HARNESSING MACHINE LEARNING MODELS FOR ACCURATE CUSTOMER LIFETIME VALUE PREDICTION: A COMPARATIVE STUDY IN MODERN BUSINESS ANALYTICS

This study evaluated multiple machine learning models to predict customer lifetime value (CLV), including ensemble methods, linear regression, and deep learning architectures. Our comparative analysis demonstrated that ensemble models, such as Random Forest and Gradient Boosting, provided a robust balance of accuracy and interpretability, while deep learning approaches excelled in capturing complex data patterns but required higher computational resources. The results highlight the trade-offs businesses must consider in selecting models based on data availability, scalability, and interpretability. The insights from this research enable businesses to identify high-value customers effectively, optimize marketing strategies, and drive sustainable profitability.