Enhancing Customer Lifetime Value Prediction Using Random Forests and Neural Network Ensemble Methods

Abstract

This research paper investigates the efficacy of enhancing customer lifetime value (CLV) prediction through the integration of Random Forests and Neural Network ensemble methods. CLV represents a critical metric for businesses, offering insights essential for developing customer-centric strategies and optimizing resource allocation. Traditional prediction models often struggle with the complexity and non-linearity inherent in customer behavior data. To address these challenges, we propose a hybrid model combining Random Forests, known for their ability to handle large feature spaces and prevent overfitting, with Neural Networks that excel in capturing complex patterns through deep learning. The ensemble approach leverages the strengths of both methodologies, aiming to improve prediction accuracy and robustness. We conducted comprehensive experiments using real-world datasets from diverse industries, employing rigorous cross-validation techniques to ensure the reliability of our findings. Our results demonstrate that the proposed ensemble model outperforms individual models concerning prediction accuracy, with a significant reduction in error rates and enhanced stability across various customer segments. This study provides empirical evidence supporting the adoption of ensemble learning strategies for CLV prediction, offering practical implications for marketers and data scientists seeking to refine customer relationship management systems. Further research could explore the integration of additional machine learning techniques, as well as the model's adaptability to dynamic market conditions.