Customer churn prediction using deep learning

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dc.authorscopusid55628294300
dc.authorscopusid57202924825
dc.authorscopusid55322301200
dc.contributor.authorSeymen, Ömer Faruk
dc.contributor.authorDoğan, Onur
dc.contributor.authorHızıroğlu, Abdülkadir
dc.date.accessioned2022-02-15T16:58:29Z
dc.date.available2022-02-15T16:58:29Z
dc.date.issued2021
dc.departmentBakırçay Üniversitesien_US
dc.description12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020 -- 15 December 2020 through 18 December 2020 -- -- 258059en_US
dc.description.abstractChurn studies have been used for years to achieve profitability and to establish a sustainable customer-company relationship. Deep learning is one of the contemporary methods used in churn analysis due to its ability to process huge amounts of customer data. In this study, a deep learning model is proposed to predict whether customers in the retail industry will churn in the future. The model was compared with logistic regression and artificial neural network models, which are also frequently used in the churn prediction studies. The results of the models were compared with accuracy classification tools, which are precision, recall and AUC. The results showed that the deep learning model achieved better classification and prediction success than other compared models. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-030-73689-7_50
dc.identifier.endpage529en_US
dc.identifier.isbn9783030736880
dc.identifier.issn2194-5357
dc.identifier.scopus2-s2.0-85105878020en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage520en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-73689-7_50
dc.identifier.urihttps://hdl.handle.net/20.500.14034/415
dc.identifier.volume1383 AISCen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.journalAdvances in Intelligent Systems and Computingen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCustomer churn modellingen_US
dc.subjectDeep learningen_US
dc.subjectPredictive analyticsen_US
dc.subjectRFMen_US
dc.subjectForecastingen_US
dc.subjectLearning systemsen_US
dc.subjectLogistic regressionen_US
dc.subjectNeural networksen_US
dc.subjectPattern recognitionen_US
dc.subjectSalesen_US
dc.subjectSoft computingen_US
dc.subjectArtificial neural network modelsen_US
dc.subjectChurn analysisen_US
dc.subjectChurn predictionsen_US
dc.subjectClassification toolen_US
dc.subjectCustomer churn predictionen_US
dc.subjectCustomer dataen_US
dc.subjectLearning modelsen_US
dc.subjectRetail industryen_US
dc.subjectDeep learningen_US
dc.titleCustomer churn prediction using deep learningen_US
dc.typeConference Objecten_US

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