大数据时代中小型零售企业客户细分实证研究

Research on the Customer Segmentation of Small and Medium-sized Retail Enterprises in the Era of Big Data

针对中小型零售企业很少利用大数据指导企业经营活动,提出一种改进的RFM模型与k-means相结合,并应用熵值法确定各指标权重,得出客户细分模型。采用某中小型零售企业的真实数据进行实证分析。实证研究结果说明,该客户细分模型在类内平均距离和泛化能力上都优于传统的RFM模型,方法可行、有效。在理论上为客户细分的研究提供了借鉴,在实践上为中小型零售企业客户细分提供了参考。

In view of the fact that small and medium-sized retail enterprises seldom use big data to guide their business activities,this paper proposes an improved RFM model combined with K-Means,and uses the entropy value method to determine the weight of each index,and obtains the customer segmentation model.Empirical analysis is made with the real data of a small and medium-sized retail enterprise.The empirical results show that the customer segmentation model is superior to the traditional RFM model in average distance and generalization ability,and the method is feasible and effective.In theory,it provides a reference for the research on customer segmentation,and in practice,it provides a reference for the customer segmentation of small and medium-sized retail enterprises.