One of the challenges subscription businesses face is differentiating between order types.  The problem For Shopify merchants, offering single purchase options complicates things even more — with single purchase options, order data will show in two places (Shopify and ReCharge). This leads to a major disconnect between user behaviour and orders, unable to leverage the full potential of Google Analytics.  At this point, you probably have a multitude of “known unknowns”, such as: Which traffic sources drive more first time subscription orders? What's my conversion rate on one time purchases? What's my average customer lifetime value (CLV or LTV)? Do my one time purchasers end up converting to subscribers? What's my churn rate month over month? The solution Bridging the gap with Littledata Littledata helps bridge different platforms by linking orders betweenShopify, ReCharge and Google Analytics.  Differentiating between order types With Littledata’s improved tracker, merchants can differentiate between order types. For this, we use the Affiliation dimension.  In the Google Analytics report, it will look something like this:  Right away, this information answers a few questions: What is the distribution between my order types? Are my recurring subscription orders growing month over month? Is the average order value (AOV) of subscribers higher than that of one time purchasers?  Both of these can be viewed in this custom report. What traffic sources drive the most sales?  One of the questions our team is asked most often is what sources of traffic drive the most subscription orders?  Short answer: the Affiliation dimension can be used as a secondary dimension in the source/medium reports, or use this custom report.  By using filters to single out an order type, you can easily determine what traffic sources drive the most first time subscription orders. Segment more Segmentation opens up new ways to look at the data as well. Creating two segments for one time purchases and first subscription purchases, you can see how the two types of purchases differ.  Look for behavioural differences like:  Do One Time purchasers AOV higher or lower compared to First Subscription orders? Are users testing the product first and then committing to a subscription?  Littledata custom dimensions Google Analytics custom dimensions are an excellent way to expand your data collection and reporting power. With our Shopify app, Littledata adds these custom dimensions: Littledata - Shopify Customer ID Littledata - Last Transaction Date Littledata - Purchase Count Littledata - Lifetime Revenue Littledata - Payment Gateway With the help of these custom dimensions, we can answer the following questions:  What's my median customer lifetime value (CLV)? How many purchases do customers make in their lifetime? What's my churn rate month over month? Since these are custom dimensions, they cannot be aggregated on Google Analytics, meaning the data will need to be displayed using a different method. For this, we’ll use Google Sheets with the Google Analytics add-on to query the data and pivot tables.  Step1 - Query all the data you need Metrics Avg. Order Value Revenue Dimensions Littledata - Shopify Customer ID  Littledata - Lifetime Revenue Littledata - Purchase Count Affiliation - to differentiate between the order types.  It should look something like this:  In this case, the custom dimensions are at index 4, 6 and 8. This may differ depending on your setup.  Step 2 - Run the report After you run the report, this will create another sheet in your document. It will look something like this:  Step 3 - Create a pivot table  In the rows section, add the Affiliation dimension to differentiate between the order types.  Shopify will mean a one time purchase (normal purchase). The other two order types are the first subscription order and recurring order.  In the values area, add:  The user IDs summarised by countunique The customer lifetime value summarised by median so that we have the median LTV. We use median over average so that this number is not influenced too much by the outliers.  Purchase count summarised by median.  Average order value  The end result should look something like this:  Step 4 - Interpret the data In this report, we can instantly draw some conclusions:  Most customers make single purchases rather than subscribing  Subscription first order median purchase is 2, so this means users have purchased in the past before committing to a subscription Subscribers purchase 8 times (median value), with a median CLV is around $500.  How to take this further Since we know most customers order at least once before committing to a subscription, we can calculate the average number of days between a single purchase and a first subscription purchase.  When you’re armed with that type of information, you can adjust your email marketing flows accordingly and adjust your remarketing campaigns to shorten or lengthen the number of days your ads show to leads.  With the help of the Customer IDs, we can also calculate the month over month churn rate (we’ll get to that in a follow-up post). It's your turn now How do you use these additional events and custom dimensions in your segmentation?  What was your biggest insight using these events and custom dimensions?  How did it influence your marketing campaigns? Share your experience (and current approach with GA) via the live chat in the bottom corner. I'm curious about the different ways you make use of these additional data points! In the meantime, our team is here at the ChargeXSummit in Santa Monica sharing all about our ReCharge connection for subscription-based stores.

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]