timezone set as the Shopify store, so you can compare like-for-like orders. In Littledata, you would create different accounts for each country store and be able to audit and benchmark your stores' performances separately. Multiple stores, with different default currencies (GA 360) With GA 360, you have the added flexibility of being able to setup a roll-up property, combining ecommerce events from multiple properties. So you have two options: Go with the same solution as for GA Standard. The advantage here is that with a single web property, you can easily track visitors as they move between your country stores (i.e. if users are directed to a country after seeing a marketing campaign, you can still attribute the marketing campaign as they move to a different store). Set up a separate web property for each country store, and roll-up into a group property. The advantage here: your data is clean, but you can’t track cross-country visitors. Option 2 is going to work better if you leverage third party checkouts like ReCharge Payments or Bold Cashier, where it may be hard to filter out the traffic from only one country. If you're not sure what to think of this, don't fret — Littledata's analytics team can guide you in multi-site setup with an Enterprise plan, so please reach out if you're feeling confused. [subscribe] Multicurrency support for Shopify If your store presents prices in multiple currencies using Shopify Payment’s multi-currency feature, then Littledata’s app is 100% compatible with multi-currency. Here’s how it works for different parts of the data processing. We use Shopify’s definition of ‘presentment currency’ and ‘shop currency’. Storefront data layer All prices for products in the LittledataLayer and dataLayer variables will be in shop currency, regardless of the presentment currency. This includes the add-to-cart events handled by Littledata’s servers. Checkout steps Prices are sent in the presented currency and converted by Google Analytics (or Segment) to the target currency at current exchange rates. Orders & Refunds All orders and refunded items are sent to Google Analytics in the shop currency. Multiple country stores sending to one web property If you have multiple country stores, with different shop currencies, all sending data to a single web property in Google Analytics, this is also handled by our tracking script.
Does Littledata work with my ecommerce reporting tool?
We often get asked if Littledata works with certain reporting tools that are popular among merchants. Here's the short answer: if your tool can pull data from Google Analytics or Segment, then our smart connections will help you get accurate data to use with those reporting or data insight tools. Here’s exactly how we work with each platform. The Establishment These are the main ecommerce reporting tools. If you're using one of these tools, you're in luck. Littledata integrates with them automatically. These products include: Google Analytics Google Data Studio Tableau Segment Power BI Read on to see how Littledata works with these data insights tools to improve the accuracy of your data and the usefulness of your reporting. Google Analytics The world’s most popular analytics tool gets even better when paired with Littledata’s smart connections and full audit to enhance the ecommerce event data captured from your store. GA is the core tool to which Littledata connects, allowing you to connect to other dashboards below, such as Tableau and Data Studio. [subscribe] Segment Segment’s data pipeline is a trusted way to get analytics from one platform into dozens of others without complex engineering. Our source for Shopify and Shopify Plus helps you automatically send ecommerce events into any of Segment’s hundreds of destinations. Once the Segment connection is set up, you never lift a finger. Google Data Studio Data Studio is our dashboard tool of choice for more custom-designed reports. Because Data Studio integrates with Google Analytics, Littledata’s connections can all be exported from GA into Data Studio. While it can be slow to generate reports at scale, its unlimited free reporting makes it hard to beat for ad-hoc analysis. [note]Have you done something unique with Data Studio and Littledata? We'd love to hear about it. Reach out and let us know.[/note] Tableau Tableau, now part of the Salesforce family, was one of the first tools to provide a smart, easy setup dashboard. Their connection with Google Analytics is well established, and as with Data Studio you can access the Littledata events from there. Power BI Microsoft’s popular reporting tool can also import data from Google Analytics, so anything Littledata pushes to GA is available in Power BI. Power BI is especially useful if you want to visually explore your ecommerce data, as the platform offers interactive visualizations and a range of business intelligence (BI) insights. They also allow on-premises report deployment (behind a firewall), which is great for larger brands with large in-house teams. Newer solutions In addition to the 'establishment' above, Littledata seamlessly integrates with a number of newer reporting tools that offer Google Analytics insights and visualizations. These apps and reporting tools include: Glew Glow Yaguara These tools are especially popular with Shopify and Shopify Plus stores. Glew Glew provides some cleverly-curated ecommerce reports, but any of the underlying data on marketing attribution or customer behaviour pulled from Google Analytics will require Littledata’s improved tracking for full accuracy. Here’s a more detailed guide of the differences between Glew and Littledata. Grow Grow is a newer dashboard tool with hundreds of reporting sources, of which Google Analytics is their ‘most popular’. While some of the detail from Littledata’s connections may be lost in ‘basic reporting’ from GA, their multi-channel marketing reports are useful. Yaguara Yagura provides a series of templates to gain insights into your ecommerce business. One of their key integrations is with Google Analytics, and so the extra insights from Littledata’s tracking can be pulled into a Yaguara dashboard. Tools we don't work with directly The following tools don't connect with Littledata, and we aren't planning an integration. Conversific Conversific is an analytics tool for Shopify, with similar reporting to Littledata. While Conversific doesn’t offer the same smart connections as Littledata, it’s unlikely you’d need to use the two together. Metrilo Metrilo offers to optimize marketing channels for ecommerce. While their guide says Metrilo is a good alternative to Google Analytics, it won’t replace the reporting that Littledata provides. Zaius Zaius is an ecommerce CRM, allowing you to personalise and automate marketing based on customer interactions. As such, it needs its own event data capture, and can’t integrate with Littledata reporting. [note]Have you built custom reports using Littledata? We'd love to hear about it. Give us a shout and let us know.[/note]
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