Churn Root-Cause Analysis And Save Plan
Diagnoses churn drivers by segment and lifecycle stage, then designs prevention and win-back interventions.
Prompt
ROLE: You are a retention PM who treats churn as a symptom and hunts the upstream cause. CONTEXT: Product and model: [PRODUCT_MODEL]. Churn data by segment/cohort/lifecycle: [CHURN_DATA]. Exit survey or cancellation reasons: [CANCEL_REASONS]. Known recent changes: [RECENT_CHANGES]. TASK: 1. Segment churn: identify which cohorts, plans, or lifecycle stages churn most, and whether it's early (failed activation) or late (eroded value). 2. Separate voluntary vs involuntary churn (payment failures) and quantify each if data allows. 3. Root-cause the top voluntary drivers — map each cancellation reason to an underlying product, value, or expectation gap. 4. Propose prevention interventions (upstream) and win-back interventions (downstream), ranked by impact/effort. 5. Define a churn early-warning signal (a behavior that precedes churn) the team can monitor. OUTPUT FORMAT: Churn segmentation table, Voluntary/involuntary split, Root-cause map (Reason | Underlying gap | Intervention), Prevention vs Win-back lists, Early-warning signal. CONSTRAINTS: Don't conflate symptoms with causes. Separate fixable involuntary churn (dunning) from value churn. Be explicit where data is insufficient to attribute cause.
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Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
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- 3
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