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

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dc.authoridHiziroglu, Abdulkadir/0000-0003-4582-3732
dc.authoridSEYMEN, OMER FARUK/0000-0003-2224-5546
dc.authoridER, Orhan/0000-0002-4732-9490
dc.authoridolmez, emre/0000-0003-1686-0251
dc.authorwosidHiziroglu, Abdulkadir/A-9036-2018
dc.contributor.authorSeymen, Omer Faruk
dc.contributor.authorOlmez, Emre
dc.contributor.authorDogan, Onur
dc.contributor.authorOrhan, E. R.
dc.contributor.authorHiziroglu, Abdulkadir
dc.date.accessioned2024-03-09T18:48:47Z
dc.date.available2024-03-09T18:48:47Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractChurn 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.en_US
dc.identifier.doi10.35378/gujs.992738
dc.identifier.endpage733en_US
dc.identifier.issn2147-1762
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85161458197en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage720en_US
dc.identifier.urihttps://doi.org/10.35378/gujs.992738
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1478
dc.identifier.volume36en_US
dc.identifier.wosWOS:001011280200016en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherGazi Univen_US
dc.relation.ispartofGazi University Journal of Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectChurn Prediction; Convolution Neural; Network; Artificial Neural Network; Deep Learningen_US
dc.titleCustomer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessmenten_US
dc.typeArticleen_US

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