基于改进RFM模型的电子商务客户细分

Customer classification of E-commerce based on improved RFM model

对电子商务企业的客户进行准确细分,采取相应的营销策略,是电子商务发展的重要环节。在传统零售行业客户细分的RFM模型上,引入总利润属性,创建RFP模型,使用数据挖掘K-Means算法对某电子商务企业客户进行聚类分析,与RFM模型比较,并分析了模型属性的关联性对聚类结果的影响,得出了模型比较的六个结论和四个营销策略,能为电子商务行业以及其他销售行业提出相关营销策略。

It is essential to classify E-commerce customers accurately and take suitable marketing strategy for the development of E-commerce. Based on the RFM （ Recency, Frequency, Monetary） model used in customer classification of traditional retail, the total profit attributes were introduced to set RFP model and K-Means clustering method was used to analyze customers of a firm in E-commerce and compared with the RFM model. The relevance of model attributes impact on clustering results was analyzed. Finally, the paper gets six conclusions about the comparison between the both models and puts forward four st＇rategies for E-Commerce and other sales industries.