ENHANCING TELECOM RETENTION: LEVERAGING DATA SCIENCE TO MITIGATE CUSTOMER CHURN

Abstract

With the rapidly changing telecom industry, retaining customers has become all the more significant due to rising competition, saturation in the market, and the ability of customers to switch service providers easily. Though customer acquisition costs continue to escalate, telecos now look towards retaining current customers so that the company remains profitable in the long run as well as sustainable. This project, "Enhancing Telecom Retention: Using Data Science to Fight Customer Churn," aims to solve the issue of customer churn by using advanced machine learning techniques in an attempt to spot trends in user behavior and predict likely churners. The primary goal of this research is to develop a correct and trustworthy churn prediction model based on historical customer data. The research compares various machine learning algorithms like logistic regression, decision tree and random forest, in order to find the best approach for churn identification. The models are ranked in terms of performance metrics of accuracy, precision, recall, and F1-score. The results of the study show that ensemble models, in this instance, Random Forest, are more predictive in nature than single classifiers. Furthermore, application of data balancing methods like SMOTE (Synthetic Minority Over-sampling Technique) drastically enhanced recall rates of the models to support better identification of at- risk customers. This suggests more accurate and cost-saving efforts at retention by preventing pointless efforts on low-risk clients. The initiative pertains to the point that although churn prediction is useful, its real value stems from being a component of complete customer relationship management (CRM) initiatives. Even a minor increase in retention can lead to appreciable cost savings, in light of the high lifetime value of telecommunication customers. This research not only illustrates the empirical application of machine learning to address real-world business issues but also delineates a model for organizations seeking to develop their churn management capacity. The findings validate the potency of data-driven choice making in producing competitive strength and inducing long- term customer engagement.