统计数据挖掘在CRM中的应用研究
|
摘要
企业离不开市场,激烈的市场竞争、全球化的经济浪潮导致企业必须以客户为中心,掌握更多更准确的客户信息,对客户个性化需要作出快速反应,为客户提供更便捷的购买渠道和更好的售后服务与经常性的客户关怀,这就是客户关系管理(Customer Relationship Management)即CRM的核心内容。随着网络和信息技术的发展,企业可获得的信息量日益膨胀,呈几何倍增长,CRM面临着信息爆炸所带来的严峻挑战。
如何从庞杂无序的数据中获取真正对企业有用的信息,发现数据背后隐藏的模式,从而指导企业的生产经营实践,这就是数据挖掘的任务所在。CRM的实施需要强大的技术支撑,数据挖掘以其从纷繁、复杂的数据中提取有用知识的强大功能而在CRM中有着广泛的应用。在CRM中,数据挖掘要发现的是商业活动中各个因素与客户行为之间的不直观的关系。因此,数据挖掘要想在商业活动中真正起到作用就必须与基本的商业过程关联起来。数据挖掘技术在CRM中的应用主要体现在新客户开发、客户效益分类、交叉销售及预测、顾客维系(流失分析)、顾客细分、客户赢利性识别等方面。
本文将统计学的思想、方法和技术贯穿于数据挖掘整个过程之中,分别从理论和实证两个方面研究和验证了统计数据挖掘技术在CRM中的应用。文章回顾了前人对CRM和数据挖掘的研究成果,从CRM和数据挖掘的演变过程、发展现状来论证CRM的进一步发展离不开数据挖掘作为技术支撑。本文的理论研究部分定义了CRM和统计数据挖掘的内涵,进一步论证了统计数据挖掘技术在CRM中的适用性并提出了基于统计学的数据挖掘流程以数据挖掘模型的建立和实现方法。文章的实证分析部分分为两章,分别为数据挖掘在CRM中应用的两个方面即客户细分和交叉销售及预测。两章均以电信CRM为例,运用统计数据挖掘技术建立了电信客户细分模型和电信增值业务的交叉销售和预测模型,并结合企业的实际情况对模型结果进行了分析和解释。研究结果可以为电信企业制定科学有效的营销策略提供依据,从而提高电信企业的CRM水平,提升企业的竞争能力。文章对企业如何在CRM中应用统计数据挖掘技术具有一定的指导意义。
Enterprise is inseparable from the market. fierce competition in the market and the tide of economic globalization lead to customer-centric enterprises, which mastering more accurate customer information, givingrapid response to personalized customer needs, providing customers with more convenient channels through which to buy, bettering after-sale services and regular care is indispensable. That is the core of customer relationship management content. With the growing volume of information, customer relationship management (CRM) is facing the challenges brought about by the "information explosion".
Customer relationship management implementation requires a strong technical support, data mining from its intricate and complex data extraction capabilities of useful knowledge which in CRM have broad application. To really work in commerce activities, data mining should be linked to commerce process. And the knowledge it try to find out is not obvious. The main aspects of data mining used in CRM are searching of new customer, cross-selling, customer maintaining, customer classificationand the profits ability recognizing.
Letting the statistical methods and techniques work throughout the entire data mining is the main considering of the thesis. There are both theories and demonstrations which sustain the application of statistics based data mining. Reviewing the achievement made in CRM and data mining, the thesis try to reason that the further development of CRM depend on data mining by studying the evolvement and actuality of CRM This paper puts forward the connotation of CRM and ulteriorly demonstrates that statistical based data mining is the same with CRM. And then it sum up the flow of data mining as well as the way in which models be made. There are 2 chapters dealing with the demonstration of customer classification and cross-selling. Taking customer relationship management in telecommunication industry for example, this thesis drew this conclusion through the right customer information mining, the establishment of a customer segmentation model, the analysis of value-added telecom services and cross-selling forecast models, those enterprises can get helpful findings and Suggestions which is operable in the development of an effective, scientific based marketing strategy. The thesis gives a good support to the implementation of CRM.
如何从庞杂无序的数据中获取真正对企业有用的信息,发现数据背后隐藏的模式,从而指导企业的生产经营实践,这就是数据挖掘的任务所在。CRM的实施需要强大的技术支撑,数据挖掘以其从纷繁、复杂的数据中提取有用知识的强大功能而在CRM中有着广泛的应用。在CRM中,数据挖掘要发现的是商业活动中各个因素与客户行为之间的不直观的关系。因此,数据挖掘要想在商业活动中真正起到作用就必须与基本的商业过程关联起来。数据挖掘技术在CRM中的应用主要体现在新客户开发、客户效益分类、交叉销售及预测、顾客维系(流失分析)、顾客细分、客户赢利性识别等方面。
本文将统计学的思想、方法和技术贯穿于数据挖掘整个过程之中,分别从理论和实证两个方面研究和验证了统计数据挖掘技术在CRM中的应用。文章回顾了前人对CRM和数据挖掘的研究成果,从CRM和数据挖掘的演变过程、发展现状来论证CRM的进一步发展离不开数据挖掘作为技术支撑。本文的理论研究部分定义了CRM和统计数据挖掘的内涵,进一步论证了统计数据挖掘技术在CRM中的适用性并提出了基于统计学的数据挖掘流程以数据挖掘模型的建立和实现方法。文章的实证分析部分分为两章,分别为数据挖掘在CRM中应用的两个方面即客户细分和交叉销售及预测。两章均以电信CRM为例,运用统计数据挖掘技术建立了电信客户细分模型和电信增值业务的交叉销售和预测模型,并结合企业的实际情况对模型结果进行了分析和解释。研究结果可以为电信企业制定科学有效的营销策略提供依据,从而提高电信企业的CRM水平,提升企业的竞争能力。文章对企业如何在CRM中应用统计数据挖掘技术具有一定的指导意义。
Enterprise is inseparable from the market. fierce competition in the market and the tide of economic globalization lead to customer-centric enterprises, which mastering more accurate customer information, givingrapid response to personalized customer needs, providing customers with more convenient channels through which to buy, bettering after-sale services and regular care is indispensable. That is the core of customer relationship management content. With the growing volume of information, customer relationship management (CRM) is facing the challenges brought about by the "information explosion".
Customer relationship management implementation requires a strong technical support, data mining from its intricate and complex data extraction capabilities of useful knowledge which in CRM have broad application. To really work in commerce activities, data mining should be linked to commerce process. And the knowledge it try to find out is not obvious. The main aspects of data mining used in CRM are searching of new customer, cross-selling, customer maintaining, customer classificationand the profits ability recognizing.
Letting the statistical methods and techniques work throughout the entire data mining is the main considering of the thesis. There are both theories and demonstrations which sustain the application of statistics based data mining. Reviewing the achievement made in CRM and data mining, the thesis try to reason that the further development of CRM depend on data mining by studying the evolvement and actuality of CRM This paper puts forward the connotation of CRM and ulteriorly demonstrates that statistical based data mining is the same with CRM. And then it sum up the flow of data mining as well as the way in which models be made. There are 2 chapters dealing with the demonstration of customer classification and cross-selling. Taking customer relationship management in telecommunication industry for example, this thesis drew this conclusion through the right customer information mining, the establishment of a customer segmentation model, the analysis of value-added telecom services and cross-selling forecast models, those enterprises can get helpful findings and Suggestions which is operable in the development of an effective, scientific based marketing strategy. The thesis gives a good support to the implementation of CRM.
引文
[1]张尧庭,谢邦昌,朱世武.数据挖掘入门及应用--从统计技术看数据挖掘[J/OL].http://211.154.163.43:90/～kjqk/jsjyyyrj/jsjy2004/0404pdf/0404.
[2]韩家伟.数据挖掘:概念与技术[M].北京:机械工业出版社,2001
[3]刘同明.数据挖掘技术及其应用[M].北京:国防工业出版社,2001
[4]武兴兵.客户关系管理--企业利润发动机[M].北京:中国民航民航出版社,2002
[5]刘伟.现代企业的客户关系管理[J].中山大学学报论丛.2001(2):(7-9)
[6]中国人民大学统计学系课题组.统计学与数据挖掘[J].统计与信息论坛.2002(1):4-9
[7]林晓源.浅谈客户关系管理CRM[J].中山大学学报论丛.2001(4):5-6
[8]常新功,贾伟.数据仓库技术在客户关系管理中的应用[J].山西财经大学学报.2001(6):15-20
[9]陈久梅.模糊聚类分析法在专有技术评估中的应用[J].商业研究.2005(3):6-8
[10]吴为英.基于粗集理论的商业数据挖掘[D].山东:山东科技大学,2003
[11]吕廷杰.客户关系管理与主题分析[M].北京:人民邮电出版社,2002
[12]杨永恒.客户关系管理--价值导向及使能技术[M].大连:东北财经大学出版社,2002
[13]菲利普.科特勒.营销管理:分析、计划、执行和控制[M].上海:上海人民出版社,1997
[14]曾海颖.客户关系管理中的数据挖掘[D].南京:南京航空航天大学,2003
[15]Smith,Wendell.R.Product differentiation and market segmentation as alternative product strategies[J].Journal of Marketing.1956(7):3-8
[16]Soper,Suzanne.the evolution of segmentation methods in services:where next[J].Journal of Financial Services Marketing.2002(8):68-69
[17]Anand,T.and Kahn,G.Opportunity Explorer.Navigating Large Databases Using Knowledge Discovery Templates:Workshop Knowledge Discovery in Database,1993
[18]焦瑾.基于数据挖掘的商业银行个人客户细分系统分析与设计[D].成都:西南财经大学,2003
[19]Lazer,William.Life style concept and marketing toward scientific marketing[M].Stephen Greyser.ed.Chicago:American Marketing,1963
[20]Peppard,Joe.Customer Relationship Management(CRM)in Financial Services[J].European ManagementJournal,2000(11):10-15
[21]数据挖掘研究组.数据挖掘技术概述[J/OL].http://www.dwway.com/document.php?id=144&type=6
[22]Timothy.J.Ross.模糊逻辑及其工程应用[M].北京:电子工业出版社,2001
[23]数据挖掘应用现状与产品分析[J/OL].http://www.dwway.com/document.php?id=335&type=6
[24]宋俊德.我国CTI、呼叫中心和CRM技术的发展和应用[J].2001(10):12-15
[25]吴峰,施鹏飞.概念聚类挖掘方法的客户交易行为分析[J].微型电脑应用.2000(16):28-32
[26]王己峰,张典元.汉化SPSS/PC+在世界银行贷款项目中的应用[J].统计与信息论坛.现代统计分析案例荟萃专辑.1996(6):11-15
[27]郭志刚.基于统计的数据挖掘技术在CRM中的应用研究[M].北京:北京人民大学出版,1999
[28]刘玉梅.基于数据挖掘技术的电信CRM系统的研究与实施[D].哈尔滨:哈尔滨工程大学,2003
[29]石磊.探索式数据挖掘模型的讨论[J].河南科学.2000(18):18-21
[30]赵宏波,孟雅羚.数据挖掘在电信客户关系管理中的应用[J].电信技术.2001(12):12-14
[31]Data Mining Researchers Search for New Tools[J].R&D Magazine.2002(7):7-12
[32]James H,Drew D.R,Mani Andrew L,Betz Piew Datta.Targeting Customers with Statistical and Data Mining Techniques[J].Journal of Service Research,2001(2):3
[33]道焰,代玉龙.基于数据挖掘的银行信用卡客户关系管理系统[J].科技论坛.2005(3):27
[34]高惠旋.SAS系统与市场调查数据分析[M].北京:北京大学内部教材,1999
[35]张文彤.世界优秀统计工具SPSS 11.0统计分析教程[M].北京:北京希望电子出版社,2002
[36]李斌,郭剑毅.聚类分析在客户关系管理中的应用与研究[J].计算机工程与设计.2005(2)540-542
[37]The basics SPSS for Windows 10.0 SPSS Inc.Chicago:Illinois,1999
[38]Advanced Statistical Analysis Using SPSS(10.0 Revised)SPSS Inc.Chicago:Illinois,2000
[39]R.Srikant and R.Agrawal.Mining sequential patterns Generalizations and performance improvements.In Proc.5th[C].Extending Database Technology.1996:13
[40]M.Garofalakis,R.Rastogi,and K.ShimSpirit.Sequential pattern mining with regular expression constraints[C]:VLDB,1999
[41]J.Pei et al.mining frequent itemsets with constraints[C].ICDE.2001
[42]J.Pei,J.Han,B.Mortazavi-Asl,H.Pinto,Q.Chen,U.Dayal and M-C.Hsu.PrefixSpan:Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth[C]Conf.Data Engineering.2003:215-224
[43]J.Pei and J.Han.Can we push more constraints into frequent pattern mining[C].KDD.2003
[44]Pei J,Han J,Wang WMining Sequential Patterns with Constraints in Large Databases[C].ACM CIKM Conference.2002
[45]Helen Pint.Multi-dimensional Sequential Pattern Mining[D].SIMON FRASER UNIVERSITY.2002
[46]Pei et al,PrefixSpan.Mining sequential patterns efficiently by prefix-projected pattern growth [J].ICDE.2001
[47]S.Ramaswamy,S.Mahajan.and A.Silberschatz.On the discovery of interesting patterns in assiociation rules[C].Conf.Very Large Data Bases.1998
[48]Alex Berson,Steephen Smith,Kurt Thearling.CRM与数据挖掘技术[M].北京:人民邮电出版社.2003
[49]R.Srikant and R.Agrawal.Mining sequential patterns.Generalizations and performance improvements[J].In 5th Intl.Conf.Extending Database Technology.1996(6):18
[50]H.Pinto,J.Han,J.Pei,K.Wang,Q.Chen,and U.Dayal.Multi-dimensional Sequential Pattern Mining[C].Conf.Information and Knowledge Management.2001(3):15
[51]薛薇.数据挖掘的客户关系管理应用研究[D].北京:中国人民大学统计数据库研究室,2002
[52]CRM研究小组.CRM概念[EB/OL].http://www.crmchina.com.cn.2001
[53]徐振明,顾明.客户资源管理(CRM)系统理论模型的设计与实现[J].计算机工程与应用.2002(11):11-14
[54]楼巧萍.基于J2EE技术架构的分析型CRM系统[D].杭州:浙江工业大学,2003
[2]韩家伟.数据挖掘:概念与技术[M].北京:机械工业出版社,2001
[3]刘同明.数据挖掘技术及其应用[M].北京:国防工业出版社,2001
[4]武兴兵.客户关系管理--企业利润发动机[M].北京:中国民航民航出版社,2002
[5]刘伟.现代企业的客户关系管理[J].中山大学学报论丛.2001(2):(7-9)
[6]中国人民大学统计学系课题组.统计学与数据挖掘[J].统计与信息论坛.2002(1):4-9
[7]林晓源.浅谈客户关系管理CRM[J].中山大学学报论丛.2001(4):5-6
[8]常新功,贾伟.数据仓库技术在客户关系管理中的应用[J].山西财经大学学报.2001(6):15-20
[9]陈久梅.模糊聚类分析法在专有技术评估中的应用[J].商业研究.2005(3):6-8
[10]吴为英.基于粗集理论的商业数据挖掘[D].山东:山东科技大学,2003
[11]吕廷杰.客户关系管理与主题分析[M].北京:人民邮电出版社,2002
[12]杨永恒.客户关系管理--价值导向及使能技术[M].大连:东北财经大学出版社,2002
[13]菲利普.科特勒.营销管理:分析、计划、执行和控制[M].上海:上海人民出版社,1997
[14]曾海颖.客户关系管理中的数据挖掘[D].南京:南京航空航天大学,2003
[15]Smith,Wendell.R.Product differentiation and market segmentation as alternative product strategies[J].Journal of Marketing.1956(7):3-8
[16]Soper,Suzanne.the evolution of segmentation methods in services:where next[J].Journal of Financial Services Marketing.2002(8):68-69
[17]Anand,T.and Kahn,G.Opportunity Explorer.Navigating Large Databases Using Knowledge Discovery Templates:Workshop Knowledge Discovery in Database,1993
[18]焦瑾.基于数据挖掘的商业银行个人客户细分系统分析与设计[D].成都:西南财经大学,2003
[19]Lazer,William.Life style concept and marketing toward scientific marketing[M].Stephen Greyser.ed.Chicago:American Marketing,1963
[20]Peppard,Joe.Customer Relationship Management(CRM)in Financial Services[J].European ManagementJournal,2000(11):10-15
[21]数据挖掘研究组.数据挖掘技术概述[J/OL].http://www.dwway.com/document.php?id=144&type=6
[22]Timothy.J.Ross.模糊逻辑及其工程应用[M].北京:电子工业出版社,2001
[23]数据挖掘应用现状与产品分析[J/OL].http://www.dwway.com/document.php?id=335&type=6
[24]宋俊德.我国CTI、呼叫中心和CRM技术的发展和应用[J].2001(10):12-15
[25]吴峰,施鹏飞.概念聚类挖掘方法的客户交易行为分析[J].微型电脑应用.2000(16):28-32
[26]王己峰,张典元.汉化SPSS/PC+在世界银行贷款项目中的应用[J].统计与信息论坛.现代统计分析案例荟萃专辑.1996(6):11-15
[27]郭志刚.基于统计的数据挖掘技术在CRM中的应用研究[M].北京:北京人民大学出版,1999
[28]刘玉梅.基于数据挖掘技术的电信CRM系统的研究与实施[D].哈尔滨:哈尔滨工程大学,2003
[29]石磊.探索式数据挖掘模型的讨论[J].河南科学.2000(18):18-21
[30]赵宏波,孟雅羚.数据挖掘在电信客户关系管理中的应用[J].电信技术.2001(12):12-14
[31]Data Mining Researchers Search for New Tools[J].R&D Magazine.2002(7):7-12
[32]James H,Drew D.R,Mani Andrew L,Betz Piew Datta.Targeting Customers with Statistical and Data Mining Techniques[J].Journal of Service Research,2001(2):3
[33]道焰,代玉龙.基于数据挖掘的银行信用卡客户关系管理系统[J].科技论坛.2005(3):27
[34]高惠旋.SAS系统与市场调查数据分析[M].北京:北京大学内部教材,1999
[35]张文彤.世界优秀统计工具SPSS 11.0统计分析教程[M].北京:北京希望电子出版社,2002
[36]李斌,郭剑毅.聚类分析在客户关系管理中的应用与研究[J].计算机工程与设计.2005(2)540-542
[37]The basics SPSS for Windows 10.0 SPSS Inc.Chicago:Illinois,1999
[38]Advanced Statistical Analysis Using SPSS(10.0 Revised)SPSS Inc.Chicago:Illinois,2000
[39]R.Srikant and R.Agrawal.Mining sequential patterns Generalizations and performance improvements.In Proc.5th[C].Extending Database Technology.1996:13
[40]M.Garofalakis,R.Rastogi,and K.ShimSpirit.Sequential pattern mining with regular expression constraints[C]:VLDB,1999
[41]J.Pei et al.mining frequent itemsets with constraints[C].ICDE.2001
[42]J.Pei,J.Han,B.Mortazavi-Asl,H.Pinto,Q.Chen,U.Dayal and M-C.Hsu.PrefixSpan:Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth[C]Conf.Data Engineering.2003:215-224
[43]J.Pei and J.Han.Can we push more constraints into frequent pattern mining[C].KDD.2003
[44]Pei J,Han J,Wang WMining Sequential Patterns with Constraints in Large Databases[C].ACM CIKM Conference.2002
[45]Helen Pint.Multi-dimensional Sequential Pattern Mining[D].SIMON FRASER UNIVERSITY.2002
[46]Pei et al,PrefixSpan.Mining sequential patterns efficiently by prefix-projected pattern growth [J].ICDE.2001
[47]S.Ramaswamy,S.Mahajan.and A.Silberschatz.On the discovery of interesting patterns in assiociation rules[C].Conf.Very Large Data Bases.1998
[48]Alex Berson,Steephen Smith,Kurt Thearling.CRM与数据挖掘技术[M].北京:人民邮电出版社.2003
[49]R.Srikant and R.Agrawal.Mining sequential patterns.Generalizations and performance improvements[J].In 5th Intl.Conf.Extending Database Technology.1996(6):18
[50]H.Pinto,J.Han,J.Pei,K.Wang,Q.Chen,and U.Dayal.Multi-dimensional Sequential Pattern Mining[C].Conf.Information and Knowledge Management.2001(3):15
[51]薛薇.数据挖掘的客户关系管理应用研究[D].北京:中国人民大学统计数据库研究室,2002
[52]CRM研究小组.CRM概念[EB/OL].http://www.crmchina.com.cn.2001
[53]徐振明,顾明.客户资源管理(CRM)系统理论模型的设计与实现[J].计算机工程与应用.2002(11):11-14
[54]楼巧萍.基于J2EE技术架构的分析型CRM系统[D].杭州:浙江工业大学,2003