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Market segmentation is one of the most important areas of knowledge-based marketing. When it comes to personal financial services in retail banks, it is really a challenging task as data bases are large and multidimensional. The conventional ways in customer segmentation are knowledge based and often get bias results. On the contrary, data mining can deal with mass of data and never overlook any important phenomena. In this paper, we choose the clustering ensemble method to do customer segmentation due to labeled data sets are not available. Through the experiments and tests in the real personal financial business, we can make a conclusion that our models reflect the true characteristics of various types of customers and can be used to find the investment orientations of customers.