// Link · 6 May 2026

Designing Data-Driven Intelligent Systems for Customer Lifecycle Optimization

Lifecycle optimization fails when it maximizes propensity instead of incremental value build event-time features, separate prediction from decision, log every exposure for counterfactual evaluation, and monitor for drift before the model corrupts its own training data.

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