Definition

This is a cohort chart that shows the proceeds revenue generated by users who installed the app within the specified dates.

For example, if the chart indicates $2,500 for June 4, it means that users who installed the app on that date collectively generated $2,500 in proceeds revenue.

As time progresses, the revenue for each cohort will typically increase, reflecting the ongoing revenue contributions from users who continue to engage with the app.

FAQ

What purchases are included in the Cohort Proceeds chart?

The Cohort Proceeds chart includes all purchases, not just subscriptions. This means that any revenue generated by users—whether from one-time purchases, subscriptions, renewals, or any other in-app purchases—is reflected in the chart.

What does the X-axis represent: event timeline or cohort timeline?

The X-axis in the Cohort Proceeds chart represents the cohort timeline. Each cohort is grouped by the date users installed the app, so the Proceeds value shown corresponds to the activity of users who installed the app on a particular date, not to the timeline of specific events.

Why do the figures on the chart change over time for historical cohorts?

Figures on the chart for historical cohorts may change as users continue to interact with the app. These updates are due to:

• Subscription renewals: Revenue increases when users in a cohort renew their subscriptions.
• New purchases: If users make additional one-time or subscription purchases after their initial install date, this contributes to the cohort’s proceeds.
• Refunds: If users request refunds for past purchases, this revenue is subtracted from the cohort’s total proceeds.

This chart is dynamic and reflects the ongoing revenue contributions or adjustments associated with each cohort, providing a comprehensive view of cohort-generated revenue over time.

If Apphud was introduced to an existing app and historical data was not migrated, Apphud may retrieve purchase information for historical cohorts when users launch the app for the first time after integration. This can also lead to adjustments in the revenue figures for historical cohorts.