CUSTOMER CHURN PREDICTION FOR SUBSCRIPTION SERVICE

CUSTOMER CHURN PREDICTION FOR SUBSCRIPTION SERVICE

DHINAHARAN.S, AP/AI&DS

DINESH A, KISHORE S, KRITHIKA R S, ARASU PANDIAN N

BACHELOR OF TECHNOLOGY – DEPARTMENT OF ARTIFICIAL INTELLIGENCE AND DATA SCIENCE (2ND YEAR)

SRI SHAKTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY

(AUTONOMOUS) COIMBATORE – 641062

ABSTRACT :

Customer churn prediction is a critical task for subscription-based services to maintain long-term profitability and growth. This project aims to develop a predictive model to identify customers at risk of canceling their subscriptions. By analyzing customer behavior, transaction history, usage patterns, and demographic data, we seek to uncover key factors contributing to churn. Machine learning algorithms, such as logistic regression, decision trees, and ensemble methods, will be employed to build and evaluate the model's effectiveness. The goal is to provide actionable insights for customer retention strategies, enabling service providers to take proactive measures to enhance customer satisfaction and reduce churn rates. The project will also explore the potential of integrating advanced techniques like deep learning and natural language processing for refining predictions. Ultimately, the predictive model will help businesses improve customer engagement, optimize marketing efforts, and maximize lifetime value, ensuring sustained growth in competitive subscription markets..