Analysis and Optimization of Customer Lifetime Value Prediction using Machine Learning and Deep Learning Models by RFM Techniques

In today’s data-driven hospitality sector, customer interactions increasingly occur through digital platforms, generating extensive behavioral and transactional information. This study analyse the prediction of Customer Lifetime Value (CLV) using machine learning models—Linear Regression, Random Forest, and LightGBM—trained on features derived from hotel website interactions and booking records. After comprehensive data preprocessing, the models were evaluated using MAE, RMSE, and R² metrics. LightGBM achieved the highest predictive performance (R² = 0.504), followed by Random Forest (R² = 0.497), while Linear Regression underperformed (R² = 0.386), highlighting the advantages of non-linear models in modeling intricate customer patterns. Residual analyses confirmed LightGBM's stability and low bias across diverse customer profiles. Apart from prediction, the study applies Recency-Frequency-Monetary (RFM) analysis to segment customers into distinct value-based groups. These segments form the basis for tailored marketing strategies, allowing hotels to allocate resources more efficiently, enhance customer retention, and develop targeted campaigns aligned with customer potential. By integrating web-derived behavioral data with advanced modeling and segmentation, this research offers hotel managers practical tools for strategic planning in customer relationship management.