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Customer lifecycle optimization now requires real-time decision systems. Learn how data, models, and feedback loops drive growth.
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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.