Appsee’s action cohorts feature enables you to understand how often your users return to the app and perform certain actions in a given time frame. Action cohort reports can help you understand if your app meets your users’ needs and expectations and enables you to measure how your app optimization efforts impact action retention.

1. Navigate to the ‘Actions Cohorts’ section in the Appsee dashboard and click ‘Create Your First Cohort’.

2. Enter the name of the relevant process you’d like to measure.
For example: ‘In-App Purchase’, then select ‘Device Type’ (optional), the time frame which you’d like to include data from, and then click ‘Next’.

3. Select the first action that will define the cohort. Users that performed this action will be grouped in the rows of the cohort.
Each step can be either a ‘Screen View’, a ‘Screen Action’, a custom event, or a session start.

4. Select the second action that will define the cohort. You will then see how many of the users that performed the first action, came back and performed the second action.

How is action retention calculated?
1. Our retention cohort analysis groups the number of users that performed the first action of the cohort (and not the total number of app users). The cohort will include every user that used the app and performed the first action within the defined time frame and selected date bucket (daily/weekly/monthly).

2. In this case, we’re looking at weekly cohorts, as shown above, the numbers 1,2,3 along the top of the retention report will indicate weekly buckets. The percentage under each weekly bucket represents the number of users who returned to use the app and performed the second action during that specific bucket.

3. The beginning and ending of each bucket will be different for each new user in the cohort. The bucket marked “1” indicates a timeframe of 7 days following the initial session of the user. For example, if the initial session occurred at 10 am on Sunday, then bucket “1” for him/her begins 10 am Sunday of the following week. Bucket 1 for this specific user extends between 7 and 13 days from his/her initial session.

4. A user is only be counted once per cohort, but can be included in more than one cohort: For example, if the weekly cohort is based on a “login” event, a customer who logged in at least one item each week will be in every cohort, and not only the cohort for their first purchase.

5. Users may “complete” a cohort within 30 days (when watching daily cohorts), 20 weeks (weekly cohort) or 12 months (monthly cohort).

6. When a user performs the second action, it is counted for every first action that was performed in the timeframe, not just the latest one.

7. Each cohort cell is unique: if a user performs the second action twice in the same bucket, it will only be counted once.

8. When creating a cohort in which both steps are similar (for example: “Log In”), every action will act both as a first and second action. If a user logged in 3 times: on Sunday, Monday, and Tuesday – the action performed on Monday will be counted as the second action for the one on Sunday, but will also open a new cohort that the Tuesday action will complete.

9. When filtering by versions, we will show only the users that performed their first action in the selected version.


  1. Compare specific action cohorts from version to version or platform to platform.
  2. Click on the red play button to view sessions of users who don’t complete the desired action.
  3. Click on the blue play button to view sessions of users who do complete the desired action. Even if they completed it, we can learn about the struggles they might have experienced along the way.

Below is an example of an action cohort that helps you understand how quickly users complete a purchase. For example, on the week of Feb 26- March 4, there were 1,103 users who started their first session. That same week, 13.69% of those users triggered the custom event ‘PurchaseComplete.’ The week after (March 5- March 11), 7.25% of users triggered the ‘PurchaseComplete’ event.

Here are some questions you can use action cohorts to help answer:

  1. What type of effect does a new feature have on user retention? You can compare a new feature action cohort to a baseline retention cohort of users who started their first session and then came back for any session.
  2. Which actions show a higher impact on retention? How can you push users to take that specific action?
  3. Do users who experience a crashed sessions return for another session?
  4. Do the same users experience multiple crashes?
  5. From the users who fail to log in or register, how quickly do they do so successfully?

Recommended article:

  1. Create your first action cohort with this tutorial.