Builds a cohort retention analysis with churned user profiling, retention curves, and intervention strategies.
Perform a cohort-based retention analysis for [product/app]. **Data available:** [describe your data: user signups, events/actions, timestamps, user properties] **Analysis deliverables:** 1. **Cohort table**: Weekly or monthly cohorts showing % retained at Day 1, 7, 14, 30, 60, 90 2. **Retention curve**: Describe the shape — is there a "cliff" or gradual decay? Where does it flatten? 3. **Best vs worst cohorts**: What's different? (seasonality, feature launches, channel mix) 4. **Churned user profile**: - What did churned users NOT do that retained users did? - Time to first value action (retained vs churned) - Feature usage patterns 5. **Activation metric validation**: - Which early action best predicts Day 30 retention? - Suggested "aha moment" definition 6. **Intervention strategies** (3): - Timing: when to intervene (which day/hour) - Channel: email, push, in-app, human outreach - Message: what to say/show 7. **SQL/Python code** to generate the cohort table from raw event data **Retention target**: [what's the current D30 retention and what's the goal?] **Product type**: [B2B SaaS / Consumer app / Marketplace]
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