Enhancing Customer Lifetime Value Prediction Using Ensemble Learning and Long Short-Term Memory Networks

Abstract

This research paper explores the efficacy of combining ensemble learning techniques with Long Short-Term Memory (LSTM) networks to enhance the accuracy of predicting customer lifetime value (CLV), a critical metric for strategic decision-making in business management. The study addresses the limitations of traditional CLV prediction models, which often rely on linear assumptions and fail to capture complex, non-linear customer behavior patterns. We propose a hybrid model that integrates the temporal modeling capabilities of LSTM networks with the robustness of ensemble learning methods, such as Random Forests and Gradient Boosting Machines, to improve prediction performance. The model is trained and evaluated using a comprehensive dataset from a leading e-commerce platform, incorporating variables such as transaction history, customer demographics, and engagement metrics. Experimental results demonstrate that the proposed hybrid model outperforms baseline models, including conventional regression techniques and standalone LSTM models, by achieving a significantly lower mean squared error and higher predictive accuracy. Sensitivity analysis further reveals that the ensemble component enhances model stability, while the LSTM captures temporal dependencies effectively. This study contributes to the advancement of data-driven approaches for CLV prediction, providing actionable insights for practitioners seeking to optimize customer relationship management strategies and allocate marketing resources efficiently. Future research directions include exploring real-time model deployment and integrating additional data sources, such as social media interactions, to further refine prediction accuracy.