Retention Cohort Shock Analysis Builder
Analyze sudden retention drops or spikes in specific cohorts and isolate likely causes, impacted segments, and next investigative steps.
Prompt Template
You are a product analyst investigating an unexpected retention change. Build a cohort shock analysis from the details below. Business type: [SaaS, app, marketplace, subscription ecommerce, etc.] Retention metric: [day 7, week 4, month 3, renewal rate, etc.] Affected cohorts: [signup dates, acquisition source, plan, geography, device, etc.] Size of change: [drop or spike magnitude] Relevant product or business changes: [pricing, onboarding, release, policy, campaign, bug, seasonality] Available data: [cohort tables, event logs, support tickets, billing events, NPS, etc.] Hypotheses already discussed: [list] Provide: 1. Clear problem framing and scope 2. A ranked hypothesis list with what evidence would confirm or reject each one 3. Segment cuts to inspect first 4. Confounders and measurement traps to rule out 5. A step-by-step investigation plan 6. Possible business actions for each likely root cause 7. An executive summary in plain English Focus on separating true behavior change from instrumentation noise.
Example Output
Problem Frame
Month-2 retention fell from 41% to 33% for self-serve US cohorts acquired in February after the new onboarding flow launched.
Highest-Priority Hypotheses
1. Onboarding flow increased early confusion for low-intent users
2. Trial-to-paid billing bug removed some users from the retained population incorrectly
3. Paid social mix shifted toward weaker-intent traffic
First Cuts to Review
- Device: mobile web vs desktop
- Acquisition source: branded search vs paid social
- Activation completion within first 24 hours
Executive Summary
The sharp drop may be partly real and partly measurement-related. Check billing-state definitions first, then compare activation rates and acquisition mix before escalating product changes.
Tips for Best Results
- 💡Include recent launches and tracking changes, because retention shocks love wearing disguises.
- 💡Ask for confirm-or-reject evidence next to each hypothesis so the analysis leads to action, not just theories.
- 💡Mention the exact affected cohorts instead of saying retention fell in general.
Frequently Asked Questions
What is the Retention Cohort Shock Analysis Builder prompt?
Analyze sudden retention drops or spikes in specific cohorts and isolate likely causes, impacted segments, and next investigative steps. 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 [drop or spike magnitude], [list] — with your own details before running it. Include recent launches and tracking changes, because retention shocks love wearing disguises.
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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