Intelligent Churn Prediction and Retention System for Subscription Businesses

Project Code :TCMAPY2042

Objective

The objective of the "Intelligent Churn Prediction and Retention System for Subscription Businesses" is to use machine learning to predict which customers are likely to stop using a service (churn). The system helps businesses understand customer behavior by analyzing past data, such as usage patterns, customer interactions, and subscription details. By predicting churn early, businesses can take proactive steps to retain valuable customers, improve customer satisfaction, and reduce revenue loss due to churn.

Abstract

The Intelligent Churn Prediction and Retention System for Subscription Businesses is designed to predict customer churn in subscription-based businesses, providing valuable insights for improving customer retention strategies. The system uses machine learning models—specifically Decision Tree, Random Forest, and XG Boost—to predict whether a customer is likely to churn or not, based on various features like account age, monthly charges, payment methods, and customer engagement metrics. The system utilizes a dataset from Kaggle containing customer behaviour and subscription-related features. By automating churn prediction, businesses can proactively target high-risk customers with tailored retention measures, ultimately enhancing customer loyalty and reducing churn rates

Keywords: Customer Churn, Retention Strategies, Machine Learning, Decision Tree, Random Forest, XG Boost, Subscription Businesses, Churn Prediction, Predictive Analytics

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.