Abstract. Customer retention is fundamental to the growth of customer-centric businesses, especially in highly competitive subscription-based markets where customers can easily switch providers. High customer churn—the tendency of customers to stop engaging with a brand—can lead to significant material losses, reputational damage, and increased customer acquisition costs. This article reviews key strategies for proactive churn management using Machine Learning (ML) models. The findings indicate that integrating behavioral features with segmented ML models significantly enhances predictive accuracy compared to baseline models. The study discusses the essential steps in the ML workflow for churn prediction, including data collection, feature engineering, and customer segmentation. By identifying the research gap in real-time intervention strategies, this paper provides a framework for aligning ML outputs with marketing analytics.

Keywords: machine learning, economic sciences, cloud computing, customer churn, reinforcement learning.