Synthesize churn interview notes into a strategic action plan
Exit interviews generate insight that dies in a doc. This prompt extracts patterns from raw churn notes and converts them into prioritized retention initiatives with clear ownership.
I have conducted exit interviews with {{NUMBER_OF_CHURNED_CUSTOMERS}} churned customers from {{COMPANY_NAME}}. Here are my raw notes: {{RAW_CHURN_NOTES}}.
1. Identify and group the stated churn reasons into no more than five distinct themes. For each theme, state how many customers mentioned it and quote one specific phrase that best represents that theme.
2. Separate 'stated reasons' from 'inferred root causes.' For example, 'too expensive' is a stated reason; the root cause may be failure to demonstrate ongoing value. For each theme, propose the most likely root cause.
3. Classify each root cause as: (a) Product gap — something we don't do, (b) Execution gap — something we do poorly, or (c) Fit gap — we signed a customer who was never a strong match. This classification matters because the fixes are different.
4. Recommend one specific action for each root cause. For product and execution gaps, assign a likely owner (Product, CS, Sales). For fit gaps, describe the ICP characteristic that predicted churn.
5. Flag any churn reasons that appear in only one interview — these may be real signals or outliers, and we should not over-index on them without more data.
Do not suggest generic 'improve onboarding' actions unless the notes specifically point to onboarding as a cause. {{NUMBER_OF_CHURNED_CUSTOMERS}}{{COMPANY_NAME}}{{RAW_CHURN_NOTES}}
How to use this prompt
- Copy the prompt above (Copy button on the top-right).
- Replace each
{{VAR}}with your own value. Variables:{{NUMBER_OF_CHURNED_CUSTOMERS}}{{COMPANY_NAME}}{{RAW_CHURN_NOTES}}. - Paste it into one of the recommended tools below.
- Iterate: tighten constraints in the prompt if the output is generic.
Why this prompt is structured this way
The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.
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