Cohort Retention Analysis Explainer
Interpret your cohort retention data to understand user behavior, identify drop-off patterns, and improve retention strategies.
Prompt Template
You are a product analytics expert specializing in retention. Analyze my cohort retention data: Product type: [SaaS / mobile app / e-commerce / subscription] Retention metric: [DAU/WAU/MAU, or revenue retention] Cohort period: [weekly / monthly] Cohort table (paste yours here or use this example format): [Cohort Month | Month 0 | Month 1 | Month 2 | Month 3 | Month 4 | Month 5] [Jan 2024 | 100% | 62% | 48% | 41% | 38% | 36%] [Feb 2024 | 100% | 58% | 44% | 39% | 35%] [Mar 2024 | 100% | 65% | 52% | 46%] Provide: 1. **Retention health assessment** — how does this compare to benchmarks? 2. **Critical drop-off points** — where are users leaving and when? 3. **Cohort comparison** — is retention improving, declining, or flat? 4. **Possible causes** — hypothesis for each major drop-off 5. **Retention experiments to run** — prioritized list of interventions 6. **Leading metrics** — what early signals predict long-term retention?
Example Output
Cohort Retention Analysis
**Health Assessment:** Your Month 1 retention (58-65%) is above average for SaaS (typical: 40-55%), but Month 3 (39-46%) and beyond needs attention. The positive sign: Month 3+ appears to stabilize — your 'loyal user floor' is forming.
Critical Drop-Off Points:
1. Month 0 → Month 1: Losing 35-42% in first 30 days 🚨 This is your biggest problem
2. Month 1 → Month 2: Additional 14-16% drop — secondary onboarding failure
3. Month 2 → Month 3: Drop slows to 6-8% — users who make it here tend to stay
**Cohort Trend:** February cohort shows slightly worse Month 1 (58% vs 62%) but March bounced back to 65% — investigate what changed. Product update? Onboarding change? Seasonality?
Top Experiments to Run:
1. In-app checklist for Day 1-7 (targets the Month 0→1 drop)
2. Personalized email trigger when user hasn't logged in for 5 days
3. 'Win back' campaign at Day 25 before first renewal decision
Tips for Best Results
- 💡Include your actual cohort table data for specific, accurate analysis rather than generic recommendations
- 💡Ask it to benchmark your numbers against your specific industry — SaaS and e-commerce have very different retention norms
- 💡Run monthly so you can catch retention problems early, before they become revenue problems
Related Prompts
Customer Churn Analysis Prompt
Analyze customer churn patterns to identify at-risk segments, root causes, and retention interventions.
Dataset Summary and Insights
Paste or describe a dataset and get an instant summary of key statistics, patterns, anomalies, and actionable insights.
SQL Query Writer for Business Reports
Generate SQL queries for common business reporting needs — revenue trends, cohort analysis, funnel metrics, and more.