Objective Cluster Analysis in Value-Based Customer Segmentation Method

Clustering is popular used in customer value segmentation in business research. Compared with other clustering methods, the objective clustering analysis can automatically and objectively determine the number of clusters and find out the optimal clustering scheme. This investigation discussed the reasonable evaluation system of value-driven customer segmentation, identified customer behavior using a recency, frequency and monetary(RFM) index and customer basic properties as integrated variables, and then, presented a novel approach-objective clustering analysis that be used in value-based customer segmentation. The shortcoming of the extra criterion was appraised by its algorithm. The new criterion is followed and be used in segmentation. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a standard datasets of a book club to segment its customers. The experimental results demonstrate that the proposed method can more effectively target clustering groups than the former one.