An E-commerce Customer Segmentation Method based on RFM Weighted K-means

Abstract

Customer segmentation of the heterogeneity of electricity enterprise to meet customer demand and limited resource optimal allocation is of great significance, in view of the existing customer segmentation using a single method to calculate index weight, customer clustering index weight did not consider different dimensions empowerment, lead to the problem of low accuracy, customer segmentation results based on RFM weighted K-means a kind of electricity customer segmentation model, The weight of each index of e-commerce customer RFM is obtained by Analytic hierarchy process (AHP)-entropy weight method, Euclidean distance weighting is used, k-means clustering is used to divide customers, customer types are analyzed, comprehensive value of customers is sorted by combining the weight of indicators, and differentiated marketing strategies are provided for e-commerce enterprises according to customer segmentation results. The results show that the e-commerce customer segmentation obtained by this method has greater differences and better segmentation effect.

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