Resampling and Ensemble Strategies for Churn Prediction

Authors : Serra Çelik, Seda Tolun Tayali

Pages : 263-273

Doi:10.17671/gazibtd.1314870

View : 158 | Download : 168

Publication Date : 2023-10-31

Article Type : Research

Abstract :Churn analysis is a customer relationship management analytics that companies implement to predict the customers who are likely to terminate doing business with them. The success of marketing efforts to retain the existing customers is possible only if probable churners are correctly specified beforehand. Therefore, having powerful models with high prediction capabilities that lead to a profit growth is crucial. The imbalanced nature of churn datasets negatively effects the classification performance of machine learning methods. This study examines resampling –over- and under-sampling- and ensemble learning –bagging, boosting, and stacking– strategies integrated with the cross-validation procedure on imbalanced churn prediction. The experimental results, which are compared to the results of Support Vector Machines taken as the benchmark, show that ensemble methods improve the prediction performances. Also, applying over-sampling achieves a noticeable performance in comparison with the under-sampling approach.
Keywords : kayıp tahmini, sınıf dengesizliği, yeniden örnekleme, destek vektör makineleri, değerlendirme metrikleri, topluluk öğrenme