Machine Learning-Based Predictive Analytics for Customer Churn in the Telecom Industry

In the highly competitive telecom industry, customer churn significantly impacts revenue, with annual churn rates averaging 15–25%. Given that acquiring a new customer is 5–10 times more expensive than retaining an existing one, customer retention has become paramount. This study focuses on predicting customer churn in the Indian and Southeast Asian telecom markets, where prepaid models dominate, and churn prediction is particularly challenging. We analyzed customer-level data from a leading telecom firm over four months to build predictive models that identify high-risk churn customers. Using usage-based churn definitions, we focused on high-value customers, who contribute 80% of the revenue. Our approach involves extensive data cleaning, feature engineering, and the application of dimensionality reduction techniques like PCA, followed by model building using clustering, logistic regression, SVM, and random forest algorithms. Our results show that the random forest model performs best, with an accuracy of 99.97% and an F1-score of 0.998, significantly outperforming the other models. The insights derived from these models not only help in predicting churn but also in identifying key indicators of customer dissatisfaction, enabling targeted retention strategies. This study highlights the importance of proactive measures in customer retention and provides a robust framework for telecom companies to mitigate churn effectively.