Customer churn hypothesis tree
A customer churn hypothesis tree turns a broad retention concern into smaller claims that can be tested. Start with the observed change, then separate product value, pricing, service and measurement explanations. Each leaf should point to evidence: cancellation feedback, adoption data, price cohorts or support records. The tree is useful in a weekly retention review because it makes assumptions visible and prevents a team from jumping directly to a feature request. Keep branches mutually distinct and replace a branch when its test rules it out.
Open it in the AI editor with a prompt pre-filled — keep what works, change what doesn't.
Scenario
A subscription team investigates an unexpected increase in cancellations.
Key decisions
- Start with evidence: Separate a real churn change from a reporting change.
- Test value: Use cancellation feedback to test product-fit assumptions.
- Test pricing: Compare the rate around the price change.
- Test service: Review unresolved support work as a retention risk.
When to reuse this
Use before assigning a solution owner, when several plausible explanations need evidence.
Frequently asked questions
What does a churn hypothesis tree test?
Should branches be mutually exclusive?
What belongs at a leaf?
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