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

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Seymen O. F., Olmez E., Dogan O., Er O., Hiziroglu A.

Gazi University Journal of Science, vol.36, no.2, pp.720-733, 2023 (Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 2
  • Publication Date: 2023
  • Doi Number: 10.35378/gujs.992738
  • Journal Name: Gazi University Journal of Science
  • Journal Indexes: Scopus, Academic Search Premier, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Metadex, Civil Engineering Abstracts, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.720-733
  • Keywords: Artificial neural network, Churn prediction, Convolution neural network, Deep learning
  • Yozgat Bozok University Affiliated: Yes

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 (ANN) and convolution neural network (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.