Artificial Intelligence-Driven Customer Lifetime Value (CLV) Forecasting: Integrating RFM Analysis with Machine Learning for Strategic Customer Retention

Abstract

Customer Lifetime Value (CLV) is a critical metric in marketing analytics, enabling businesses to assess long-term profitability and optimize customer retention strategies. Traditional CLV models rely on heuristic approaches such as Regency, Frequency, and Monetary (RFM) analysis, but the advent of Artificial Intelligence (AI) and Machine Learning (ML) has significantly enhanced predictive capabilities. This study explores the integration of AI-driven ML algorithms with RFM analysis to improve CLV forecasting accuracy and enable more personalized customer engagement strategies. By leveraging supervised learning models, such as regression algorithms, decision trees, and neural networks, organizations can segment customers more effectively and predict future purchasing behaviors with greater precision (Lemmens & Gupta, 2020). Moreover, AI-driven approaches allow for dynamic CLV computation, adjusting to real-time customer interactions and behavioral shifts, thereby optimizing retention efforts and marketing expenditures (Gupta & Zeithaml, 2021). The study also evaluates the efficacy of clustering techniques, such as k-means and hierarchical clustering, in refining customer segmentation for targeted marketing interventions (Kumar et al., 2022). Findings suggest that integrating AI-based ML models with RFM analysis significantly improves the accuracy of CLV predictions, leading to higher customer retention rates and long-term business sustainability. This paper contributes to the growing body of literature advocating for AI-driven marketing analytics, demonstrating the strategic advantages of data-driven decision-making in customer relationship management.