To understand customers' characteristics and their desire is critical for modern CRM (customer relationship management). The easiest way for a company to achieve this goal is to target their customers and then to serve them through providing a variety of personalized and satisfactory goods or service. In order to put the right products or services and allocate resources to specific targeted groups, many CRM researchers and/or practitioners attempt to provide a variety of ways for effective customer segmentation. Unfortunately, most existing approaches are vulnerable to outliers in practice and hence segmentation results may be dissatisfactory or seriously biased. In this study, a hybrid approach that incorporates kernel induced fuzzy clustering techniques is proposed to overcome the above-mentioned difficulties. Two real datasets, including the supervised WINE and the unsupervised RFM, are used to validate the proposed approach. Experimental results show that the proposed approach cannot only fulfill robust classification, but also achieve robust segmentation simultaneously.