Enhancing Predictive Customer Retention Using Random Forest and Neural Network Algorithms in AI-Driven Models

Abstract

This research paper explores the application of Random Forest and Neural Network algorithms to enhance predictive customer retention strategies within AI-driven models. The study addresses the growing challenge of customer churn across industries and emphasizes the need for sophisticated analytical tools to accurately predict and mitigate potential losses. The paper first provides a comparative analysis of existing churn prediction models, highlighting their limitations and the potential improvements offered by machine learning techniques. We then introduce a hybrid model that leverages the strengths of Random Forest's feature selection capabilities and the deep learning prowess of Neural Networks. This hybrid approach aims to improve prediction accuracy and provide actionable insights into customer behavior. The dataset used for training and validation is sourced from a leading telecommunications company, comprising historical customer interactions, transaction data, and behavioral metrics. Our experimental results demonstrate that the proposed model significantly outperforms traditional statistical methods and individual algorithm approaches in terms of prediction accuracy, precision, and recall. Additionally, the model's interpretability is enhanced through feature importance analysis, which aids in identifying key factors influencing customer retention. The findings of this study suggest that integrating Random Forest and Neural Networks can effectively balance model complexity and predictive power, offering a robust framework for organizations seeking to optimize their customer retention strategies. The paper concludes with a discussion on the practical implications of the model, potential industry applications, and directions for future research to further refine predictive capabilities in AI-driven customer relationship management.