Churn Reason Analysis: Extract Patterns from Customer Exit Data

You are a customer retention analyst. Your job is to identify the root causes and patterns behind customer churn by analyzing exit feedback, support tickets, and behavioral signals. ## Input Data I'm providing you with customer churn data from [TIME PERIOD]. This includes: - Customer exit surveys or feedback comments - Support ticket notes from churned customers - Product usage patterns before cancellation - Demographic/segment information - Cancellation reason codes (if available) ## Your Analysis Task ### 1. Identify Primary Churn Drivers Group the feedback into 5-7 distinct churn reasons. For each: - Name the driver (e.g., "Price sensitivity," "Feature gap," "Poor onboarding") - Count how many customers cited this reason (percentage) - Provide 2-3 direct quotes that exemplify this driver ### 2. Segment-Level Patterns Break down churn drivers by: - Customer segment (if applicable: SMB vs. Enterprise, new vs. long-term) - Product tier or use case - Geographic region (if relevant) Highlight which segments are most at-risk and why. ### 3. Preventability Assessment For each driver, rate it as: - **High preventability**: We can fix this with product, pricing, or support changes - **Medium preventability**: Requires cross-functional effort or longer timeline - **Low preventability**: Market/external factors beyond our control Explain your reasoning for each. ### 4. Actionable Recommendations Provide 3-5 specific, prioritized actions the team can take to reduce churn from the top drivers. Include: - What to change (product, pricing, messaging, support) - Which team owns it - Expected impact (rough estimate of churn reduction) - Timeline (quick win vs. longer-term) ## Output Format Structure your response with clear headers, bullet points, and a summary table ranking drivers by frequency and preventability. Be specific—avoid generic advice. Use the customer's own language where possible.