Improvement Research of Customer Segmentation in Knowledge Intensive Business Services

Knowledge-intensive business services (KIBS) show the service specialized, knowledgeable, customized features, which determines its customers with a highly participatory and interactive. The reasonable classification of the customer will contribute to offering better solutions for the problem to meet customer´s demand. Combined with features of KIBS customer services, this paper points out that the market segmentation is no longer limited to variables of customer behavior characteristics, poses the afterwards market segmentation strategy based on attitude variables. Describes traditional K-means and SOFM cluster methods, proposes SVC algorithm to conduct market segmentation. Through application case of market segmentation the paper contrasted clustering effect of three methods, improving the ability to determine the effect of classification and advantages.