User Retention Cohort Analysis AI Prompt

Build and interpret a user retention cohort analysis.

Event data: {{event_data}} (user_id, event_date, acquisition_date or cohort_date)
Retention definition: {{retention_definition}} (e.g. any login, completed core action, purchase)
Cohort granularity: {{granularity}} (weekly / monthly)

1. Build the retention matrix:
   - Rows: cohorts defined by {{cohort_period}} of first use or acquisition
   - Columns: periods since acquisition (Period 0, 1, 2, ... N)
   - Cell value: % of cohort still active in that period
   - Period 0 = 100% by definition (the acquisition period)

2. Key retention metrics:
   - Day 1 retention: % of users returning the day after first use
   - Day 7 retention: % returning in the first week
   - Day 30 retention: % returning within the first month
   - Long-term retention: at what period does the retention curve flatten? This is the product's natural retention floor.

3. Cohort comparison:
   - Are newer cohorts retaining better or worse than older ones?
   - Which cohort has the best Day 30 retention? What was happening during that acquisition period?
   - Plot cohort curves on the same chart: diverging curves indicate improving or worsening product health

4. Retention curve shape interpretation:
   - Sharp early drop then flat: high initial churn but strong core user base
   - Gradual continuous decline: no engaged user base, product is not habit-forming
   - Bump at specific period: seasonal return or notification-driven re-engagement

5. Retention by acquisition channel:
   - Which acquisition channels produce the highest Day 30 retention?
   - Are there channels bringing volume but low retention? (Wasted acquisition spend)

6. Recommendations:
   - At which period does the biggest retention drop occur? What is the likely cause?
   - What single change would most improve the retention curve shape?

Return: retention matrix, key metrics table, cohort comparison chart description, curve interpretation, and top recommendations.