Predicting customer churn using grey wolf optimization-based support vector machine with principal component analysis

Customer churn is a challenging problem that can lead to a loss of organizational assets. Organizations need to predict customer churn successfully in order to get rid of potential damages and gain a competitive advantage. The aim of this study is to provide a churn prediction model by including feature selection and optimization in classification. The study performs principal component analysis (PCA) to select the best features, support vector machine (SVM) to predict customer churn, and grey wolf optimization (GWO) to optimize the parameters of SVM. In other words, this study proposes a novel hybrid model called PCA-GWO-SVM to enhance the prediction ability in customer churn. A comparison experiment is carried out, evaluating the proposed model with the other classification algorithms. Experimental results show that the proposed PCA-GWO-SVM hybrid model produces higher accuracy, recall, and F1-score than other machine learning algorithms such as logit, k-nearest neighbors, naive Bayes, decision tree, and SVM.