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
Frequently Asked Questions
What is the Cohort Retention Analysis Explainer prompt?
Interpret your cohort retention data to understand user behavior, identify drop-off patterns, and improve retention strategies. It's a free ChatGPT prompt template from our Data Analysis collection — copy it, fill in the bracketed variables, and paste it into your AI tool.
Which AI tools work with this prompt?
It's written and tested for ChatGPT, Claude and Gemini. Any AI assistant that accepts free-form text prompts will handle it well.
How do I customize this ChatGPT prompt?
Replace the bracketed variables — such as [DAU/WAU/MAU, or revenue retention], [weekly / monthly], [Feb 2024 | 100% | 58% | 44% | 39% | 35%] — with your own details before running it. Include your actual cohort table data for specific, accurate analysis rather than generic recommendations
Is this prompt free to use?
Yes. Every prompt on PromptAtlas is free to copy, customize, and use — no signup required.
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