Run churn insight like a data project, not a prompt
Apply CRISP-DM phases to an AI-assisted customer-churn insight workflow.
The business question is vague: why are customers churning, and what should the team fix first? CRISP-DM: business understanding, data understanding, data preparation, modeling, evaluation, deployment. The common shortcut is asking AI for causes before defining the churn population, source limits, or how the answer will be used. Before Prompt: "Analyze these notes and tell us why customers churn." After Workflow: define decision, inspect source mix, prepare churned and retained samples, model patterns, evaluate evidence, deploy a caveated action memo. Business understanding Decision: choose one Q3 retention intervention for accounts under $50k ARR. The analysis now has scope and an…
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