Enhancing Customer Churn Prediction in Telecommunications: An Adaptive Ensemble Learning Approach
This work addresses customer retention challenges for the telecommunications industry, but it is incremental as it combines existing models with ensemble techniques.
The paper tackled customer churn prediction in telecommunications by proposing an adaptive ensemble learning framework, achieving a 99.28% accuracy on three datasets, which improves over state-of-the-art methods.
Customer churn, the discontinuation of services by existing customers, poses a significant challenge to the telecommunications industry. This paper proposes a novel adaptive ensemble learning framework for highly accurate customer churn prediction. The framework integrates multiple base models, including XGBoost, LightGBM, LSTM, a Multi-Layer Perceptron (MLP) neural network, and Support Vector Machine (SVM). These models are strategically combined using a stacking ensemble method, further enhanced by meta-feature generation from base model predictions. A rigorous data preprocessing pipeline, coupled with a multi-faceted feature engineering approach, optimizes model performance. The framework is evaluated on three publicly available telecom churn datasets, demonstrating substantial accuracy improvements over state-of-the-art techniques. The research achieves a remarkable 99.28% accuracy, signifying a major advancement in churn prediction.The implications of this research for developing proactive customer retention strategies withinthe telecommunications industry are discussed.