Predictive Customer Lifetime Value: Transitioning from Segment-Based Classification to Probabilistic Revenue Forecasting

Earlier approaches to Customer Lifetime Value (CLV) have largely focused on segmentation techniques such as RFM analysis combined with clustering methods like K-Means [1]. While these methods are useful for identifying customer groups, they do not directly estimate how much value a customer will generate in the future. In this paper, we propose a predictive framework that moves beyond descriptive segmentation toward revenue forecasting. The approach combines the BG/NBD probabilistic model to estimate whether a customer is still active [2] with an XGBoost regression model to predict future spending [3]. This hybrid setup allows businesses to move from static customer grouping to a more dynamic and practical forecasting system.