Customer Churn Prediction and Retention Strategies through Machine Learning, Chatbots, and Recommendation Systems

Author(s)	Mr. Vaibhav Jaiswal, Mr. Adithya Vinod, Mr. Ashish John, Mr. Harsh Sane, Ms. Ankita Verma
Country	India
Abstract	In the highly competitive telecom industry, maintaining existing customers is just as important as attracting new ones. Predicting customer churn allows operators to recognize which users are most at risk of leaving, making it possible to implement timely retention strategies. This study creates a predictive model based on machine learning, utilizing demographic, behavioral, and service-related variables. Following data preprocessing and feature engineering, various models were tested, with ensemble methods demonstrating the highest accuracy. To enhance practicality, two improvements were added: a domain-specific chatbot for interactive insights on churn and a recommendation module that devises customized retention strategies based on churn likelihood and significant contributing factors. The integrated system not only forecasts churn but also offers actionable advice, contributing to customer satisfaction and sustained profitability.
Keywords	Customer churn prediction, machine learning, telecom industry, customer retention, recommendation system, chatbot.
Field	Computer
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-10-07

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