Published March 31, 2025 | Version v1
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Predictive Analytics for Customer Lifetime Value (CLV): Using Artificial Intelligence to Forecast Purchasing Behavior and Churn

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The importance of CLV has become a key indicator of long-term profitability and strategic marketing strategies. Traditional CLV models, based on averages or Recency-Frequency–Monetary analysis, fail to capture complex customer behaviors and market trends. In this study, the purpose of this research project is to design and evaluate AI-driven predictive models that more accurately predict CLV and identify customer churn using algorithms such as Random Forest, Gradient Boosting, and Neural Networks. A method to evaluate the ability of these approaches to perform regression is analyzed, processed, and modeled using anonymous transactional and behavioral data collected from e-commerce sources, and comparisons are made between the methods used for regression. In predictive accuracy, RMSE, MAE, and ROC-AUC will be used to evaluate assessment metrics such as RMSE, MAE, and ROC-AUC. This study expects to show that AI-based models improve predictive power and prove key behavioral factors that affect long-term value. The results will provide tangible insights for marketing teams that enable data-driven segmentation, resource-efficient resource optimization, and personalized retention strategy. Finally, this research points out the transformative value of machine learning to improve marketing efficiency, churn improvement, and sustainable customer relationships.

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