Customer churn prediction in imbalanced datasets with resampling methods: A comparative study

Abstract

Customer churn presents a significant challenge for businesses in the era of subscription-based services because retaining customers plays a key role in sustained growth. Existing techniques for automatic churn prediction suffer from a primary challenge inherent in datasets as their significant disproportion between majority and minority classes, which may result in model bias favoring the dominant class. This study presents a comprehensive analysis of Customer Churn Prediction (CCP) with a focus on three public highly imbalanced datasets. The explored datasets span diverse business sectors, including telecommunications, online retail, and banking. We employ a comparative analysis regarding fourteen distinct classification methods considering popular resampling strategies, namely the Synthetic Minority Over-sampling Technique (SMOTE) and the Adaptive Synthetic Sampling (ADASYN). In particular, we investigate a specific configuration that combines a novel two-phase resampling method predicated on both clustering and ensemble techniques in conjunction with Long Short-Term Memory (LSTM) networks. Our findings demonstrate competitive effectiveness, underscoring its potential for effective imbalance correction by further enhancing prediction accuracy. Achieved results suggest that in almost all instances, the integrated approach outperforms the standalone methods across different scenarios in the three datasets, particularly in terms of the Area Under the Curve (AUC). This research represents a significant contribution to the field of churn prediction for addressing class imbalance.