Cohort analysis

Gain insights from the behavior and performance of groups of users related by common attributes.

This help center article is part of the App + Web Property Beta.

App + Web properties are not currently supported in the Analytics app.

 

A cohort is a group of users who share a common characteristic that is identified in this report by an Analytics dimension. For example, all users with the same Acquisition Date belong to the same cohort. Cohort analysis allows you to explore the behavior of these groups over time on your app or site.

Example cohort analysis

Create a cohort analysis

  1. Sign in to Google Analytics.
  2. On the left near the bottom, select Analysis.
    Accessing advanced analysis in Google Analytics
  3. Select Cohort analysis.
  4. Define the Inclusion criteria: the condition that adds a user to a cohort.
  5. Set the Return criteria: a subsequent condition these users meet to remain in your cohort.
  6. Monitor how your users' behavior changes over time by looking at cohorts from different dates.

For example you can see how long it takes newly acquired users to transact on your site and how that changes in the week you are running a promotion, or how many users you retain over time and if that new app design you just launched is improving retention rates.

How cohorts work

Cohort analysis starts by finding the users who satisfy the selected inclusion criteria. You can set the granularity of the analysis to daily, weekly, or monthly cohorts. The data table shows how many users belong to each cohort for the duration of the analysis.

Next, the analysis shows the number of users in each cohort who meet the return criteria after the start date. For example, if you select daily granularity and look at the cohort for January 1st, the day 1 column represents the subset of users who met the inclusion criteria on January 1st and also the return criteria on January 2nd.

Users can only belong to one cohort: the first one for which they qualify chronologically. For example, with transactions selected as the inclusion criteria, if a user completes a transaction in every week of the analysis timeframe, that user is only counted in the first row of the table, since that was the week in which their initial transaction occurred, and hence, first met the inclusion criteria.

Configure the cohort analysis

Cohort inclusion

Defines the condition a user has to meet to be included in a cohort. Select from:

  • Acquisition date: the first time the user visited your app or website, as measured by this Google Analytics property.
  • Session: the first session for the user within the analysis date range.
  • Transaction: the first time the user had a transaction event within the analysis date range.
  • Conversion: the first time the user had a conversion event within the analysis date range.

Return criteria

Defines the condition a user who belongs to a cohort has to meet to be counted in any of the following time ranges. Only users who meet the return criteria are included in the cohort table. Select from:

  • Session: the user has at least 1 session within the return time period.
  • Transaction: the user has at least 1 transaction event within the return time period,
  • Conversion: the user has at least 1 conversion event within the return time period.

Cohort granularity

Defines the initial and returning cohort time frame. The return time granularity is the same as the cohort granularity. Select from:

  • Daily: from midnight to midnight in the property timezone.
  • Weekly: from Sunday to Saturday included, not on a rolling 7 days.
  • Monthly: from the beginning of the month to the end of the month.

Breakdown

Divides each cohort into sub-groups based on a selected dimension so you can easily compare how a cohort differs along that dimension.

Values

Determines the metric to show in the cohort table. For example, Active users shows how many active users meet the criteria for being included in each cell of the table.

Understand the cohort analysis

Example 1

Example cohort analysis: weekly transactions 1

Between October 6th and October 12th, this site acquired 17,093 users.

These 17,093 newly acquired users had 176 transactions in the same week they were acquired (Oct 6th to Oct 12th).

These same 17,093 users had 38 transactions the week following their acquisition week (Oct 13th to Oct 19th).

Example 2

Example cohort analysis: weekly transactions 2.

Between October 6th and October 12th this site acquired 17,093 users.

Of the 17,093 newly acquired users in the week from Oct 6th to Oct 12th, 171 had at least one transaction in the same week they were acquired (Oct 6th to Oct 12th).

Of the 17,093 newly acquired users in the week from Oct 6th to Oct 12th, 31 had at least one transaction in the first week after they were acquired (Oct 13th to Oct 19th).

Example 3

Example cohort analysis: weekly transactions 3

Between October 6th and October 12th, this site had 270 users with at least one transaction.

Of the 270 users who had at least one transaction during this time, 14 had at least one transaction in the following week (Oct 13th to Oct 19th).

Of all the users who had at least one transaction between Oct 6th and Nov 30th, 290 had their first transaction between Oct 13th and Oct 19th. Of these 290 users, 11 had at least one transaction the following week (Oct 20th to Oct 26th).

Example 4

Example cohort analysis: breakdown dimension

This example shows the results of adding Device category as the breakdown dimension. Each cohort is broken down according to which device type the user was using when they were acquired (desktop, mobile, or tablet).

When you include a breakdown dimension, users are only attributed to the first instance of the breakdown value that applies to them. For example, say User A first appears as a mobile user, then returns the same day as a desktop user. User A only appears in the mobile breakdown for that cohort.

Limits of cohort analysis

  • Cohort analysis can show a maximum of 60 cohorts.
  • When you apply a breakdown dimension, a maximum of the top 15 values of that dimension are shown.
  • Demographic dimensions are subject to thresholding. If the number of users in the cohort is too small to protect their anonymity, those users won't be included in the analysis.
  • Currently, you can't apply segments to or create audiences from cohort analyses. We're working to add these features in a later release.
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