Customer Segmentation Analysis Based On K-means Algorithm

Customer resource is the most important assets of enterprise, whilecustomer relationship managements is the competition focus. Customersegmentation from a different perspective can help enterprises to adopttargeted measures, allocate resource rationally, understand customerrequirements better, improve the number of customers, therebysimultaneously provide a better service to customers, then get more return.Customer segmentation is the practice of dividing a customer base intogroups of individuals that are similar in specific ways relevant tomarketing, such as customer value、needs、 preferences and so on.Customer segmentation is the inevitable outcome of economicdevelopment and market competition.Using RFM model can identify customer valueeffectively. Based onthe theories of customer segmentation，we use of RFM model to analyzea cosmetics company’s customer consumption data to identify theenterprise customer value, and the company’s customer segments.However, the traditional RFM model is defective in customersegmentation, gap between categories is not big enough. To solve thisproblem, we use two clustering methods to cluster RFM model’sindicators, which k-means clustering method has a higher computing speed than systematic clustering method, and the effect of customersegmentation also significantly better than other clustering methods...