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Customer Retention Probability Score

A daily customer score used to target retention actions before lapse. The test group converted roughly 60% better than the existing approach.

RetentionCustomer scoringProduction ML
Business problem

Retention spend was being used without a reliable view of which customers were likely to stop buying.

Key decision

Score repurchase probability every day and connect the score directly to retention actions instead of treating churn as a reporting metric after the customer was already lost.

What changed

Retention moved from broad targeting to a daily customer level decision input that teams could use across campaigns and product actions.

Takeaway

Prediction creates value only when it changes who the business acts on and what it does next.

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Open the full architecture, modeling, validation and lessons.