Customer segmentation in the retail sector: A data analytics approach

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dc.authorid0000-0002-8076-3678
dc.contributor.authorŞahinbaş, Kevser
dc.contributor.authorÇatak, Ferhat Özgür
dc.date.accessioned2022-11-18T06:55:19Z
dc.date.available2022-11-18T06:55:19Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractData analytics techniques are widely used in customer segmentation, which groups objects according to the similarity difference on each object and provides a high level of homogeneity in the same cluster or a high level of heterogeneity between each group. In this study, the behavior of customers in the retail sector was analyzed using customer segmentation data mining methods such as OPTICS, BIRCH, Agglomerative Clustuering, K-Means and DBSCAN algithms. The aim of the study is to investigate different data analytics algorithms using a private textile and retail company that has an agreement with e-commerce sites and marketplaces. OPTICS, BIRCH, Agglomerative Clustuering, K-Means have shown almost same clustering results, DBSCAN has outperformed with 0.206086 Silhouette value. The purpose of this paper is to provide a proof of concept of how e-commerce data analytics can be used in customer segmentation.
dc.identifier.citationŞahinbaş, K. ve Çatak, F. Ö. (2022). Customer segmentation in the retail sector: A data analytics approach. 14th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2022 içinde (174-178. ss.). Virtual, Hangzhou, 20-21 August 2022. https://doi.org/10.1109/IHMSC55436.2022.00048
dc.identifier.doi10.1109/IHMSC55436.2022.00048
dc.identifier.endpage178
dc.identifier.isbn9781665461696
dc.identifier.scopus2-s2.0-85141188638
dc.identifier.scopusqualityN/A
dc.identifier.startpage174
dc.identifier.urihttps://doi.org/10.1109/IHMSC55436.2022.00048
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9985
dc.indekslendigikaynakScopus
dc.institutionauthorŞahinbaş, Kevser
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof14th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectDeep Learning
dc.subjectSegmentation
dc.titleCustomer segmentation in the retail sector: A data analytics approach
dc.typeConference Object

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