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@article{180931,
        author = {Abhinav Kale and Manav Jha and Ninad Bomanwar and Chanksh Dubey and Pratik Golder},
        title = {Customer Churn Prediction Leveraging Multivariate Linear Regression},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3721-3724},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180931},
        abstract = {In subscription-driven industries, the risk of losing customers—commonly referred to as churn—threatens both revenue and growth. This project constructs a predictive analytics platform that proactively flags subscribers likely to churn by employing a multivariate linear regression model. Built using Next.js, Prisma ORM, and a Neon (Postgres) database, our solution calculates a churn probability score for each user based on features such as subscription tier, days since last login, number of events in the previous 30 days, and generated revenue. The platform includes a live analytics dashboard, comprehensive reporting tools, and AI-powered retention recommendations, enabling businesses to strengthen engagement efforts and curb subscriber turnover.},
        keywords = {Customer churn prediction, Multivariate linear regression, Next.js, Prisma ORM, Neon DB, AWS SES},
        month = {June},
        }