The Cohort Analysis Report enables you to track the quality of a group of customers (cohort) over time and compare it against previous periods.

 

With the Cohort Analysis Report, you will be able to:

· Understand which cohort drove the highest lifetime value

· Pinpoint the exact week/month high-value customers were acquired

· Understand which points in time you are losing customers

 

For example, customers acquired in January may make more deposits in the first month after acquisition compared to those acquired in February.

 

Once this has been identified, you can examine your acquisition strategy in these respective months to understand which campaigns attract higher-quality customers. 

 

Or another reason one cohort may behave differently is due to market-wide factors. For example, customers acquired during January sales may be of higher quality than those acquired in February. 

 

 

The survival rate is the percentage of users from the cohort who made a deposit or purchase since the month they made their first deposit or purchase. Tracking survivability over time is an important indicator of the success of your brand’s retention efforts.

 

"#" - represents the cumulative number of purchases/deposits made per customer. 

 

"$" -represents the cumulative deposit/purchase amount per customer.  

 

 

3. Select your time period. You can either select pre-set timeframes dating back to 6, 9, 12 or 18 months or a custom time period.  

 

 

1. Understand how a cohort behaves each week/month since their first deposit/purchase  

 

For the first cohort (February 2025), the average customer's order amount was $114.00 in their first month (Month-0). This value then grew to a cumulative average of $117.56 after one month (Month-1), $119.78 after two months (Month-2), and so forth, reaching $138.88 by Month-8.

An interesting insight a marketeer could identify is the difference in value between cohorts. For example, the July 2025 cohort was much larger (9,255 users) than the February 2025 cohort (6,537 users), but their initial average order amount was significantly lower ($63.92 vs. $114.00). This could prompt an investigation into whether a specific marketing campaign in July attracted a higher volume of lower-value customers.

 

2. Compare cohorts’ behavior by month/week since the acquisition 

 

To compare the behavior of different cohorts, you can read the chart by columns. This allows you to identify which cohorts have a higher cumulative value in their respective lifecycle stage.

For example, you can analyze which cohorts perform the best following their first month of acquisition by looking at Month-1. Here we can see customers acquired in February 2025 had a significantly higher cumulative order amount ($117.56) than customers acquired in March 2025 ($81.78) or July 2025 ($64.86). Using this data, you can then look into your acquisition program for these months and identify whether a different marketing strategy caused this.

 

3. Compare how each cohort behaves on the same calendar month

 

 

To analyze cohort behavior by calendar month, read the report diagonally. In this example, if you want to study how each cohort behaved in October 2025, you would look at the data highlighted. This path goes from Month-8 for the February 2025 cohort ($138.88) and continues down diagonally: Month-7 for the March 2025 cohort ($99.46), Month-6 for the April 2025 cohort ($104.04), and so on.