Customer Behavior Analysis by Intuitionistic Fuzzy Segmentation: Comparison of Two Major Cities in Turkey

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dc.authoridSeymen, Ömer Faruk/0000-0003-2224-5546
dc.authoridHiziroglu, Abdulkadir/0000-0003-4582-3732
dc.authoridDogan, Onur/0000-0003-3543-4012
dc.authorwosidSeymen, Ömer Faruk/GYV-2299-2022
dc.authorwosidHiziroglu, Abdulkadir/A-9036-2018
dc.authorwosidDogan, Onur/HPC-1959-2023
dc.contributor.authorDogan, Onur
dc.contributor.authorSeymen, Omer Faruk
dc.contributor.authorHiziroglu, Abdulkadir
dc.date.accessioned2024-03-09T18:48:36Z
dc.date.available2024-03-09T18:48:36Z
dc.date.issued2022
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractThe vast quantity of customer data and its ubiquity, as well as the inabilities of conventional segmentation tools, have diverted researchers in search of powerful segmentation techniques for generating managerially meaningful information. Due to its noteworthy practical use, soft computing-based techniques, especially fuzzy clustering, can be considered one of those contemporary approaches. Although there have been various fuzzy-based clustering applications in segmentation, intuitionistic fuzzy sets that have the complimentary feature have appeared in limited studies, especially in a comparative context. Therefore, this study extends the current body of the pertaining literature by providing a comparative assessment of intuitionistic fuzzy clustering. The comparison was carried out with two other well-known segmentation techniques, k-means and fuzzy c-means, based on transaction data that belong to Turkey's two major cities. Over 10,000 records of customers' data were processed for segmentation purposes, and the comparative approaches were presented. According to the results, the intuitionistic fuzzy clustering approach outperformed the other methods in terms of the clustering efficiency index being utilized. The validity of the segmentation structure obtained by the superior approach was ensured via nonsegmentation variables. The comparative assessment and the potential managerial implications could be considered as a contribution to the corresponding literature. This study also compares the effects of the different parameter values used in the proposed model.en_US
dc.identifier.doi10.1142/S0219622021500607
dc.identifier.endpage727en_US
dc.identifier.issn0219-6220
dc.identifier.issn1793-6845
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85116861101en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage707en_US
dc.identifier.urihttps://doi.org/10.1142/S0219622021500607
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1405
dc.identifier.volume21en_US
dc.identifier.wosWOS:000771946800009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWorld Scientific Publ Co Pte Ltden_US
dc.relation.ispartofInternational Journal of Information Technology & Decision Makingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCustomer Segmentation; Fuzzy Clustering; Intuitionistic Fuzzy C-Means; Statistical Methods; Marketing Perspective; Parameter Effectsen_US
dc.titleCustomer Behavior Analysis by Intuitionistic Fuzzy Segmentation: Comparison of Two Major Cities in Turkeyen_US
dc.typeArticleen_US

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