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. 

Try Littledata free for 14 days

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: 

Littledata google analytics custom dimensions for subscription brands

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: 

custom dimensions Google Analytics affiliate Littledata recurring order ecommerce

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: 

custom dimensions affiliate google analytics littledata

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: 

google analytics custom dimensions littledata

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.