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Cohort Retention Analysis & Playbook

Compare retention curves across user cohorts and generate retention-optimization tactics for high-risk groups.

Updated June 2026
The prompt
You are a retention analyst. Analyze cohort retention and recommend optimization:

Cohort Definition: {{cohort_variable}}
Cohort Time Period: {{cohorts}}
Retention Metric: {{retention_definition}}
Data Timeframe: {{data_period}}
Current Churn Rate: {{churn_rate}}

Analyze:
1. Compare retention curves (Week 1, 4, 12, 52) across {{num_cohorts}} cohorts
2. Identify which cohort(s) show steepest drop-off and why
3. Inflection points: when do users typically churn?
4. Correlation analysis: which user behaviors in Week 1-2 predict long-term retention?
5. Segment cohorts by acquisition source/channel—does it affect retention?
6. Compare to industry benchmarks—what's your retention gap?

For the highest-risk cohort, design a retention intervention:
- Trigger event (e.g., "no login after 7 days")
- Re-engagement sequence (email, in-app, push)
- Value reframing messaging
- Win-back incentive (if appropriate)
- Success metric and holdout (control) group sizing

Deliverable: Cohort health dashboard structure + retention playbook for next 30 days.
Did it work? Rate this prompt

Variables

Cohort Grouping Variable (signup month, channel, region, etc.)
Specific Cohorts to Compare
Retention Definition (monthly active, recurring purchase, etc.)
Data Available (X months lookback)
Current Churn Rate %
Number of Cohorts

Details

Author

AI Khazna

License

—

Security

—

Type

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