A Multi-Dimensional Customer Segmentation Model Using The Fuzzy C-Means Clustering Algorithm: A Pilot Study In The B2B Setting

Customer segmentation allows companies to create mutual profiles of their customers. Determining industrial customer segments based on a single perspective causes various customer features to be disregarded. This study aims to develop a holistic segmentation approach in a B2B setting. The paper proposes a multi-dimensional segmentation model with four main criteria: customer purchasing performance, customer cooperation, customer workload, and customer potential. The case study demonstrates the real-life application of the proposed model using 379 customer data and 17 sub-criteria under four dimensions. The Fuzzy C-Means Clustering Algorithm creates the customer segments, and the Fuzzy Analytical Hierarchical Process is used to calculate criteria weights. The marketing strategies of each segment are used to guide customer relations and managerial decisions. This paper suggests that companies segment their customers by considering financial performance, cooperation level, future potential throughput, and challenges. It provides a practical and holistic insight into industrial customer segmentation.