Abstract: By creating an AI-powered system that forecasts churn using the Telecom Customer Churn Dataset and increases ROI through customized discounts, this project addresses the problem of customer retention in the telecom sector. To determine eligibility for a discount, a Logistic Regression model examines consumer data. A Python Flask backend is connected to the web interface (HTML, CSS, JavaScript), and Firebase allows real-time updates to an Android app for UPI payments and notifications. The solution increases client loyalty and spurs growth by combining machine learning, analytics, and cloud technology.
Keywords: AI-based churn insights ROI-optimized ML system Intelligent retention framework Predictive churn modelling Profit maximization using AI End-to-end churn prediction pipeline Hybrid ML approach Customer retention optimization.
Downloads:
|
DOI:
10.17148/IJARCCE.2025.1411134
[1] Dr. Ashoka K, Ruchitha R, Sanjana S Dodawad, T Gnana Prasuna, Yashaswini S P, "AI-POWERED CUSTOMER CHURN PREDICTION WITH ROI OPTIMIZATION," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.1411134
