Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment

Authors : Omer Faruk SEYMEN, Emre ÖLMEZ, Onur DOĞAN, Orhan ER, Kadir HIZIROĞLU

Pages : 720-733

Doi:10.35378/gujs.992738

View : 275 | Download : 1366

Publication Date : 2023-06-01

Article Type : Research Paper

Abstract :Churn studies have been used for many years to increase profitability as well as to make customer-company relations sustainable. Ordinary artificial neural network insert ignore into journalissuearticles values(ANN); and convolution neural network insert ignore into journalissuearticles values(CNN); are widely used in churn analysis due to their ability to process large amounts of customer data. In this study, an ANN and a CNN model are proposed to predict whether customers in the retail industry will churn in the future. The models we proposed were compared with many machine learning methods that are frequently used in churn prediction studies. The results of the models were compared via accuracy classification tools, which are precision, recall, and AUC. The study results showed that the proposed deep learning-based churn prediction model has a better classification performance. The CNN model produced a 97.62% of accuracy rate which resulted in a better classification and prediction success than other compared models.
Keywords : Churn prediction, Convolution neural network, Artificial neural network, Deep learning