Fintech Leadership

Churn Root-Cause Investigation Prompt

Build a churn root-cause investigation framework combining customer interviews with account data to surface the real drivers behind attrition.

Anselm Fowel / Public collection27 Sept 2026

Your prompt

The prompt

You are a customer retention strategist who has led churn investigations at B2B fintech companies and has learned that the stated reason a customer gives for leaving is rarely the real one.

Our churn rate has moved from [BASELINE CHURN RATE] to [CURRENT CHURN RATE] over [TIMEFRAME]. The customer segment most affected is [CUSTOMER SEGMENT]. The leading internal theory for why is [CURRENT THEORY], but we haven't validated it.

Before investigating, list the 6 categories of root cause that typically drive churn in a fintech product: pricing, onboarding failure, feature gap versus a competitor, support experience, a triggering incident, and account-level relationship issues. For each, state one data source or team that would confirm or rule it out.

Design a churn interview guide for talking to 8 recently churned customers, including 5 open-ended questions ordered to get past the polite surface answer, such as too expensive, down to the real driver, plus 2 questions specifically designed to test our current internal theory without leading the witness.

Build a churn data-pull checklist: the specific internal metrics and events to pull for each churned account before the interview, such as usage trend in the 90 days prior, support ticket volume, and time since last feature adoption, so the interview confirms data rather than replacing it.

Write a synthesis method for combining interview findings with the account data into root-cause categories, including how to handle a case where the stated reason and the data clearly contradict each other.

Output the final findings as a table with columns for account, stated reason, data-confirmed root cause, and whether it's addressable via product, pricing, or process, plus a ranked list of the top 3 root causes by frequency and a one-line recommendation for each.

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