回归分析在安徽电信差异化服务中的应用
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摘要
在中国电信运营商重组以后,国内电信业的市场环境已渐趋合理,它们之间的竞争更加激烈。客户是电信运营商生存和发展的根基[1],对客户的保有和争夺也就成为竞争的焦点。
作为全业务运营商,中国电信在C网业务发展的同时,导致部分固网业务的下滑,同时由于传统的固网是后付费方式,C网的用户大多还未接受预付费方式,这给企业带来了极大的欠费风险和收入增长压力。
为兼顾客户感知和企业利益,必须实现差异化服务和精确化营销,解决以往在固网中应用的普遍催缴的欠费管理方式,引入银行业的授信方法,提高企业的品牌形象和有针对性的欠费管理模式。
本文主要研究内容是在数据仓库的基础上,对用户的日消费行为进行分析和挖掘,获得内在规律性,实现对客户的差异服务和管理。
在电信业,普遍存在着“信息丰富”,“知识贫乏”的现象,传统处理方法难以发现其中蕴含的知识,迫切需要新知识、新技术来解决这个问题。用数据仓库对信息进行管理,并在此基础上对信息进行挖掘,从而应用于客户管理,销售策化,资源配置等诸多方面。在现实生产环境中,信息获取的高效和业务的智能,是挖掘结果能否被应用推广的至关重要因素。
为此,本文在数据仓库的设计和组织中,充分考虑了效率,挖掘算法的设计也考虑了实时性的要求,在挖掘结果的展示中借助COGNOS的强大功能,为前端员工提供了高效、实时的界面,以期对企业的信息化建设探索一条有本地特色的可行之路。
本论文的主要工作有:
(1)设计一个符合生产条件的数据仓库,实现对元数据的管理。借助ORACLE的新特性,以进程流实现对数据的逐步归约。为提高挖掘精度和实现界面响应高效做基础准备。
(2)根据中国电信业务特点和用户消费习惯,以回归模式对用户数据进行挖掘,解决传统算法中通过提高测试集来提高挖掘精度和离线分析的方法,满足实际生产的要求。
(3)在展示时,将COGNOS集成在.NET的框架中,实现业务智能和使用者的个性需求。
After the reorganization of the domestic telecommunications industry, the market environment has become more reasonable and more intense competition between telecom providers in China, Customers are telecom providers survival and development foundation, retain customers and compete has become the focus of competition.
As a full service provider, China Telecom’s business development in the C network, leading to some decline of tone business in cable telephone, and because the traditional business is post-paid way, C network users not to accept prepaid most of them to enterprise arrears has brought great pressure on the risk and income growth.
To take into account customer perception and profits, to achieve differentiation and precision services for customers, the application of cable network universal reminder of the arrears management, the introduction of methods of banking credit, and improving brand image and target management of arrears .
This dissertation studies the content of the data warehouse is based on the daily consumption of user behavior analysis and mining, were intrinsic regularity to realize the difference between customer service and management.
In the telecommunications industry, widespread has a kind of phenomenon of "information rich", "knowledge poor", the traditional approach difficult to find it contains the knowledge of the urgent need for new knowledge, new technology to solve the problem. Information with data warehouse management, and based on the information to mining, which applies to client management, sales strategy, resource allocation and other aspects. In the real production environment, efficient access to information and business intelligence is the mining results can be applied to promote the crucial factor.
The dissertation, the data warehouse design and organization, fully taking into account the efficiency of mining algorithm is also designed to consider the real-time requirements, the mining results demonstrate the power of using COGNOS for the marketing employees provides a highly efficient real-time interface ,and to the enterprise's information construction to explore with local characteristics of a practical way.
The main work of this thesis:
(1) Designed data warehouse production conditions, to achieve the management of metadata. With ORACLE new features, in order to process the data stream to achieve a gradual reduction. To improve mining accuracy and achieve interface to prepare to respond efficiently.
(2) According to China Telecom's business characteristics and user habits, a kidn of online regression algorithm be used to improve depends testing set to improve the mining accuracy of the traditional and off-line analysis method algorithm, to meet the production of Practical requirements.
(3) In the web, COGNOS integrated in the. NET framework, to achieve business intelligence and individual needs of users
作为全业务运营商,中国电信在C网业务发展的同时,导致部分固网业务的下滑,同时由于传统的固网是后付费方式,C网的用户大多还未接受预付费方式,这给企业带来了极大的欠费风险和收入增长压力。
为兼顾客户感知和企业利益,必须实现差异化服务和精确化营销,解决以往在固网中应用的普遍催缴的欠费管理方式,引入银行业的授信方法,提高企业的品牌形象和有针对性的欠费管理模式。
本文主要研究内容是在数据仓库的基础上,对用户的日消费行为进行分析和挖掘,获得内在规律性,实现对客户的差异服务和管理。
在电信业,普遍存在着“信息丰富”,“知识贫乏”的现象,传统处理方法难以发现其中蕴含的知识,迫切需要新知识、新技术来解决这个问题。用数据仓库对信息进行管理,并在此基础上对信息进行挖掘,从而应用于客户管理,销售策化,资源配置等诸多方面。在现实生产环境中,信息获取的高效和业务的智能,是挖掘结果能否被应用推广的至关重要因素。
为此,本文在数据仓库的设计和组织中,充分考虑了效率,挖掘算法的设计也考虑了实时性的要求,在挖掘结果的展示中借助COGNOS的强大功能,为前端员工提供了高效、实时的界面,以期对企业的信息化建设探索一条有本地特色的可行之路。
本论文的主要工作有:
(1)设计一个符合生产条件的数据仓库,实现对元数据的管理。借助ORACLE的新特性,以进程流实现对数据的逐步归约。为提高挖掘精度和实现界面响应高效做基础准备。
(2)根据中国电信业务特点和用户消费习惯,以回归模式对用户数据进行挖掘,解决传统算法中通过提高测试集来提高挖掘精度和离线分析的方法,满足实际生产的要求。
(3)在展示时,将COGNOS集成在.NET的框架中,实现业务智能和使用者的个性需求。
After the reorganization of the domestic telecommunications industry, the market environment has become more reasonable and more intense competition between telecom providers in China, Customers are telecom providers survival and development foundation, retain customers and compete has become the focus of competition.
As a full service provider, China Telecom’s business development in the C network, leading to some decline of tone business in cable telephone, and because the traditional business is post-paid way, C network users not to accept prepaid most of them to enterprise arrears has brought great pressure on the risk and income growth.
To take into account customer perception and profits, to achieve differentiation and precision services for customers, the application of cable network universal reminder of the arrears management, the introduction of methods of banking credit, and improving brand image and target management of arrears .
This dissertation studies the content of the data warehouse is based on the daily consumption of user behavior analysis and mining, were intrinsic regularity to realize the difference between customer service and management.
In the telecommunications industry, widespread has a kind of phenomenon of "information rich", "knowledge poor", the traditional approach difficult to find it contains the knowledge of the urgent need for new knowledge, new technology to solve the problem. Information with data warehouse management, and based on the information to mining, which applies to client management, sales strategy, resource allocation and other aspects. In the real production environment, efficient access to information and business intelligence is the mining results can be applied to promote the crucial factor.
The dissertation, the data warehouse design and organization, fully taking into account the efficiency of mining algorithm is also designed to consider the real-time requirements, the mining results demonstrate the power of using COGNOS for the marketing employees provides a highly efficient real-time interface ,and to the enterprise's information construction to explore with local characteristics of a practical way.
The main work of this thesis:
(1) Designed data warehouse production conditions, to achieve the management of metadata. With ORACLE new features, in order to process the data stream to achieve a gradual reduction. To improve mining accuracy and achieve interface to prepare to respond efficiently.
(2) According to China Telecom's business characteristics and user habits, a kidn of online regression algorithm be used to improve depends testing set to improve the mining accuracy of the traditional and off-line analysis method algorithm, to meet the production of Practical requirements.
(3) In the web, COGNOS integrated in the. NET framework, to achieve business intelligence and individual needs of users
引文
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[2]刘顺挺.基于数据挖掘技术的个人信用评估模[D].http://www.netyi.net.
[3]曾志刚.影响电信宽带客户流失的原因分析[J].现代电信科技2007第1期
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[5] Wu Zhibi. datamining techniques for the system and application of telecommunication customer & billing analysis[D]. [硕士学位论文].2006:17-21.
[6]韩加玮.《数据挖掘一概念与技术》[M].高等教育出版社.2001
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[8]冯勤.基于回归数据挖掘预测系统的分析与研究[D]. [硕士学位论文].天津大学2005.9
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[12]陶露菁.基于数据挖掘的电信客户分群设计和实现[D]. [硕士学位论文].南京大学.
[13]恽爽,胡南军.董浚等数据挖掘软件现状研究[J].计算机工程与应用.2003.8
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[15]张森等.基于电信数据挖掘的研究与应用[D].浙江工业大学硕士学位论文.2001.
[16]朱爱群.客户关系管理与数据挖掘[M].中国财政经济出版社.2001.
[17]刘延民.基于数据挖掘的电信企业客户流失模型研究[D]. [硕士学位论文].电子科技大学.
[18]龙志勇.数据挖掘在电信客户关系管理的应用[J].信息网络,2003.
[19]胡世良.我国电信市场环境的几点变化[J].通讯世界.1996.8.
[20]郭道宁,舒华英.数据挖掘在电信运营市场决策支持中的应用[D].北京.邮电大学学报.2004.6.
[21]胡敏.CRM离普及有多远[J].中国计算机用户.2003.
[22]郭琳.论客户关系管理对顾客价值创造理论的发展[J].经济师.2003.9.
[23]菲利普科特勒.市场营销管理(亚洲版) [M].中国人民大学出版社.2002.
[24]齐佳音,舒华英.电信营业客户价值研究的紧迫性及方向探讨[J].电信科学.2003.6.
[25]郝丽娜,吴光宇,陈文林等.数据库复制技术在数据仓库化的决策支持系统中的应用[J].北大学机械工程与自动化学院.上海金山钢铁集团设备部.2000.
[26] Oracle Database Data Warehousing Guide 11g Release 1 (11.1) Part Number B28313-02[EB/OL].2007.http://otn.oracle.com.
[27]胡侃,施鹏飞.基于大型数据仓库的数据挖掘研究综述.软件学报.1998.
[28]邹先霞,王淑礼,魏长华.数据仓库与数据挖掘技术浅谈.高等函授学报(自然科学版).第13卷第3期2000年6月.
[29]胡侃,夏绍玮.基于大型数据仓库的数据挖掘:研究综述.软件学报.1998(1).
[30]王冰清,怀进鹏.IDSS中数据仓库和数据挖掘的研究与实现.北京航空航天大学学报.1998(4).
[31]郑纬民,黄刚.数据挖掘纵览[J].计算机世界.1999年5月31日.
[32]胡珊.数据仓库技术与联机分析处理[M].科学出版社.1998.
[33] Oracle database performance tuning guide11g release 1 (11.1).[EB/OL]. part number B28274-02.2008.http://otn.oracle.com.
[34] Robert Stackowiak.Joseph Rayman.Rick Greenwald.Oracle data warehousing and business intelligence solutions[M].Wiley Publishing, Inc.http://www.wiley.com .2007.
[35] Bob Griesemer.Oracle warehouse builder 11g gettingstarted. Packt Publishing Ltd. www.packtpub.com.2009.
[36] Oracle warehouse builder user's guide 11g release 1 (11.1) b31278-06. [EB/OL].2009.http://otn.oracle.com.
[37] Oracle 10g/11g Data and database management utilities[M]. Packt Publishing Ltd. http://www.packtpub.com.2007.
[38] Oracle data mining concepts 11g release 1 (11.1) b28129-04. [EB/OL] . http://otn.oracle.com.2008.
[39] Oracle Data mining administrator's guide 11g release 1 (11.1). [EB/OL] .b28130-04. http://otn.oracle.com.2008.
[40] Oracle Data mining application developer's guide 11g release 1(11.1) b28131-04. [EB/OL]. http://otn.oracle.com.2008.
[41]朱建秋.数据挖掘系统发展综述[J].上海复旦德门软件公司.http://www.datamining.cn.
[42]罗可,蔡碧野,卜胜贤等.数据挖掘及其发展研究[J],计算机工程与应用,2002.14
[43] Lilian Hobbs.Susan Hillson.Shilpa Lawande.Oracle 10g data warehouse[etal]. Elsevier Digital Press.2005.
[44]张众,欧阳一鸣,王浩等.数据挖掘在金融领域中的应用研究[D]. [硕士学位论文].计算机编程与应用.2004.1.
[45]王翠英,林齐宁.基于客户细分的电信数据挖掘[J].信息网络,2004.1.
[46]刘君强,孙晓莹,王勋等.挖掘最大频繁模式的新方法[D]. [硕士学位论文].计算机学报.2004 .2.
[47]数据挖掘讨论组.http:www.dmgroup.org.cn.
[48]数据挖掘研究院下载天空.http://www.dmresearch.net.
[2]刘顺挺.基于数据挖掘技术的个人信用评估模[D].http://www.netyi.net.
[3]曾志刚.影响电信宽带客户流失的原因分析[J].现代电信科技2007第1期
[4]孟玉明.数据挖掘在电信客户流失中的应用研究[D].[硕士学位论文].中南大学,2007.
[5] Wu Zhibi. datamining techniques for the system and application of telecommunication customer & billing analysis[D]. [硕士学位论文].2006:17-21.
[6]韩加玮.《数据挖掘一概念与技术》[M].高等教育出版社.2001
[7]王永富.数据仓库和数据挖掘技术在电信行业经营分析中的研究与应用[D].[硕士学位论文].重庆大学.
[8]冯勤.基于回归数据挖掘预测系统的分析与研究[D]. [硕士学位论文].天津大学2005.9
[9]梁绮颖.数据挖掘在客户维系挽留中的应用[D]. [硕士学位论文].暨南大学.2007.4.
[10]段云峰,吴唯云,李剑威等数据仓库及其在电信领域的应用[M].电子工业出版社.2000.
[11]陈娜.数据挖掘技术的研究现状及发展方向[J].电脑与信息技术.第14卷第1期. 2006年2月.
[12]陶露菁.基于数据挖掘的电信客户分群设计和实现[D]. [硕士学位论文].南京大学.
[13]恽爽,胡南军.董浚等数据挖掘软件现状研究[J].计算机工程与应用.2003.8
[14]郭彦伟.电信客户流失分析的数据应用技术[D].北京.北京科技大学[硕士学位论文].2006.
[15]张森等.基于电信数据挖掘的研究与应用[D].浙江工业大学硕士学位论文.2001.
[16]朱爱群.客户关系管理与数据挖掘[M].中国财政经济出版社.2001.
[17]刘延民.基于数据挖掘的电信企业客户流失模型研究[D]. [硕士学位论文].电子科技大学.
[18]龙志勇.数据挖掘在电信客户关系管理的应用[J].信息网络,2003.
[19]胡世良.我国电信市场环境的几点变化[J].通讯世界.1996.8.
[20]郭道宁,舒华英.数据挖掘在电信运营市场决策支持中的应用[D].北京.邮电大学学报.2004.6.
[21]胡敏.CRM离普及有多远[J].中国计算机用户.2003.
[22]郭琳.论客户关系管理对顾客价值创造理论的发展[J].经济师.2003.9.
[23]菲利普科特勒.市场营销管理(亚洲版) [M].中国人民大学出版社.2002.
[24]齐佳音,舒华英.电信营业客户价值研究的紧迫性及方向探讨[J].电信科学.2003.6.
[25]郝丽娜,吴光宇,陈文林等.数据库复制技术在数据仓库化的决策支持系统中的应用[J].北大学机械工程与自动化学院.上海金山钢铁集团设备部.2000.
[26] Oracle Database Data Warehousing Guide 11g Release 1 (11.1) Part Number B28313-02[EB/OL].2007.http://otn.oracle.com.
[27]胡侃,施鹏飞.基于大型数据仓库的数据挖掘研究综述.软件学报.1998.
[28]邹先霞,王淑礼,魏长华.数据仓库与数据挖掘技术浅谈.高等函授学报(自然科学版).第13卷第3期2000年6月.
[29]胡侃,夏绍玮.基于大型数据仓库的数据挖掘:研究综述.软件学报.1998(1).
[30]王冰清,怀进鹏.IDSS中数据仓库和数据挖掘的研究与实现.北京航空航天大学学报.1998(4).
[31]郑纬民,黄刚.数据挖掘纵览[J].计算机世界.1999年5月31日.
[32]胡珊.数据仓库技术与联机分析处理[M].科学出版社.1998.
[33] Oracle database performance tuning guide11g release 1 (11.1).[EB/OL]. part number B28274-02.2008.http://otn.oracle.com.
[34] Robert Stackowiak.Joseph Rayman.Rick Greenwald.Oracle data warehousing and business intelligence solutions[M].Wiley Publishing, Inc.http://www.wiley.com .2007.
[35] Bob Griesemer.Oracle warehouse builder 11g gettingstarted. Packt Publishing Ltd. www.packtpub.com.2009.
[36] Oracle warehouse builder user's guide 11g release 1 (11.1) b31278-06. [EB/OL].2009.http://otn.oracle.com.
[37] Oracle 10g/11g Data and database management utilities[M]. Packt Publishing Ltd. http://www.packtpub.com.2007.
[38] Oracle data mining concepts 11g release 1 (11.1) b28129-04. [EB/OL] . http://otn.oracle.com.2008.
[39] Oracle Data mining administrator's guide 11g release 1 (11.1). [EB/OL] .b28130-04. http://otn.oracle.com.2008.
[40] Oracle Data mining application developer's guide 11g release 1(11.1) b28131-04. [EB/OL]. http://otn.oracle.com.2008.
[41]朱建秋.数据挖掘系统发展综述[J].上海复旦德门软件公司.http://www.datamining.cn.
[42]罗可,蔡碧野,卜胜贤等.数据挖掘及其发展研究[J],计算机工程与应用,2002.14
[43] Lilian Hobbs.Susan Hillson.Shilpa Lawande.Oracle 10g data warehouse[etal]. Elsevier Digital Press.2005.
[44]张众,欧阳一鸣,王浩等.数据挖掘在金融领域中的应用研究[D]. [硕士学位论文].计算机编程与应用.2004.1.
[45]王翠英,林齐宁.基于客户细分的电信数据挖掘[J].信息网络,2004.1.
[46]刘君强,孙晓莹,王勋等.挖掘最大频繁模式的新方法[D]. [硕士学位论文].计算机学报.2004 .2.
[47]数据挖掘讨论组.http:www.dmgroup.org.cn.
[48]数据挖掘研究院下载天空.http://www.dmresearch.net.