Abstract
Defining customer requirements in a huge amount of data of the digital era is crucial for companies in a competitive business environment. Customer segmentation has been attracted to a great deal of attention and has widely been performed in marketing studies. However, boundary data which are close to more than one segment may be assigned incorrect classes, which affects to make the right decisions and evaluations. Therefore, segmentation analysis is still needed to develop efficient models using advanced techniques such as soft computing methods. In this study, an intuitionistic fuzzy clustering algorithm were applied to customer data in a supermarket according to the amount spent in some product groups. The data represent 33-month customer shopping data in a supermarket for eight product groups. The results indicate the intuitionistic fuzzy c-means based customer segmentation approach produces more reliable and applicable marketing campaigns than conditional fuzzy c-means and k-means segmentation method.
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Dogan, O., Hiziroglu, A., Seymen, O.F. (2021). Segmentation of Retail Consumers with Soft Clustering Approach. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-51156-2_6
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