Leveraging Neural Networks and Random Forest Algorithms for Enhanced Predictive Customer Behavior Analytics

Abstract

This research paper explores the synergistic integration of neural networks and random forest algorithms to enhance predictive analytics in customer behavior analysis. With the exponential growth of data in the digital commerce landscape, accurately predicting customer behavior has become crucial for businesses aiming to personalize marketing strategies and improve customer retention. This study proposes a hybrid model combining the strengths of neural networks—specifically their ability to capture non-linear relationships and complex patterns in data—with the robustness of random forest algorithms, known for their proficiency in handling structured data and minimizing overfitting through ensemble learning. The research employs a comprehensive dataset from a leading e-commerce platform, encompassing various customer interaction metrics such as purchase history, browsing patterns, and demographic information. The proposed hybrid model demonstrates superior performance in predictive accuracy, achieving a marked improvement over traditional single-method approaches. Through extensive experimentation, the model's efficacy is validated using cross-validation techniques and performance metrics such as precision, recall, and F1-score. Additionally, the study presents an in-depth comparative analysis, showcasing the advantages of the hybrid approach in addressing challenges related to data heterogeneity and interpretability. The findings underscore the potential of combining deep learning and ensemble algorithms as a potent tool for businesses to gain actionable insights and foster data-driven decision-making. This research contributes to the field by providing a scalable framework that can be applied across various industries, paving the way for advanced predictive modeling in customer analytics.