基于RFME模型和AdaBoost分类器的电子商务客户关系研究  

Research on E-commerce Customer Relationship Based on RFME Model and AdaBoost Classifier

作　　者：陈俊龙 吴丽丽[1] CHEN Junlong;WU Lili(College of Information Science and Technology,Gansu Agricultural University,Lanzhou Gansu 730070)

机构地区：[1]甘肃农业大学信息科学技术学院,甘肃兰州730070

出　　处：《软件》2021年第3期1-7,共7页Software

基　　金：民生科技专项(科技特派员专题),项目名称:定西地区农村电子商务营销综合能力提升(20CX9NA095)。

摘　　要：为进一步探究和分析电子商务客户关系,本文提出e价值的指标体系和计算方法,同时基于使用k-means方法对客户进行分类,实现对客户关系的深层发掘。基于改进的RFM模型实现了对客户的辨别与分类功能,对不同客户的e价值能进行有效预测,同时可以为企业在电商相关领域营销策略的差异化实施提供依据。对客户关系进行深层细分。同时基于Ada Boost分类器,提出以C5.0决策树作为基分类器的客户保持与流失预测模型,降低错误预测成本,精准识别高价值客户。In order to further explore and analyze the relationship between e-commerce customers,this article proposes an index system and calculation method for e-value,and at the same time classifies customers based on the use of k-means method to realize in-depth exploration of customer relationships.Based on the improved RFM model,the function of identifying and categorizing customers is realized,and the e-value of different customers can be effectively predicted.At the same time,it can provide a basis for the differentiated implementation of marketing strategies for companies in the e-commerce-related fields.In-depth segmentation of customer relationships.At the same time,based on the AdaBoost classifier,a customer retention and churn prediction model based on the C5.0 decision tree is proposed to reduce the cost of error prediction and accurately identify high-value customers.

关 键 词：RFM ADABOOST 电子商务 客户价值 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]