Find the activation metric that predicts customer retention
A method to identify the early actions that separate retained users from churned ones, and how to drive them.
The prompt
Help me find the activation moment for my product: the early actions that predict a new user or customer sticks.
Product and core value: [WHAT it does and for whom]
What "retained" means for us: [e.g. "still active in week 8", "renewed after year 1"]
Events I track in the first days/weeks: [LIST: e.g. created project, invited teammate, connected integration, sessions in week 1]
Data available: [PASTE a summary by cohort or describe what I can export]
Current activation definition, if any: [DEFINITION and rate]
Steps:
1. List candidate activation events and combinations (e.g. "invited 2+ teammates in first 7 days").
2. Explain how to compare retention for users who did vs. didn't do each action, by cohort, and how to pick a threshold (the point where retention gains flatten).
3. If I've pasted data, run the comparison and show it.
4. Warn about correlation vs. causation: power users do everything. Suggest an experiment that pushes some users to the action to test whether it causes retention.
5. Recommend the activation metric, its target time window, and a target rate.
Then give me growth ideas to increase it: onboarding steps to remove, emails or in-app prompts to add, and sales/success touchpoints, each tied to the metric.
Return a short memo plus a table: Candidate action | Retention if done | Retention if not done | Gap | Notes. Don't fabricate figures; mark missing data [NEED].
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Data & Analysis prompts
What to fill in
- WHAT it does and for whom
- e.g. "still active in week 8", "renewed after year 1"
- PASTE a summary by cohort or describe what I can export
- DEFINITION and rate
- NEED
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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