Customer Churn Analysis Using Python and Data Analytics for Telecom Industry

Abstract: The global telecommunication sector is currently navigating an era of rapid transformation driven by intense market competition. This paper work explores the intersection of data science and business intelligence, examining how analytical methodologies can be leveraged to mitigate customer churn—the rate at which subscribers terminate service relationships. Through a comprehensive analysis of telco datasets, this study demonstrates that identifying "at-risk" behaviour before departure is critical for revenue stability, as the cost of acquiring new customers is 5 to 6 times higher than retaining existing ones. We present a data-driven framework for churn assessment that incorporates a specialized Injection Layer for automated log collection and data handling, an Analytical Layer utilizing an engine for deep-dive SQL querying, and a Visualization Layer for interactive reporting via Power BI. The findings reveal that significant predictors of churn include high monthly charges and short-term contract types. Furthermore, the implementation of an ML-based optimization framework achieved an accuracy score of 0.843 using an Extra Trees Classifier, proving that predictive modelling can achieve 40-60% reductions in attrition when integrated into core management strategies. This study contributes to the field by providing actionable insights for organizations seeking to optimize resource allocation while maintaining long-term organizational sustainability.

Keywords: Analytical Engine, Customer Churn Management, Injection Layer, Predictive Modelling.