Doğan, OnurHızıroğlu, AbdülkadirSeymen, Ömer Faruk2022-02-152022-02-15202197830305115552194-5357https://doi.org/10.1007/978-3-030-51156-2_6https://hdl.handle.net/20.500.14034/199International Conference on Intelligent and Fuzzy Systems, INFUS 2020 -- 21 July 2020 through 23 July 2020 -- -- 242349Defining 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. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.eninfo:eu-repo/semantics/closedAccessCustomer segmentationFuzzy clusteringIntuitionistic fuzzy c-meansMarketing perspectiveStatistical methodsFuzzy clusteringFuzzy setsRetail storesSalesSoft computingCompetitive businessCustomer requirementsCustomer segmentationIntuitionistic Fuzzy C-MeansIntuitionistic fuzzy clusteringK-means segmentationsSegmentation analysisSoft computing methodsClustering algorithmsConference Object10.1007/978-3-030-51156-2_61197 AISC39462-s2.0-85088749341N/A