Enhancing Predictive Customer Retention Using Machine Learning Algorithms: A Comparative Study of Random Forest, XGBoost, and Neural Networks

Abstract

This research paper investigates the efficacy of machine learning algorithms in enhancing predictive customer retention, a critical component for sustaining competitive advantage in today’s dynamic business environment. The study conducts a comparative analysis of three prominent machine learning models: Random Forest, XGBoost, and Neural Networks, each known for their robust capabilities in classification tasks. Utilizing a comprehensive dataset from a multinational retail company, the study first preprocesses the data to address class imbalance through techniques such as SMOTE and applies feature selection methods to optimize model performance. Each model is rigorously trained and tested, with hyperparameters fine-tuned through grid search methods to ensure optimal performance. The evaluation metrics used include accuracy, precision, recall, F1-score, and area under the ROC curve (AUC) to provide a holistic assessment of each algorithm’s predictive power. Results indicate that while all three models demonstrate significant potential in predicting customer churn, Neural Networks outperform the others in terms of precision and AUC, attributing this success to their ability to model complex non-linear relationships in the data. The paper concludes with a discussion on the practical implications of these findings, suggesting that businesses can leverage these insights to deploy more effective customer retention strategies. Additionally, the paper highlights avenues for future research, including exploring hybrid models and incorporating additional data sources to further refine predictions.