International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 101-107, January 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
R Priyadarshini
Thenneti Bhuvaneswari
Borra Aswini
Brahmanapalli Mobeena
Vedam Balu
B. Martin
Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.
Abstract: Customer churn is one of the biggest challenges that any subscription-based service provider has to face, as it directly affects the stability of revenue and long-term growth. This paper presents the design of a data-driven churn prediction and analytics system using machine learning with statistical modeling. The proposed system analyzes customer behavior, subscription history, usage patterns, payment behavior, and service interactions to predict which customers are highly likely to churn. The model applies a variety of different data analytics techniques, including but not limited to exploratory data analysis, feature engineering, predictive modeling comprising Logistic Regression, Random Forest, and XGBoost, and customer segmentation, to provide strategic insights useful in retention strategy. This in turn shall help in making necessary proactive decisions toward reduction of churn rate and thereby enhancing CLV.
Keywords: Customer Churn Prediction, Machine Learning, Predictive Modeling, Customer Segmentation, Customer Lifetime Value.
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