Deep Ensemble Learning for Customer Churn Prediction: A Comprehensive Overview

For subscription-based organizations to identify prospective churners and put customer retention tactics into place, customer churn prediction is essential. While classical machine learning techniques have shown their value, more precise and reliable results may now be obtained with the help of deep learning and ensemble techniques. This article examines deep ensemble learning, which blends ensemble methods like bagging, boosting, and stacking with deep learning models like RNNs, CNNs, and autoencoders. The comparison analysis demonstrates how deep ensemble learning outperforms standalone deep learning, hybrid models, and classical machine learning in terms of accuracy and robustness. Steps for a practical implementation are described, with an emphasis on feature engineering, data preprocessing, training models, integration, and evaluation. Deep ensemble learning shows to be a potent method for predicting customer attrition, enabling businesses enhance customer retention and overall performance through improved understanding and prediction of customer behavior, despite obstacles such computational complexity and data requirements.

Deep Ensemble Learning for Customer Churn Prediction: A Comprehensive Overview Keywords:

Customer Churn Prediction , Deep Ensemble Learning , Machine Learning , Deep Learning Models , Customer Retention Strategies