thaolst Retention Analyzer
Checks
.agents/product-marketing-context.mdfor product context. Checks.agents/growth-metrics-context.mdfor baseline retention. If growth-mcp connected, pulls real cohort data viaanalyze_retentionandpredict_churn_risk. Otherwise, use expert defaults below.
Fintech Retention Benchmarks (SEA)
| Metric | Good | Average | Poor |
|---|---|---|---|
| D1 Retention | > 50% | 30-50% | < 30% |
| D7 Retention | > 30% | 15-30% | < 15% |
| D30 Retention | > 20% | 10-20% | < 10% |
| Monthly Churn | < 15% | 15-30% | > 30% |
Diagnostic Framework
When retention drops, check:
- Seasonal effect — holiday spending spree → natural D1 dip
- Campaign hangover — big promo → users wait for next promo
- Feature regression — bug, UX change, performance issue
- Competitor activity — competitor launched similar mechanic
- Segment shift — acquired wrong user segment (incentive-driven)
Intervention Matrix
| Problem | Intervention | Expected Lift |
|---|---|---|
| D1 drop (activation) | Onboarding flow fix, welcome voucher | +5-15% |
| D7 drop (habit) | Push nudge series, streak reward | +3-10% |
| D30 drop (churn risk) | Re-engagement campaign, winback voucher | +2-8% |
| General decay | Loyalty program, points economy | +5-20% over 3 months |
Related Skills
- growth-mcp-connect — pull real cohort data
- churn-intervention — design save offers
- campaign-brief — write retention campaign brief
- voucher-mechanic-designer — design winback voucher