Evaluating the Effectiveness of Different Machine Learning Models in Predicting Customer Churn in the USA

Abstract

 Customer churn is deemed as the process by which customers stop using the product or service of a company now the burning issue facing organizations in the USA across all sectors: telecommunication, retail, banking, and subscription-based services. In the current competitive marketplace, companies in America face substantial challenges in retaining customers. The utmost objective of this study was to compare the performance of various machine learning algorithms in terms of predicting customer churn, thereby identifying the most effective techniques for accurately forecasting churn within US businesses. The scope of this study focused on contrasting machine learning algorithms for customer churn forecasting using extensive datasets derived from US businesses across various industries. The dataset of customer churn applied in this study is a rich set of data points developed to capture several dimensions of customer behavior and interaction with the business. For predicting customer churn, distinctive machine learning algorithms were considered, notably, Logistic Regression, Random Forest, and Gradient Boosting. The performance evaluation metrics of the models encompassed accuracy, precision, recall, F1-score, and ROC-AUC. While all three models perform similarly, SVM appeared to have the highest accuracy among the three algorithms. The adoption of machine learning for churn prediction extends considerable benefits for businesses in the United States alone, especially in highly competitive fields like telecommunications, retail, and subscription services. By leveraging predictive analytics, firms can identify high-risk customers and proactively engage with them to reduce churn rates and thereby improve customer loyalty.