Predict and prevent churn
Predict and prevent churn
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Design a churn prediction and prevention system for [SaaS/subscription product]. Define: leading indicators of churn (behavioral signals available in our data), how to build a churn risk score (describe model approach: logistic regression, ML, or heuristic rules), risk tier definitions (high/medium/low), automated intervention by tier, and how to measure the lift from interventions vs a control group.
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Pour un usage opérationnel, précisez l’horizon de prédiction (churn à 30/90 jours) et les sources de données (usage, facturation, support). Exigez un seuil de risque ajustable et un plan d’expérimentation avec groupe témoin pour chaque intervention. Cela rend le système directement actionnable et mesurable.
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