The article examines how artificial intelligence reshapes churn prediction and customer lifetime value (CLV) management in data-driven marketing. Relevance follows from the growing pressure to align acquisition and retention spending with incremental value under volatile demand and privacy constraints. Novelty lies in integrating uplift-based policy learning, survival-time horizon modeling, and early-signal CLV for media bidding into a single operating blueprint for marketing and sales leaders. The study describes current model families for churn and CLV, reviews evidence on explainability pipelines for managerial sign-off, and evaluates incrementality-aware targeting that reallocates spend from probability-only ranking to causal response. Special attention is given to cold-start acquisition, where CLV must inform bidding before behavioral depth accumulates. The aim is to synthesize actionable guidance that links model choice to levers in pricing, service, offer design, and paid media. Methods include comparative reading, structured content analysis, and decision-matrix synthesis. The conclusion outlines a practitioner-ready cadence for screening, selection, bidding, and governance. The article will benefit CMOs, heads of sales, growth teams, and analytics leaders building durable, value-aligned programs.

churn prediction, customer lifetime value, uplift modeling, survival analysis, explainable AI, generative AI, B2B SaaS, media bidding, CAC payback, retention strategy

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