A two-stage clustering approach for multi-region segmentation

Authors:
Jiahui Mo;Melody Y. Kiang;Peng Zou;Yijun Li
Affiliations:
School of Management, The University of Texas at Dallas, 800 W Campbell Road, SM 33, Richardson, TX 75080, United States;Information Systems Department, College of Business Administration, California State University, Long Beach, 1250 Bellflower Blvd., Long Beach, CA 90840, United States and Management Science and E ...;Business Administration Department, School of Management, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin, Heilongjiang 150001, China;Management Science and Engineering Department, School of Management, Harbin Institute of Technology, 92 Xidazhi Street, Harbin, Heilongjiang 150001, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Previous research in multi-region segmentation has found that the customer segmentation derived based on the customer attributes from one region (i.e., city or country) cannot be directly adopted by another region. As a result, for a firm that operates in multiple regions, a market segmentation method that can integrate data from different regions to obtain a set of generalized segmentation rules can greatly enhance the competitiveness of the company. In this research, we applied self-organizing map (SOM) network, an unsupervised neural networks technique as both a dimension reduction and a clustering tool to market segmentation. A two-stage clustering approach, which first groups similar regions together then finds customer segmentation for each region-group, is proposed. Empirical data from one of the largest credit card issuing banks in China was collected. The data, that includes surveys of customer satisfaction attributes and credit card transaction history, is used to validate the proposed model. The results show that the two-stage clustering approach based on SOM for multi-region segmentation is an effective and efficient method compared to other approaches.