MUMBAI, India, Sept. 7 -- Intellectual Property India has published a patent application (202621088794 A) filed by Dip Bharatbhai Patel on July 21, 2026, for Ai-Powered Customer Churn Prediction Monitoring System..
Inventor includes Dip Bharatbhai Patel.
The application for the patent was published on September 04, 2026, under issue no. 36/2026.
Abstract: ABSTRACT [505] Reliable and early identification of customers at risk of discontinuing a service constitutes a foundational requirement for sustainable subscription- based, telecommunications, banking, and e-commerce businesses; however, conventional churn-management approaches such as periodic manual account reviews, static rule-based flagging, and retrospective usage reporting remain inherently reactive, imprecise, and slow, thereby exposing enterprises to preventable revenue loss and delayed retention intervention. [510] Existing churn-analysis systems exhibit critical limitations including reliance on a narrow set of historical usage variables that fail to capture behavioural, transactional, and sentiment-based early-warning signals, insufficient integration of real-time monitoring pipelines capable of continuously updating churn risk as new customer activity occurs, absence of interpretable, per-customer risk scoring suitable for prioritising retention resources, and lack of automated alerting and recommended-action generation linked directly to predicted churn drivers. [515] The integration of multi-source data ingestion, engineered behavioural and transactional features, machine learning based predictive modelling, real-time monitoring and alerting, and automated retention-action recommendation presents transformative opportunities for developing a continuously operating, enterprise-deployable churn prediction and monitoring capability that is significantly more accurate and timely than traditional retrospective or rule-based churn-management approaches. [520] The present invention describes an AI-Powered Customer Churn Prediction Monitoring System and Method comprising a data ingestion and integration module that consolidates transactional, behavioural, support-interaction, and demographic data from multiple enterprise sources; a feature engineering module that derives predictive behavioural and engagement indicators from the consolidated data; a machine learning prediction module that computes a churn probability score for each customer using trained classification models; a real-time monitoring and alerting module that continuously updates churn risk as new customer activity is recorded and triggers alerts upon threshold breach; a customer risk scoring and segmentation module that ranks and groups customers by churn likelihood and value; and a retention action recommendation module that generates targeted intervention suggestions linked to the dominant churn drivers identified for each customer. [525] Comprehensive evaluation of the system across a benchmark dataset of enterprise customer records demonstrated churn prediction accuracy of 94.7 ± 1.2 percent, area under the receiver operating characteristic curve of 0.962 ± 0.008, precision of 92.3 ± 1.5 percent, recall of 90.8 ± 1.6 percent, false positive rate of under 5.1 percent, and mean end-to-end risk-score refresh latency of under 2.3 seconds following new customer activity ingestion. [530] These findings confirm that the AI-Powered Customer Churn Prediction Monitoring System and Method constitutes a significant advancement in customer relationship management and retention technology, with deployment applicability spanning subscription services, telecommunications operators, banking and financial services, and e-commerce platforms, thereby addressing the substantial unmet need for a highly accurate, timely, and actionable churn-prevention solution.
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