Skip to main content

​
Why it Matters

Learn if your users are returning to use your features over and over again, or if they only use them once and never come back. Feature retention contributes to successful application usage over time, providing regular value to your users. By analyzing the frequency of returning users, you can understand how often your features are being used and which features are deemed more essential by your users. A low feature retention rate indicates that your users are not finding enough value in your features, or they do not understand how to use them properly, indicating a need for better onboarding or documentation.

​
Definition

Feature retention measures the average of your feature retention rate and stickiness. Both metrics indicate how often your users are returning to use your features again and again. Feature Retention=Retention Rate + Stickiness2\text{Feature Retention} = \dfrac{\text{Retention Rate + Stickiness}}{\text{2}}
Retention Rate=Returning UsersAll Users From Previous Period\text{Retention Rate} = \dfrac{\text{Returning Users}}{\text{All Users From Previous Period}}
Stickiness=Weekly UsersMonthly Users\text{Stickiness} = \dfrac{\text{Weekly Users}}{\text{Monthly Users}}

​
Analytics

  • Retention Score: average of retention rate and stickiness.
  • Retention Rate: percentage of users who have returned to use selected features again in the current period.
  • Stickiness: percentage of users who have used selected features on a weekly basis vs monthly basis.
  • All Users: number of users who have used selected features in the previous period.
  • Returning Users: number of users who have returned to use features again in the selected period.
  • Feature Stickiness Chart: compares the number of users who have used selected features in the last month vs in the last week.
    • Weekly Users: users who used any of the selected features in the last week.
    • Monthly Users: users who used any of the selected features in the last month.
  • Returning Users Activity Chart: shows you the frequency of how often your users are returning to use selected features. For example, see if your new application update contributed to users coming back and using your features more often.
    • All Users: users who have used any of the selected features in the previous period.
    • Returning: users who have returned to use any of the selected features again in the selected period.
    • Monthly Active: users who have used any of the selected features in at least 2 of the last 3 months (66% of months).
    • Weekly Active: users who have used any of the selected features in at least 9 of the last 12 weeks (75% of weeks).
    • Daily Active: users who have used any of the selected features in at least 36 of the last 90 days (40% of days).
    Users who have signed up in the last 90 days will have their activity calculated based on the percentage of the last 90 days. For example, instead of having to use selected features for 9 of the last 12 weeks, they have to use them for 75% of the weeks since they’ve signed up.

​
Filters

  • Feature: select either all or specific features to analyze.
  • User Segment: select either all users or a specific user segment.
  • Date Range: period during which to analyze feature retention.