Machine Learning-Driven Customer Churn Analytics in Telecommunications: A Comprehensive Predictive Framework

Abstract

Customer churn prediction has emerged as a critical challenge for telecommunications enterprises confronting intensified market competition and service commoditization pressures. This investigation presents a comprehensive machine learning framework for predicting customer attrition utilizing real-world operational data from a major telecommunications provider. The dataset encompasses 7,043 customer records spanning 21 feature dimensions with a churn rate of 26.5%. The methodological approach integrates advanced data preprocessing techniques, sophisticated feature engineering strategies, and ensemble learning methodologies. The feature engineering approach synthesizes domain expertise with mathematical transformations, expanding the original feature space through business-relevant expert features and statistical transformations. A dual-stage feature selection methodology employs statistical hypothesis testing combined with machine learning wrapper methods. Seven state-of-the-art algorithms underwent evaluation, with Light Gradient Boosting Machine (LightGBM) demonstrating superior performance, achieving 84.8% churn detection accuracy. Economic impact analysis reveals potential net value generation of 150,000−200,000 per 1,000 customers under realistic cost assumptions, providing telecommunications executives with quantitative decision-support tools for customer retention optimization.