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@article{180045,
        author = {Tushar Kumar Sharma and Tushar Srivastava and Utkarsh Mishra and Varun Mishra and Kajal Gehlot},
        title = {AI-Driven Churn Prediction For SaaS Businesses},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {921-926},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180045},
        abstract = {SubAnalytics is an AI-powered subscription analytics tool for SaaS businesses. It uses machine learning models to predict customer churn, calculate churn metrics, identify high-risk subscribers and analyze subscription plan distributions. SubAnalytics is built with a modular architecture using a MongoDB-backed Express.js backend and a Python-based predictive microservice. Users can upload anonymized customer data via a secure web interface and the tool will generate visual and textual insights. This paper evaluates the performance of the prediction model, usability of the tool, and real-world applicability for improving customer retention strategies. The results show SubAnalytics can support data-driven decision-making in subscription-based businesses.},
        keywords = {Subscription Analytics, Customer Churn Prediction, SaaS, Predictive Modeling, Business Intelligence, Express.js, MongoDB},
        month = {June},
        }