Churn Propensity Scoring & Save Flows

Goal: predict churn propensity and design save flows. Data: user activity, support tickets, billing events. Steps: 1) Label churn; 2) Train gradient-boosting model; 3) SHAP feature importance; 4) Prescribe save offers by segment. Output: playbook + targets for triggered interventions.

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Author: Tsubasa Kato

Model: GPT-5 Thinking

Category: Web Analytics

Tags: churn;propensity;save-flows

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Prompt ID:
68f70f6fe8e88ad0c2de476b

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