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@article{196930,
        author = {KUMARARAJA JETTI and MUPPARAJU SUPRAJA and THIPPIREDDY VENKATA BHARGAVI and SONTI THIRUPATHIRAO and VUKKEM LOKA KALYAN BABU and REPALLE GOWTHAM ADITYA},
        title = {An Intelligent Machine Learning Framework for Customer Churn Prediction and Retention Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {16251-16257},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196930},
        abstract = {Customer churn is a major challenge for businesses, as losing existing customers directly affects revenue and long-term growth. This study presents an intelligent machine learning framework designed to predict customer churn and support effective retention strategies. The proposed approach analyzes historical customer data, including usage patterns, transaction behavior, and service interactions, to identify customers who are likely to leave. In this work, specific machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Gradient Boosting (XGBoost) are applied and compared to determine the most accurate prediction method. The framework also includes a retention analytics component that helps organizations understand the key factors influencing churn and suggests targeted actions to improve customer satisfaction and loyalty. Experimental results show that the proposed system can accurately identify high-risk customers and provide useful insights for decision-making. This approach enables businesses to take timely actions, reduce churn rates, and enhance overall customer retention.},
        keywords = {Customer Churn Prediction, Machine Learning, Retention Analytics, Customer Behavior Analysis, Predictive Modeling, Classification Algorithms, Business Intelligence, Customer Segmentation.},
        month = {April},
        }