How to measure retention performance on your Shopify store through cohort analysis
Reporting & Analysis
Cohort analysis is a vital method to gauge customer retention and engagement. It involves categorizing customers by their acquisition month and tracking how many continue shopping in subsequent months. You can create such reports manually or with tools like Google Analytics. However, Airboxr offers a convenient Cohort Analysis Hop that automates the process using Shopify data. This matrix helps you visualize customer retention trends, aiding in marketing strategies and re-engagement campaigns. Understanding cohort analysis is essential for long-term business success.
You’ve spent so much time and effort to acquire your customers. Now, how do you measure whether you’re doing enough to retain them?
One of the ways to analyze if your customers are staying with you, and coming back to shop repeatedly is by looking at retention cohorts. It looks like a matrix, and gives you a quick overview of:
your customer ‘cohorts’ by the month they were acquired, and
what percentage of them stayed on and shopped with you again one month later, two months later, and so on.
Cohort analysis is a report that you want to track over the long term, say over 6 months or 12 months. It helps you to assess whether or not you need to reach out to your customers to re-engage them and re-ignite their interest in your store.
In this article, we will cover these topics:
How to create a cohort analysis
There are many ways to do a cohort analysis. Here are a few options:
1) Creating a report in Google Analytics
If you’re the building sort, you can use Google Analytics to build your own cohort analysis report. Google gives detailed instructions on how to do that.
By building your own report, you can determine:
The cohort type (e.g. customers acquired)
The cohort size (e.g. acquisition by day, week, or month)
The metric you want to evaluate (e.g. whether they make a repeat purchase)
The relative date range and number of cohorts
Which cohorts are illustrated in the report
However, most Shopify stores tend to use Shopify data in their reporting. This is because Google Analytics provides data on your visitor cohorts, not customer cohorts.
If you’re looking for customer cohorts, the next two methods would be more relevant.
2) Manually keeping track of customers in Shopify
There doesn’t seem to be any way of creating a customer cohort analysis within Shopify itself. However, if you’re looking to assess the effectiveness of your acquisition efforts in April, you can find a list of people who placed orders from your store in April, then check each one to see when their previous orders were to get a sense of your customer retention rate for that month.
Run Cohort Analysis for your Shopify store in seconds.
3) Using Airboxr’s revenue analytics tool
Airboxr’s Cohort Analysis Hop automatically creates a customer cohort matrix for you based on your Shopify data.
Once you add the Cohort Analysis Hop from the Hop Marketplace, you can already run it on a spreadsheet. This analysis is conducted for the last 6 months. Below is a demo of how it looks like when you run the Hop:
Airboxr allows you to create a cohort analysis of your Shopify customers with a single click. Learn more about how it works and sign up for a free account.
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How to read a cohort analysis
As shown above, the data is presented in matrix form. Next we will look at how to read the data in the matrix.
Usually, the rows of the matrix will show the month of customer acquisition, and the columns will show each subsequent month following the acquisition month, with Month 0 (aka M0) being the acquisition month. You can see it in the example screenshot below.
In this example, row 4 shows that out of the customers who were acquired in May 2022 (M0 = 100%), most of them continued as a repeat customer in June 2022 (M1 = 89.6%). This indicates that the customers acquired in May were mostly high quality, highly relevant ones, and continue to find value in your products the next month. The March cohort gives more data over a longer time period, showing how the rate of returning customers drops off over time, indicating your customers may not find a reason to return over a longer time period.
The example also shows that unfortunately, all the customers acquired in April did not make a repeat purchase, indicating that these were not the right type of customers to acquire.
This kind of information can help you plan your next steps in assessing your marketing campaigns. For the above example, you may wish to review the campaign run in April which did not yield great results, or determine what worked well for March and May so you can learn from those campaigns. If you believe there could be a benefit in sending different marketing messages, you might also want to plan a new re-engagement campaign for the April cohort so that you don’t lose them completely.
Prefer to watch a cohort analysis walkthrough on video instead? Here our cofounder talks through the process of creating and interpreting a cohort analysis on your Shopify data.
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