中文摘要：

针对零售业客户细分指标粗糙和方法精准性低的问题,提出一种基于数据挖掘聚类分析的零售业客户细分方法;方法构建了一套基于RFM的多指标客户细分指标体系,采用熵值法赋予指标权重,进而使用K-Means算法进行客户细分;实证研究结果表明：方法在客户行为特征区分能力和聚类紧凑性方面均优于传统基于RFM的细分方法,方法可行、有效,能够更好地解决零售业客户细分问题,提升客户关系管理和营销决策质量.

英文摘要：

Due to the problem in the roughness of customer segmentation indicator and low accuracy in retail industry,a customer segmentation method in retail industry is propoesed on the basis of clustering analysis of data mining,and a set of RFM based on multi-indicator customer segmentation index system is constructed by using entropy value method to give indicator weight and then by using K-Means algorithm to conduct customer segmentation. Empirical research results show that this method is better than the traditional RFM based on segmentation method in the perspective of distinguishing capacity for customer behaviors feature and clustering compactness,and this method,with feasibility and validity,can better solve the problem in customer segmentation in retail industry and improves the customer relation management and marketing decision-making quality.