Comparative Analysis of Machine Learning Algorithms for Customer Churn Prediction in the USA

Abstract

Customer churn prediction has become a critical task for businesses aiming to retain customers and enhance profitability, particularly in competitive markets such as the USA. This study conducts a comparative analysis of widely used machine learning algorithms, including Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machines (SVM), and Neural Networks, for predicting customer churn. The research leverages a dataset from a U.S.- based subscription service, evaluating models based on accuracy, precision, recall, F1-score, and AUC-ROC metrics. Key factors influencing churn, such as customer demographics, usage patterns, and service interactions, are analyzed to assess feature importance. The findings reveal that ensemble methods, particularly Gradient Boosting, outperform other algorithms in terms of predictive accuracy and robustness, while simpler models like Logistic Regression offer interpretability and ease of implementation. This study provides actionable insights for businesses to adopt the most suitable machine learning techniques to proactively address customer churn and enhance retention strategies.