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计算机系统应用:2020,29(9):249-254
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基于灰色关联度和K-Means++的电子商务客户价值分类
(上海理工大学 管理学院, 上海 200093)
Classification of E-Commerce Customer Value Based on Grey Correlation Degree and K-Means++
(Business School, University of Shanghai for Science & Technology, Shanghai 200093, China)
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投稿时间:2020-01-07    修订日期:2020-02-13
中文摘要: 现有的模型大多采用RFM模型和K-means对客户价值进行分类,对指标权重的确定大多采用AHP法,没有考虑到RFM模型指标相互之间的联系.首先根据RFM模型选择平均购买时间间隔,客户在一定时间内的购买频率,平均每笔订单交易金额和客户的活跃时间构造RFMT模型来衡量客户价值.其次使用灰色关联度确定各指标权重.最后针对K-means的缺点,运用改进K-means (K-means++)和肘部法则对RFMT模型进行聚类分析.该模型能对客户群进行更加细致的划分,既能帮助电子商务企业识别出需要重点关注的客户即已流失客户和新客户群体,同时将该企业客户划分为价值由高到低的客户群,对不同客户群提出具体的营销建议.
Abstract:The combine model of the RFM model and K-means is used to classify customer value and AHP method is mostly used to determine the weight of indicators, without considering the relationship between the indicators of RFM model. In this study, firstly, we select the average time interval, the customer purchase frequency in a period of time, average transaction money of each order, and customer active time to structure RFMT model in order to measure the customer value. Then, determine the index weight by using grey correlation degree. Finally, aiming at the shortcomings of K-means, K-means ++ and elbow law are used to carry out cluster analysis of RFMT model. This model can make a more detailed division of customer base. It can help e-commerce enterprises to identify the customers that need to be focused on. Meanwhile, the enterprise customers can be divided into customer groups with high value to low value, and put forward specific marketing suggestions for different customer groups.
文章编号:7562     中图分类号:    文献标志码:
基金项目:国家自然科学基金(11701370)
引用文本:
冀慧杰,倪枫,刘姜,赵燚.基于灰色关联度和K-Means++的电子商务客户价值分类.计算机系统应用,2020,29(9):249-254
JI Hui-Jie,NI Feng,LIU Jiang,ZHAO Yi.Classification of E-Commerce Customer Value Based on Grey Correlation Degree and K-Means++.COMPUTER SYSTEMS APPLICATIONS,2020,29(9):249-254

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