Do you know how your Adwords-acquired customer segments grow their LTV compared to those customers acquired from organic search? Have you ever thought of performing a cohort analysis on different customer segments side-by-side in the same report? If so, a qualitative cohort analysis will help you answer those questions.
In this article, we’ll dive into what a qualitative cohort even is, why you might be interested in building this analysis, and how you can create it in Magento BI.
What are qualitative cohorts, anyway?
Cohort analysis in general can be broadly defined as the analysis of user groups that share similar characteristics over their life cycles. It allows you to identify behavioral trends across different user groups.
For a more in-depth primer on cohort analysis, take a look here - we wrote the site on it!
Most cohort analyses in Magento BI group users together by a common date (i.e, the set of all customers who made their first purchase in a given month). A qualitative cohort is a little different: it’s a user group that is defined by a characteristic that isn’t time-based. Some examples include:
- The set of all users that were acquired from an ad campaign
- The set of all users whose first purchase included a coupon (or didn’t)
- The set of all users who are of a certain age
How does that differ from the normal cohort builder?
The Cohort Analysis Builder is optimized for grouping cohorts using a time-based characteristic. This is great for analyses focusing on a specific segment of user (i.e., all users who were acquired via a paid search campaign). In the Cohort Analysis Builder, you can (1) focus in on that specific user group, and (2) cohort on a date (like their first order date).
However, if you want to analyze the cohort behavior of multiple user segments in the same cohort report (paid search vs. organic search vs direct traffic, perhaps?), this more advanced analysis can be constructed in the Report Builder.
What information should I send to support to set up my analysis?
Creating a qualitative cohort report in the Report Builder involves our analyst team creating some advanced calculated columns on the necessary tables.
To build these, submit a support ticket (and reference this article!). Here’s what we’ll need to know:
The metric you want to perform your cohort analysis with and what table it uses (example: Revenue, built on the orders table).
The user segments you want to define and where that information lives in your database (example: different values of User’s referral source, native to the users table and relocated down to the orders).
The cohort date you want your analysis to use (example: the User’s first order date timestamp). This example would allow us to look at each segment and ask “How does a user’s revenue grow in the months following their first order date?”.
The time interval that you want to see the analysis over (example: weeks, months, or quarters after the User’s first order date).
Once our analyst team responds to the above, you will have a couple of new advanced calculated columns to build out your report! Then you’ll be able to follow the below directions to do this.
Creating the qualitative cohort analysis
First, you’ll want to add the metric you’re interested in cohorting, once for each cohort you are analyzing. In this example, we want to see cumulative Revenue made in the months after a customer’s first order, segmented by the User’s referral source. This means that, for each segment, we will add one Revenue metric and filter for the specific segment:
Second, you should make two changes to the time options of the report:
Set the time interval to None. This is because we’ll eventually group by the time interval as a dimension instead of using the usual time options.
Set the time range to the window of time you want the report to cover.
In our example, we’ll be looking at an all time view of Revenue. After this, you should end up with a series of dots:
Third, you will make an adjustment to actually set up the cohorts. Based on the cohort date and time interval you specified to our analyst team, you’ll have a dimension in your account that will perform the cohort dating. In this example, that custom dimension is called Months between this order and customer’s first order date. Using this dimension, you should:
Group by the dimension with the group by option
Select all values of the dimension in which you are interested
With the Show top/bottom option, select the “top” X months that you’re interested in, and sort by the Months between this order and customer’s first order date dimension
Now, you’ll be able to see one line for each cohort that you specified. Check out our example now – we see the Revenue contributed by users of each referral source, grouped by the number of months between their first order and any subsequent order. We also added a Cumulative perspective to see the cohorts’ aggregate growth - take a look at the results table for more granularity.
What does this tell us? Here, the specific referral source Paid search is very valuable in the first month of a customer’s purchasing lifetime, but fails to retain its customer base with repeat revenue. While Direct Traffic starts off at a lower amount, revenue in subsequent months actually accumulates at a similar pace.
No matter how you dice it, cohort analysis is a powerful tool in your analysis toolbox. This type of analysis can yield some very interesting insights about your business that traditional time-based cohorts may not, enabling you to make better data-driven decisions.