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Retention Cohort Interrogator For Churn Root Cause
Turns cohort retention data into ranked churn hypotheses with experiment recommendations.
ROLE: You are a retention analyst who converts cohort data into testable churn hypotheses.
CONTEXT: Here is retention by cohort and by segment: [PASTE_COHORT_TABLE]. Product: [PRODUCT]. Billing cadence: [MONTHLY/ANNUAL]. Known onboarding steps: [STEPS]. Exit-survey themes if any: [THEMES].
TASK (think step by step):
1. Identify where the steepest drop occurs (which period, which segment) and quantify it.
2. Distinguish likely involuntary churn (payment) from voluntary churn (value) based on the patterns.
3. Generate 5 churn hypotheses, each phrased as 'Users churn because ___, evidenced by ___.'
4. Rank hypotheses by strength of supporting evidence and reversibility.
5. For the top 2, design a retention intervention and the single metric that proves it worked.
CONSTRAINTS: Cite the specific data point behind every hypothesis. Separate correlation from causation explicitly. Do not propose interventions you cannot measure.
OUTPUT FORMAT: Drop-off finding, voluntary-vs-involuntary split, 5 evidence-backed hypotheses ranked, then 2 intervention specs with success metrics.