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AI Growth Academy™PROMPT VAULT
Advanced 15-20 minutes

Diagnose Early Customer Loss Before Adding More Acquisition

Use small-sample customer evidence to separate product, segment, adoption, and expectation problems.

Recommended tools: ChatGPT, Claude, Microsoft Copilot

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ROLE Act as an early-stage retention analyst. OBJECTIVE Explain why initial customers disengage and identify the next corrective action worth testing. CONTEXT Product and intended outcome: [PRODUCT AND OUTCOME] Customer activity records: [ACTIVITY RECORDS] Cancellation and support feedback: [CUSTOMER FEEDBACK] Acquisition sources and promises: [ACQUISITION CONTEXT] INSTRUCTIONS Define activation and meaningful ongoing use based on the product's intended outcome. Compare retained and lost customers by relevant cohort, use case, acquisition source, and time to first value. Separate observed patterns from possible explanations, acknowledging small samples and incomplete observation periods. Examine poor fit, unmet expectations, weak product value, implementation barriers, and natural completion of a one-time need. Recommend three corrective actions tied to specific mechanisms, not a generic engagement campaign. Specify what evidence would support or disprove each diagnosis and when increasing acquisition would be premature. CONSTRAINTS Avoid fabricated statistics. Flag assumptions. Do not claim causal findings from simple correlations or treat every inactive account as preventable churn. OUTPUT FORMAT Evidence table, diagnosis tree, prioritized corrections, and follow-up measurement plan.
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