Customer Churn Prediction and Analytics for Subscription-based Services

Customer churn is one of the biggest challenges that any subscription-based service provider has to face, as it directly affects the stability of revenue and long-term growth. This paper presents the design of a data-driven churn prediction and analytics system using machine learning with statistical modeling. The proposed system analyzes customer behavior, subscription history, usage patterns, payment behavior, and service interactions to predict which customers are highly likely to churn. The model applies a variety of different data analytics techniques, including but not limited to exploratory data analysis, feature engineering, predictive modeling comprising Logistic Regression, Random Forest, and XGBoost, and customer segmentation, to provide strategic insights useful in retention strategy. This in turn shall help in making necessary proactive decisions toward reduction of churn rate and thereby enhancing CLV.