INTERPRETABLE CUSTOMER CHURN ANALYSIS IN TELECOMMUNICATION INDUSTRY

Customer churn prediction has become a crucial research area in the telecommunication industry due to the increasing competition among service providers and the high cost associated with acquiring new customers. Retaining existing customers is significantly more cost-effective than acquiring new ones, making churn analysis an essential business strategy. This study presents an interpretable customer churn prediction framework that combines machine learning techniques with explainable artificial intelligence to provide accurate predictions along with understandable insights for decision makers. The proposed system utilizes telecom customer behavioral data such as tenure, service usage, billing information, and contract type to identify patterns that influence churn behavior. Advanced machine learning algorithms, particularly Extreme Gradient Boosting (XGBoost), are employed to classify customers into churn and non-churn categories based on predictive features extracted from historical datasets. To enhance model transparency and interpretability, the system integrates explainable AI techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). These methods provide both global and local explanations by identifying the most influential features affecting churn probability for individual customers and the overall dataset. The developed platform also incorporates a web-based architecture using modern technologies to enable telecom analysts and managers to interact with predictive results through visual dashboards. By combining predictive analytics with interpretable machine learning, the system enables organizations to better understand the drivers of customer attrition and design targeted retention strategies. The proposed approach improves decision-making transparency, enhances trust in automated analytics systems, and supports proactive customer relationship management in the telecommunication sector.