Machine Learning System for Real-Time Customer Churn Forecast in Subscription Businesses

Abstract

Consumers play a crucial role in any business, and customer attrition could severely and adversely affect the sales and profitability in the long term. Therefore, customer turnover is an important aspect of business decision-making that requires analysis and prediction. This study seeks to use a popular customer churn data to make projections regarding the customer retention rate in the telecommunication industry. The suggested structured ML model includes such steps as correlation analysis, comprehensive data preprocessing (such as feature removing, label encoding, data splitting, and standardizing), and ensemble classifiers (inclusiveness of Random Forest, Gradient Boosting, XGBoost, and LightGBM). The parameters that guarantee assessment rely on vital measures, such as accuracy, precision, recall, F1measure, and ROC curve. The experimental findings corroborate the models' outstanding predictive ability, with all of them achieving accuracies of greater than 92%. While XGBoost emerged as the top model according to accuracy and F1-score, LightGBM's top ROC-AUC value demonstrated its superior discriminative capabilities. The analysis also pointed out customer tenure, monthly charges, and contract type as the main factors affecting churn. This research is significant because it provides a flexible and dependable churn prediction framework. This approach may be used by telecom service providers to identify high-risk consumers and develop customer-specific retention tactics. This will help them avoid revenue loss and increase customer satisfaction.