A Comprehensive Review of Artificial Intelligence Models for Lifetime Value Optimization

Abstract

One of the most important financial metrics for evaluating a customer's long-term profitability to a firm is the customer lifetime value (CLV). The theoretical and empirical developments in CLV assessment are examined in this review study, as well as their uses in financial decision-making. It analyses the strengths and limitations of several CLV models, such as deterministic, probabilistic, machine learning, and deep learning approaches. The study also addresses the use of CLV in marketing expenses, customer segmentation, and firm value. It examines several kinds of predictive models, such as ensemble learning, neural networks, and regression-based approaches. It discusses their effectiveness as they function to enhance consumer segmentation and retention strategies. This review provides perspectives on optimizing CLV forecasts for more efficient customer relationship management by comparing various approaches.

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