[GA4] User lifetime

Analyze user behavior and value over their lifetime as a customer.

The user lifetime technique shows how your users behaved during their lifetime as a customer of your site or app. The user lifetime technique can help you find specific insights such as:

  • The source/medium/campaign that drove users with the highest lifetime revenue, as compared to revenue only for the selected month.

  • The active campaigns that are acquiring users who are expected to be more valuable, with higher purchase probability and lower churn probability, as calculated by Google Analytics predictions models.

  • Unique user behavior insights, such as when your monthly active users last purchased a product from your site, or when they were last engaged with your app.

Create a user lifetime analysis

  1. Sign in to Google Analytics.
  2. On the left near the bottom, select Analysis.
    Accessing advanced analysis in Google Analytics
  3. Select User lifetime.

User lifetime data

Lifetime data is available for users who have been active on your site or app after August 15th 2020. For these users the scope of data in the user lifetime technique includes all of their data since they first visited your site or app. For example, a user who first visited your site in December, 2019 but who was last active on August 14th, 2020 is not included. If that same user was active on August 16th, 2020, then all their data going back to last year is included.

The user lifetime technique displays aggregated data for users of your site or app. Specifically, this technique can show the following information for each user:

  • Initial interactions: data associated with the first time the user was measured for a property. For example, their first visit or purchase date, or the campaign by which they were acquired as a user.
  • Most recent interactions: data associated with the last time the user was measured for a property. For example, their last activity or purchase date.
  • Lifetime interactions: data aggregated over the lifetime of the user. For example, their lifetime revenue or engagement
  • Predictive metrics: data generated through machine learning to predict user behavior:
    • Purchase probability
    • In app purchase probability
    • Churn probability

Date ranges in user lifetime analysis

When you select a date range, the analysis displays users who were active during the selected range, and provides information about these users' entire lifetime, including data from before the start of the specified range.

You can't change the end date in a user lifetime analysis. It is fixed to "yesterday."

User lifetime analysis and reporting identity

The User-ID feature gives App + Web properties 2 ways to identify and report on your users across platforms and devices. The reporting identity method used by your property affects user lifetime data as follows:

By User-ID, then device

This method uses the more accurate user ID if it is collected to identify a user and unify all related events in reporting and analysis. If no user ID is collected, then Analytics uses a device ID, either the Analytics cookie for websites or the app Instance ID for apps, to identify a user.

When a given user has both signed in and unsigned in activity for the selected date range, the analysis only uses the signed in portion of the user lifetime data. This provides a more accurate representation of your user data: user count are not duplicated, and metrics like average lifetime value (LTV) are more accurate with User-ID based usage. Activity that occurs while the user is not signed in is not included in the analysis.

By device only

This method uses only the device ID (either the Analytics cookie for websites or the App-Instance ID for apps), to identify a user, and ignores any user IDs if they were collected. With this method, user lifetime data is aggregated at the device level.


Suppose a signed in user had multiple visits to your app last year, and conducted multiple transactions worth a total of $1,000. Suppose that this same user also had 4 transactions conducted as a guest (not signed in), each worth $50, and that each transaction was made on a different device. Finally, suppose this user visited your app while signed in at least once in the date range selected by your query.

How this user appears in the user lifetime analysis depends on the reporting identity method used:

With User-ID, Google signals, then device: this user is counted once, with revenue of $1000, as only her signed in data is applicable to the analysis. Her average lifetime revenue would be $1,000.

With device only: this user appears 5 times: one with revenue of $1000, and 4 times with revenue of $50. Her average lifetime revenue would be $240.

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