Profit-Driven Decision Analytics for Telecommunications Customer Retention: A Cost-Sensitive Stacking Ensemble Framework Optimized by an Improved Sparrow Search Strategy Algorithm

Customer retention in the telecommunications industry poses a critical challenge in data-driven business operations, while high predictive accuracy does not necessarily translate into superior commercial outcomes under asymmetric misclassification costs.This study investigated a profit-oriented decision analytics framework for customer churn management by integrating predictive performance with business value optimization.A costsensitive Cost-Sensitive Improved Sparrow Search Strategy Algorithm Stacking (CS-ISSA-Stacking) framework was developed by incorporating customer lifetime value (CLV) and marketing intervention costs into an Expected Total Profit (ETP) objective function.An Improved Sparrow Search Algorithm (ISSA) was constructed using Tent chaotic mapping and Cauchy mutation mechanisms to enhance global optimization capability.EXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) were employed as heterogeneous base learners, and ISSA was used to optimize ensemble fusion weights and decision thresholds for profit-driven prediction.The proposed framework could effectively improve ETP while maintaining competitive predictive performance.Compared with conventional prediction models optimized solely for classification accuracy, the proposed approach achieved a substantial improvement in customer retention profitability.The findings demonstrated that the optimized decision threshold significantly reduced the false negative rate in highvalue customer identification.Furthermore, SHapley Additive exPlanations (SHAP)-based interpretation revealed that monthly charges, contract type, and tenure were the most influential factors affecting customer churn behavior.Profit-sensitive decision mechanisms provided a more effective strategy than traditional accuracy-oriented prediction approaches in customer churn management.The proposed framework provides a practical decision-support tool for intelligent customer retention and offers new insights into integrating business analytics with strategically operational decision making in the competitive telecommunications environments.