Harnessing AI to Predict and Prevent Telecom Churn: A Data-Driven Approach

Customer churn presents a major challenge for telecom firms, resulting in significant revenue declines and heightened expenses related to attracting new customers. By effectively predicting customer attrition, companies can execute focused retention strategies to alleviate these financial impacts. This research investigates advanced machine learning classification methods for forecasting customer churn in the telecom sector. Three classifiers were utilized—Decision Tree, Random Forest, and K-Nearest Neighbours (KNN)—yielding accuracy rates of 79%, 82%, and 79%, respectively. The Random Forest classifier achieved the highest accuracy, making it the most efficient tool for predicting churn. This study offers a dependable method for estimating customer turnover using machine learning classifiers, highlighting the exceptional performance of the Random Forest model. By employing these techniques, telecom companies can enhance customer satisfaction, decrease churn rates, and improve their retention strategies.