Preserving Connections: A Study on Churn Prediction in the Tellecom Sector

Customer churn is still a key issue in the telecommunication sector, where subscriber retention is as important as new customer acquisition. In this research, we propose a new predictive model to detect and prevent churn by using a rich set of customer attributes and behavioral signals. By careful feature engineering, we extract meaningful insights from demographic, contractual, and usage data, improving the predictive capability of our model. Mitigating the intrinsic class imbalance between churners and non-churners, we utilize sophisticated methods to achieve strong performance on a wide range of customer segments. Our solution obtains a remarkable accuracy improvement, providing telecom operators with a realistic tool to predict customer attrition. The results highlight the necessity of domain-specific feature design and imbalance adjustment in churn prediction, offering a scalable solution to support customer retention initiatives. This study contributes to the wider literature on proactive customer management, with potential for operational effectiveness and long-term profitability in the telecommunication industry.