在线零售企业CRM中的聚类分析研究
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摘要
以成本低廉、快捷、不受时空限制等优点而逐步全球流行的新型商业模式——电子商务,在最近几年中得到了迅速的发展,尤其以B2C为代表的在线零售业更是发展迅速。由于在线零售业进入的壁垒较低,加之电子商务技术的不断发展,使得在线零售业成为一个竞争激烈的领域。
将客户作为企业最重要的资源,以客户为中心的先进管理理念和管理技术客户关系管理(CRM)成为在线零售企业在激烈的竞争环境下,提高竞争力的有效方法和工具。
CRM的第一步是了解企业的客户,对于在线零售企业来说,企业与客户接触活动基本都在网上进行。通过网上接触,在线零售企业的Web站点能够收集到大量的客户数据,即包括常规的交易数据,也包括客户访问网站时的访问行为数据。相对于传统零售业模式的客户信息缺乏相比,在线零售企业拥有海量的客户数据,并且这些数据正以指数级增长。
聚类分析作为数据挖掘中的一项重要技术,具有高效的信息处理能力和分析能力,将其应用于在线零售商CRM中,对客户数据进行聚类分析,可以发现客户行为方式,掌握客户购买模式,进行客户分群等客户分析工作,是企业深入了解客户的重要渠道之一。
基于此,本文对在线零售企业CRM中的聚类分析问题进行了系统的研究。主要从以下几方面进行了分析研究:
(1) 从电子商务的定义、特点、类型、业务模式、信息应用环境、体系结构等几方面系统分析了电子商务商业模式。
(2) 在CRM理念指导下,坚持“以客户为中心”的主旨思想,从客户的分类、客户资源的发展阶段、客户购买过程、客户相关数据等几方面对在线零售企业的客户进行了系统深入的分析。
(3) 在对在线零售企业客户分析的基础上,对在线零售企业CRM的实施阶段进行了划分,总结了每阶段的任务重点,并对在线零售企业CRM中的数据挖掘进行了总结分析。
(4) 本着实用、有效的原则,突破传统客户关系管理中数据挖掘研究重
E-commerce, progressively global and popular new-type commercial mode, with the advantages of cheap with the cost, swift, no space-time limited etc. has get fast development during recent years, especially the retail online business represented by B2C developed rapidly. Because of the lower barrier that the online retail business enters and the constant development of e-commerce technology, the retail online business becomes a field with keen competition.The customer' s relation and management (CRM), which is an advanced management idea and skill, regarding customer as the center and the most important resource of enterprise, become an effective method and tool of improving competitiveness for the online retailer under the environment of keen competition.The first step of CRM is to understand customers, to the retail online industry, enterprises and customers contact on the internet basically. Through keeping in touch on the internet, the website of the online retailer can collect a large number of customers' data, namely including the routine trade data, also including the visit behavior data when the customers visit websites. Comparing to the scarce customer information of the traditional mode of retail business, the retail online business has magnanimity customer's data and these data is now growing with the index grade.Customer's cluster, as the important content in CRM, can help the online retailer to utilize effectively the large number of accumulated customers' data. it is the important channel for enterprises to understand the customer deeply through finding customer characteristic, grasping customer behavior law, carrying on groups customer' s analytical work.
Based on this, the thesis has made a systematic research on the clustering analysis problem in CRM of the online retailer, mainly from the following aspects:(1) Analysis the commercial modes of the E-commerce systematically from the definition, characteristic, type, business mode, information utilized environment and system structure .etc.(2)Under the guidance of CRM notion, the thesis, insisting on the main idea of "centering on customers" , has analyzed the customers of online retailer deeply from the several aspects of the customer' s classification, developing stages of customer resources, customer purchase processes , relevant data of customer and so on of the online retailer.(3) On the basis of the analysis on retailer' s customer, the thesis has classified the implementation stages of CRM, summarized key tasks of every stage, and analyzed data mining in CRM of the online retailer.(4)The thesis has made a systematic research on the clustering analysis of CRM in online retailer from aspects of clustering analysis principle, clustering problems, clustering algorithm, difficulties clustering faces and solutions to them and so on, which, being subject to the principle of practicality and efficiency, starts from the business analysis and surpasses the traditional research idea of paying attention to the algorithm of data mining of CRM. The thesis has collected the typical clustering questions in CRM of online retailer and analyzed several typical clustering algorithms in CRM of online retailer.
将客户作为企业最重要的资源,以客户为中心的先进管理理念和管理技术客户关系管理(CRM)成为在线零售企业在激烈的竞争环境下,提高竞争力的有效方法和工具。
CRM的第一步是了解企业的客户,对于在线零售企业来说,企业与客户接触活动基本都在网上进行。通过网上接触,在线零售企业的Web站点能够收集到大量的客户数据,即包括常规的交易数据,也包括客户访问网站时的访问行为数据。相对于传统零售业模式的客户信息缺乏相比,在线零售企业拥有海量的客户数据,并且这些数据正以指数级增长。
聚类分析作为数据挖掘中的一项重要技术,具有高效的信息处理能力和分析能力,将其应用于在线零售商CRM中,对客户数据进行聚类分析,可以发现客户行为方式,掌握客户购买模式,进行客户分群等客户分析工作,是企业深入了解客户的重要渠道之一。
基于此,本文对在线零售企业CRM中的聚类分析问题进行了系统的研究。主要从以下几方面进行了分析研究:
(1) 从电子商务的定义、特点、类型、业务模式、信息应用环境、体系结构等几方面系统分析了电子商务商业模式。
(2) 在CRM理念指导下,坚持“以客户为中心”的主旨思想,从客户的分类、客户资源的发展阶段、客户购买过程、客户相关数据等几方面对在线零售企业的客户进行了系统深入的分析。
(3) 在对在线零售企业客户分析的基础上,对在线零售企业CRM的实施阶段进行了划分,总结了每阶段的任务重点,并对在线零售企业CRM中的数据挖掘进行了总结分析。
(4) 本着实用、有效的原则,突破传统客户关系管理中数据挖掘研究重
E-commerce, progressively global and popular new-type commercial mode, with the advantages of cheap with the cost, swift, no space-time limited etc. has get fast development during recent years, especially the retail online business represented by B2C developed rapidly. Because of the lower barrier that the online retail business enters and the constant development of e-commerce technology, the retail online business becomes a field with keen competition.The customer' s relation and management (CRM), which is an advanced management idea and skill, regarding customer as the center and the most important resource of enterprise, become an effective method and tool of improving competitiveness for the online retailer under the environment of keen competition.The first step of CRM is to understand customers, to the retail online industry, enterprises and customers contact on the internet basically. Through keeping in touch on the internet, the website of the online retailer can collect a large number of customers' data, namely including the routine trade data, also including the visit behavior data when the customers visit websites. Comparing to the scarce customer information of the traditional mode of retail business, the retail online business has magnanimity customer's data and these data is now growing with the index grade.Customer's cluster, as the important content in CRM, can help the online retailer to utilize effectively the large number of accumulated customers' data. it is the important channel for enterprises to understand the customer deeply through finding customer characteristic, grasping customer behavior law, carrying on groups customer' s analytical work.
Based on this, the thesis has made a systematic research on the clustering analysis problem in CRM of the online retailer, mainly from the following aspects:(1) Analysis the commercial modes of the E-commerce systematically from the definition, characteristic, type, business mode, information utilized environment and system structure .etc.(2)Under the guidance of CRM notion, the thesis, insisting on the main idea of "centering on customers" , has analyzed the customers of online retailer deeply from the several aspects of the customer' s classification, developing stages of customer resources, customer purchase processes , relevant data of customer and so on of the online retailer.(3) On the basis of the analysis on retailer' s customer, the thesis has classified the implementation stages of CRM, summarized key tasks of every stage, and analyzed data mining in CRM of the online retailer.(4)The thesis has made a systematic research on the clustering analysis of CRM in online retailer from aspects of clustering analysis principle, clustering problems, clustering algorithm, difficulties clustering faces and solutions to them and so on, which, being subject to the principle of practicality and efficiency, starts from the business analysis and surpasses the traditional research idea of paying attention to the algorithm of data mining of CRM. The thesis has collected the typical clustering questions in CRM of online retailer and analyzed several typical clustering algorithms in CRM of online retailer.
引文
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[13] 赖茂生,傅湘玲.电子商务与竞争战略.北京:机械工业出版社,2004
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[28] 水俊峰.面向智能Web站点的数据挖掘技术研究及应用.武汉理工大学硕士学位论文.2003-2
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[31] 谢中.基于Web数据挖掘商务网站推荐系统的研究.西南师范大学硕士学位论文.2002
[32] 戈华.客户关系管理中聚类分析研究.哈尔滨工程大学硕士学位论文.2003-2
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[37] Fuzzy Clustering for Customer Segmentation in the Financial Services Industry.
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[2] 邵兵家,于同奎.客户关系管理——理论与实践.北京:清华大学出版社,2004
[3] 陈京民.数据仓库与数据挖掘技术.北京:电子工业出版社,2003
[4] Mark Sweiger, Mark R. Madsen, Jimmy Langston, Howard Lombard[著].陆昌辉等[译].点击流数据仓库.北京:电子工业出版社
[5] Corinne Baragoin, Christian M. Andersen, Stephan Bayerl, Graham Bent, Jieun Lee, Christoph Schommer. Mining Your Own Business in Telecom Using DB2 Intelligent Miner for Data. IBM Redbooks
[6] 吕延杰.电子商务教程.北京:电子工业出版社,2001.1
[7] Kenneth C. Laudon, Carol Guercio Traver[著].劳帼龄[译].电子商务——商业、技术和社会.北京:高等教育出版社
[8] Shapiro, Carl, and Hal R. Varian. Information Rules. A Strategic Guide to the Network Economy. Boston MA: Harvard Business School Press, 1999
[9] Tversky, A., and D. Kahneman. The Framing of Decisions and the Psychology of Choice. Science, January 1981
[10] Evans, Philip, and Thomas S. Wurster. Strategy and the New Economics of Information. Harvard Business Review, September-October 1997
[11] Sinha, Indajit. Cost Transparency: The Net's Threat to Price and Brands. Harvard Business Review, March-April 2000
[12] Shapiro, Carl, and Hal R. Varian. Information Rules. A Strategic Guide to the Network Economy. Boston MA: Harvard Business School Press, 1999
[13] 赖茂生,傅湘玲.电子商务与竞争战略.北京:机械工业出版社,2004
[14] Gordon S. Linofff, Michael J. A.Berry[著].沈钧毅,宋擒豹,燕彩蓉等[译].Web数据挖掘:将客户数据转化为客户价值.北京:电子工业出版社,2004
[15] Resnick and Varian. Recommender systems [J]. Communications of the ACM, 40 (3): 562C58, 1997.
[16] 冀俊忠.贝叶斯网及其在网上智能中的应用研究.北京工业大学工学博士学位论文.2004-04
[17] I. Zuckerman, D. Albrecht, and A. Nicholson. Predicting User's Requests on the WWW. In Proceedings of the Seventh International Conference on User Modeling. Springer Wien. 1999, 20: 275-284
[18] M. Pazzani, D. Billsus. Learning and Revising User Profiles: the Identification of Interesting Web Sites. Machine Learning, Kluwer Academic Publishers, 1997, 27: 313-331
[19] 王实,高文,李锦涛,黄铁军.基于隐马尔可夫模型的兴趣迁移模式发现.计算机学报,2001,24(2):152-157
[20] Maltz, D., and Ehrilich, K. Pointing the Way: Active Collaborative Filtering. Proceedings of the 1995 ACM Conference on Human Factors in Computing Systems. New York, 1995.
[21] Resnick, P., lacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. Grouplens: An Open Architecture for Collaborative Filtering of Netnews. In Proceedings of ACM CSCW94 Conference on Couputer-Supported Cooperative Work, 1994
[22] Dahlen, B. J., Konstan, J. A., Herlocker, J. L., Good, N., Borchers, A., and Riedl, J. Jump-starting Movielens: User Benefits of Starting a Collaborative Filtering System with 'Dead Data'. University of Minnesota TR 98-017, 1998.
[23] 张尧庭,方开泰.多元统计分析引论.北京:科学出版社,2003
[24] Clifford A. Shaffer.张铭,刘晓丹译.数据结构与算法分析.电子工业出版社,1998
[25] 高新波.模糊聚类分析及其应用.西安电子科技大学出版社,2004
[26] Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques. Simon Fraser University, 2002
[27] Jiawei Han and Micheline Kamber.范明,孟小蜂等译.数据挖掘概念与技术.北京:机械工业出版社,2002
[28] 水俊峰.面向智能Web站点的数据挖掘技术研究及应用.武汉理工大学硕士学位论文.2003-2
[29] 周露.基于数据挖掘技术的电子化客户关系管理(eCRM)研究.武汉大学硕士学位论文.2004-5
[30] Alex Berson, Stephen Smith, Kurt Thearling.构建面向CRM的数据挖掘应用.人民邮电出版社,2001
[31] 谢中.基于Web数据挖掘商务网站推荐系统的研究.西南师范大学硕士学位论文.2002
[32] 戈华.客户关系管理中聚类分析研究.哈尔滨工程大学硕士学位论文.2003-2
[33] 潘维民.管理从点击开始——电子商务点击流分析.中国计算机报.2000.10,(79)
[34] TSUEN HO HSU. An Application of Fuzzy Clustering in Group Positioning Analysis [J]. Proc. Natl. Sci, Counc. ROC(C), 2000(10): 157-167P
[35] M. Michalopoulos, G. D. Dounias, N. Thomidis, G. Tselentis. Decision Making Using Fuzzy C-means and Inductive Machine Learning for Managing Bank Branches Performance.
[36] Steven Russell, Weldon Lordwick. Fuzzy Clustering in Data Mining for Telco Database Marketing Campaigns. New York: Proceedings of NAFIPS 99. 1999(6): 720-726P
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