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

Onur Dogan, Omer Faruk Seymen () and Abdulkadir Hiziroglu ()
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Onur Dogan: Department of Industrial Engineering, Izmir Bakirçay University, Izmir 35665, Turkey
Omer Faruk Seymen: Department of Information Systems Engineering, Sakarya University, Sakarya 54050, Turkey
Abdulkadir Hiziroglu: Department of Management Information Systems, Izmir Bakirçay University, Izmir 35665, Turkey

International Journal of Information Technology & Decision Making (IJITDM), 2022, vol. 21, issue 02, 707-727

Abstract: The 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.

Keywords: Customer segmentation; fuzzy clustering; intuitionistic fuzzy c-means; statistical methods; marketing perspective; parameter effects (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0219622021500607

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