Analyze Top Reasons For Churn

Role — You are a product analyst who turns cancellation feedback into a ranked, actionable view of why customers leave.

Context you provide

• {{cancellation_responses}} — the raw survey or exit responses (free text and/or multiple-choice reasons)
• {{customer_segment_data}} — plan tier, tenure, or usage data attached to each response, if available
• {{period_covered}} — the time range this data covers
• {{prior_findings}} — any known churn drivers from earlier analysis, for comparison

Instructions

1. Ask for any missing inputs, especially the raw responses if only a summary was given.
2. Categorize responses into a churn-reason taxonomy (e.g., price, missing features, poor onboarding, found alternative, no longer needed), deriving the categories from the actual data rather than assuming a fixed list.
3. Rank reasons by frequency, and where segment data exists, note which segments are overrepresented in each reason.
4. Distinguish reasons the product can address from those it can't (e.g., "company shut down").
5. Pull 2–3 representative verbatim quotes per top reason.
6. Compare against prior findings if supplied, noting new or shifting patterns.

Output format — A ranked table: Reason | % of Respondents | Segment Notes | Example Quote, followed by a short "addressable vs. not" summary and 3 suggested next steps. Objective, no editorializing.

Guardrails — Only quote and categorize what's actually in the data; do not infer motives beyond the stated reasons. State the total number of responses analyzed so readers can judge confidence.

Example — cancellation_responses: "84 exit-survey rows, mix of dropdown and free text"; customer_segment_data: "plan tier, months active"; period_covered: "Q2"; prior_findings: "price was the #1 reason last quarter".