摘要
随着经济全球化及大数据技术的蓬勃发展,跨国零售商之间的竞争日益激烈,根据客户特征进行客户细分,协助客户进行个性化的服务体验,有利于跨国零售商实现精准营销和高效的客户关系管理。为了提高客户细分的精度,本文提出一种基于RFM模型的K-means聚类算法,使用簇内误方差(SSE)和轮廓系数(Silhouette Coefficient)计算聚类个数,优化K值选取。本文选取一家跨国零售商的数据进行实证检验,对细分后的结果进行特征分析,将客户划分为核心型客户、维护型客户和风险型客户三种类别,并为不同客户群体提供差异化营销策略,仅供参考。
With the booming development of economic globalization and big data technology,competition among multinational retailers has intensified.The segmentation of customers based on their characteristics and the provision of personalized service experiences for customers are instrumental for multinational retailers to achieve precision marketing and efficient customer relationship management.To increase the accuracy of customer segmentation,this paper puts forward a K-means clustering algorithm based on the RFM model,and utilizes the sum of the squared errors(SSE)and silhouette coefficient to calculate the number of clusters and refine the choice of the K value.In this paper,the data of a multinational retailer are selected for empirical testing,and the segmented results are characterized to classify customers into three categories:core customers,maintained customers,and risky customers.The paper offers differentiated marketing strategies for different customer groups.
作者
崔雯
李剑锋
Cui Wen;Li Jianfeng(College of Economics and Management of China Jiliang University,Hangzhou 310018,Zhejiang)
出处
《中国商论》
2024年第9期37-40,共4页
China Journal of Commerce
