基于RFA模型和聚类分析的百度外卖客户细分

计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 436-438.

基于RFA模型和聚类分析的百度外卖客户细分

包志强¹, 赵媛媛¹, 赵研¹, 胡啸天¹, 高帆²

西安邮电大学通信与信息工程学院西安710121¹
航天科工集团第四研究院第九总体部武汉430040²

出版日期:2019-02-26 发布日期:2019-02-26
作者简介:包志强(1978-),男,博士,副教授,主要研究方向为数据挖掘、大数据分析、导航抗干扰;赵媛媛(1996-),女,硕士生,主要研究方向为数据挖掘、大数据分析,E-mail:1732055344@qq.com。
基金资助:
本文受陕西省教育厅专项科研计划项目(17JK0703)资助。

Segmentation of Baidu Takeaway Customer Based on RFA Model and Cluster Analysis

BAO Zhi-qiang¹, ZHAO Yuan-yuan¹, ZHAO Yan¹, HU Xiao-tian¹, GAO Fan²

Department of Communication and Information Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China¹
The Ninth Research Institute of the General Department of the Fourth,Aerospace Science and Technology Group,Wuhan 430040,China²

Online:2019-02-26 Published:2019-02-26

摘要/Abstract

摘要： 针对百度外卖行业具有的客户数量大、消费数据多、维度多等特点,提出一种基于客户消费行为视角的改进RFM模型。采用层次分析算法确定模型中各个变量的权重,并在此基础上采用K-Means聚类算法进行客户细分,计算确定客户对于商家的个人价值。数据分析结果表明,基于改进RFM模型的客户细分方法可以使商家对不同价值的客户采取针对性的策略。

关键词: K-Means聚类, 百度外卖, 改进RFM模型, 客户细分

Abstract: In view of the characteristics of Baidu Take-out industry,such as large number of customers,large consumption data,high dimensions and so on,this paper proposed an improved RFM model based on perspective of customer consumption behavior,and uses the AHP algorithm to determine the weight of each variable in the model.K-Means clustering algorithm is used for customer segmentation,and the customer’s personal value for the business is computed and determined .The results of data analysis show that the customer segmentation method based on the improved RFM model canmake merchants adopt targeted strategies for customers with different values.

Key words: Baidu takeaway, Client subdivision, Improved RFM model, K-Means clustering

中图分类号:

F270

包志强, 赵媛媛, 赵研, 胡啸天, 高帆. 基于RFA模型和聚类分析的百度外卖客户细分[J]. 计算机科学, 2018, 45(11A): 436-438. https://doi.org/

BAO Zhi-qiang, ZHAO Yuan-yuan, ZHAO Yan, HU Xiao-tian, GAO Fan. Segmentation of Baidu Takeaway Customer Based on RFA Model and Cluster Analysis[J]. Computer Science, 2018, 45(11A): 436-438. https://doi.org/

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