基于数据挖掘技术的电子商务客户关系管理  

Research on Customer Relationship Management Representation Based on Data Mining

作　　者：李雪菁[1,2] LI Xuejing(College of Information Science and Technology,Sanda University,Shanghai 201209;School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200072)

机构地区：[1]上海杉达学院信息科学与技术学院,上海201209 [2]上海大学机电工程与自动化学院,上海200072

出　　处：《计算机与数字工程》2018年第9期1834-1838,共5页Computer & Digital Engineering

基　　金：国家自然科学基金重点项目(编号:61233010);上海市教育委员会民办高校(重点)科研项目(编号:2016-SHNGE-01ZD);上海杉达学院校基金(编号:2016ZZ14)资助

摘　　要：随着互联网技术的发展,电子商务也进入了海量数据时代,从这些海量数据里挖掘出有用的隐藏信息具有非常高的商业价值。客户细分、客户保持,客户满意度这三方面是具有重要现实意义的问题,也是行业的研究热点。近年来在国内,基于数据挖掘方法针对以上三个问题的研究工作开展较多,但大多数处于初级探讨,还有很多问题有待解决。论文对客户细分算法模型研究,基于聚类模型和决策树模型,首先采用聚类模型对用户进行分析,进而采用决策树对客户进行细分,并在此基础上采用K-means算法,结果表明算法效率及准确率均得到了提高。同时,论文探讨了神经网络中的BP算法对客户忠诚度的问题,实验结果表明,该方法能够快速有效地对分类客户进行分析。该方法可以针对易流失客户进一步提出针对性的对策,避免客户流失现象的持续发生。Recently,with the development of Internet technology,E-commerce has entered the era of big data. From the big data science,mining useful hidden information has very high commercial value. The problems of customer segmentation,customer retention and customer satisfaction have important practical significance. At the same time,the above problems are research hotspotissues of ecommerce industry. In recent years,the research work for the three problems based on data mining is more. But the most of research works are primary discussion and there are still many problems to be solved. For the problems of customer segmentation,customer retention and customer satisfaction,the paper researches on customer relationship data mining process. The author using clustering model based on clustering model and decision tree model to analyze users. And on this basis,the paper uses K-means algorithm. The results show that the algorithm efficiency and accuracy are improved. At the same time,this paper discusses the problem of customer loyalty in BP algorithm in neural network. The experimental results show that the method can classify customers quickly and effectively. The method for customer loyalty can be targeted for vulnerable customers further targeted measures to avoid customer churn phenomenon continues to occur.

关 键 词：大数据 数据挖掘 客户关系管理(CRM) K-MEANS算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]