A two-stage clustering approach for multi-region segmentation

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 (SUM) 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 SUM for multi-region segmentation is an effective and efficient method compared to other approaches. (C) 2010 Elsevier Ltd. All rights reserved.