GPHC: A heuristic clustering method to customer segmentation

Abstract

Customer segmentation refers to dividing customer groups into multiple different sub-communities according to customer characteristics. The accurate segmentation of customers is critical for decision-makers to fully understand the customer requirements (CRs) in the market and then design market activities to satisfy customers. In past studies, clustering algorithms have been widely used to solve customer segmentation. However, it is still difficult to divide customers clearly when facing real customer requirement data (CRD). To solve these difficulties, this paper develops a heuristic clustering method for customer segmentation, termed Gaussian Peak Heuristic Clustering (GPHC, for short). Specifically, this paper utilizes the entropy method and standardized Gaussian distribution to filter and model interval CRD. Then, the customer preference pattern hidden in CRD could be recognized by niching genetic algorithm and hierarchical clustering. Finally, the clustering result of CRD will be obtained by the k-means algorithm based on heuristics information from customer preference patterns. Furthermore, customer segmentation can be extracted from the clustering result. A practical case is used to illustrate the effectiveness of GPHC in solving the customer segmentation problem. Experiments show that the customer segmentation result output by our method is consistent with the customer segmentation result given by experts. Besides, the robustness of GPHC in the face of complex customer segmentation scenarios has been verified through numerical experiments.