Hybrid Ensemble Learning for Predicting Customer Churn in Subscription-Based Services
Abstract
Customer churn prediction is critical for subscription-based businesses aiming to retain users and optimize revenue. This paper introduces a hybrid ensemble learning approach combining gradient boosting, random forests, and deep learning to enhance churn prediction accuracy. The model leverages customer behavioral data, demographics, and usage patterns to identify churn signals. Experimental results on real-world datasets from the telecommunications and streaming industries demonstrate significant improvements in prediction performance compared to individual models. The findings provide actionable insights for developing targeted retention strategies.
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