Customer Churn: Patterns, Predictions, and Emerging Trends in ML-Driven Analytics

Abstract

Customer churn poses a substantial challenge for contemporary businesses, especially within the banking sector, where it is paramount for firms to cultivate long-lasting relationships with their clients in order to be profitable. This paper demonstrates how to use machine learning techniques to predict and subsequently mitigate customer churn in a more efficient manner. By analyzing a structured dataset from the banking sector, the study identifies key patterns in customer behaviour and evaluates multiple ML models. Including Logistic Regression, Random Forests, and Gradient Boosting for their predictive performance.

Through careful data preprocessing, addressing class imbalance with SMOTE, and analyzing model performance using the performance metrics, Random Forest was established as the preeminent model. The analysis also indicated important features correlated with churn - age, balance and estimated salary. Ultimately, the results highlighted the importance of ML informed analytics for creating tailored retention strategies, and offered actionable insights for academics and practitioners interested in improving customer loyalty through data-driven approaches.