Predicting Customer Churn in Subscription-Based Enterprises Using Machine Learning
摘要
Customer churn is a critical challenge for subscription-based businesses, impacting revenue and profitability. Predicting churn behavior allows proactive retention strategies and efficient resource allocation. This research paper focuses on the application of machine learning techniques for customer churn prediction in subscription businesses. We explore various machine learning algorithms, data pre-processing techniques, feature engineering methods, and evaluation metrics to identify effective approaches for churn prediction. The study aims to provide subscription businesses with insights into developing accurate and scalable churn prediction models to enhance customer retention.