数据挖掘技术在移动商务客户价值识别中的应用研究
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摘要
当前计算机技术已经发展到了移动互联网时代,这意味着将有超过10亿的用户和智能设备,能够随时随地接入网络当中进行信息的交互,且移动互联网终端将成为人们获取信息和分享个人知识的重要窗口,在移动终端上也将应运而生各种各样的商业产品。伴随着移动互联网随时获取和分享知识重要性的不断增加,如何更好地服务与吸引客户,提供客户真正需要的产品,以及发现客户的潜在需求,这已成为众多商家们研究的热点问题。为了深入研究移动商务市场的客户价值,本文使用数据挖掘技术的决策树(C5.0)和关联规则(Apriori)方法,对移动商务的客户价值及商业价值进行研究,从而判断哪类人群是高价值客户,识别出高价值客户人群所具有的特征,从而为企业进一步开展客户营销,更好的抓住目标客户,提供一种便于操作、可行的方法。识别出移动商务当前和潜在高价值客户人群及其所具有的特征,这一决策模型可以为移动商务价值链上的利益群体提供决策。首先运营商要创新渠道模式,创造一个让用户了解并满足顾客需求的体验价值平台。再者,内容提供商要为目标客户群提供契合度更高、更丰富的产品内容,只有提供了好的内容才能够吸引更多的用户,也只有用户通过对内容的体验才能增加对移动商务的认识,才能进一步促进移动商务的发展。总之,本文只是数据挖掘技术在移动商务应用中的一个缩影,可以进一步延伸移动商务价值链上的商业潜力。在价值链的各个环节都可以使用数据挖掘技术来挖掘其潜力及价值,从而更好地服务移动商务客户,开发客户所需要的产品。
Current computer technology has developed to the mobile Internet era, which means that there will be over one billion users and intelligent devices, anytime, anywhere access network which the interaction of information. Mobile Internet devices will become the way people obtain information and important window that people share their personal knowledge. Moreover, on the mobile terminal will emerge a variety of commercial products. Along with the importance of access and share knowledge on mobile Internet is increasingly rising, how to better serve and attract customers, providing customers really need, and the discovery of potential customer needs, which has become a hot issue for many businessmen. For in-depth study of the customer value of mobile commerce market, the article uses decision tree (C5.0) and association rules ((Apriori)) of data mining technology, research on customer value and business value of mobile commerce in order to determine which groups is high-value customers, and then, to identify the main features of high value customers, beneficial to enterprise further development of customer market, to better grasp the target customers, as well as provide an easy operation and feasible method. Identify the current and potential high-value customer groups and their characteristics, this decision-making model can provide decision for value chain and interest groups of mobile commerce.Firstly operators need to create a innovative channel model, to create experience platform that user can better understand and meet customer needs. Furthermore, the content providers need to provide a richer and better content with target customer. Only provide the enough good product for the user, the user will have a better experience and can increase the content of mobile commerce understanding and further promote the development of mobile commerce. In short, this paper just is a microcosm of mobile business applications by using data mining technology. We can further extend this method to the all aspects of the value chain of mobile business. In the whole process of value chain of mobile business can use data mining techniques to exploit its potential and value and then can better serve for the mobile business customers, develop product that customer better need.
Current computer technology has developed to the mobile Internet era, which means that there will be over one billion users and intelligent devices, anytime, anywhere access network which the interaction of information. Mobile Internet devices will become the way people obtain information and important window that people share their personal knowledge. Moreover, on the mobile terminal will emerge a variety of commercial products. Along with the importance of access and share knowledge on mobile Internet is increasingly rising, how to better serve and attract customers, providing customers really need, and the discovery of potential customer needs, which has become a hot issue for many businessmen. For in-depth study of the customer value of mobile commerce market, the article uses decision tree (C5.0) and association rules ((Apriori)) of data mining technology, research on customer value and business value of mobile commerce in order to determine which groups is high-value customers, and then, to identify the main features of high value customers, beneficial to enterprise further development of customer market, to better grasp the target customers, as well as provide an easy operation and feasible method. Identify the current and potential high-value customer groups and their characteristics, this decision-making model can provide decision for value chain and interest groups of mobile commerce.Firstly operators need to create a innovative channel model, to create experience platform that user can better understand and meet customer needs. Furthermore, the content providers need to provide a richer and better content with target customer. Only provide the enough good product for the user, the user will have a better experience and can increase the content of mobile commerce understanding and further promote the development of mobile commerce. In short, this paper just is a microcosm of mobile business applications by using data mining technology. We can further extend this method to the all aspects of the value chain of mobile business. In the whole process of value chain of mobile business can use data mining techniques to exploit its potential and value and then can better serve for the mobile business customers, develop product that customer better need.
引文
[1]余凯锐.基于价值创新的移动商务模式研究.管理科学与工程.浙江大学硕士学位论文.2007,04.
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[3]柴梦竹.基于Web的数据挖掘研究综述[J].职大学报,2010.
[4]贾泽露,刘耀林.可视化空间数据挖掘研究综述[J].地理空间信息,2009,7(6).
[5]邓维维,彭宏,郑启伦.基于数据流的移动数据挖掘研究综述[J].计算机应用研究.
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[9]陈明亮,李怀祖.客户价值细分与保持策略研究成组技术与生产现代化.2001(4)：23-27.
[10]潘越.基于CLV与客户忠诚的客户细分方法研究.大连理工大学.2004.
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[12]陈峰.基于决策树和相异度算法的移动通信客户分类方法[J].计算机应用,2009,29(8).
[13]聂捷楠,葛波,任伟.基于手机短信的电子商务平台设计与实现[J].微计算机技术.2006.
[14]Siau, K., Ee-Peng. Lim, Z. Shen. Mobile Commerce, Promise, Challenges and Research Agenda [J]. Journal of Database Management, 2001:12 (3).
[15]N. A.Mylonopoulos, G. I.Doukidis. Mobile Business:Technological pluralism, social assimilation, and growth [J]. International Journal of Electronic Commerce.2003,8(1):5-22.
[16]T. P. Liang, C. P. Wei. Introduction to the Special Issue:Mobile Commerce Applications [J]. International Journal of Electronic Commerce, 2004,8(3):7-17.
[17]Turban, E., and King, D. Introduction to E-Commerce [M]. Upper Saddle River, NJ Prentice Hall.2003.
[18]Guropp Andreas, Pousttchi Key. The Mobility-M-framework for Application of Mobile Technology in Business Processes[C]. INFORMATIK 2005-Informatik LIVE.Bonn,2005:523-527.
[19]Brian E. Mennecke Applications [M], Idea Troy J. Strader, Mobile Commerce:Technology, Theory, and Group Publishing, Hershey, PA, 2003.
[20]魏丽燕.移动商务商业模式比较研究及创新[D].硕士学位,北京邮电大学,2008.
[21]中国互联网络信息中心(CNNIC).第29次中国互联网络发展状况统计报告,2012,1.
[22]元昌安.数据挖掘原理与spss clementine应用宝典[M].北京：电子工业出版社,2009.
[27]廖单,余英泽.数据挖掘和数据仓库及其在电信业中的应用[J].重庆邮电学院学报：自然科学版.2000,12(4)：31-34.
[28]刘霞,王爱民.数据挖掘技术在CRM中的应用[J].现代管理科学,2004,3：84-85.
[29]王扶东,马玉芳.基于数据挖掘的客户细分方法的研究[J].计算机工程与应用,2011,47(4).
[30]李宝东,宋瀚涛.数据挖掘在客户关系管理(CRM)中的应用[J].计算机应用研究,2002,19(10)：71-74.
[31]Kamakura, W. A., M. A, Wedel, M., Rosa, F.,&Mazzon, J. A. (2003). Cross-s elling through database marketing a mixed data factor data factor analyzer for data augmentation and prediction[J]. International Journal of Research in Marketing,2003(20):45-65.
[32]王新军,胡曼.数据挖掘技术在寿险业交叉销售中的应用[J].保险研究,2009,6.
[33]T. Redman. Data Quality:Management and Technology. Bantam Books[M],1992.
[34]蔡丽艳,杜永久.基于数据挖掘的移动通信业客户流失分析研究[J].微计算机信息,2010,26(10).
[35]桂洁,段继光,杨素科.客户流失预测数据挖掘方法对比分析[J].电脑学习,2010,4.
[36]Gustafsson Anders, Johnson Michael D, The Effects Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention [J], Journal of Marketing,2005,69(4):210-218.
[37]单友成.CRM中模糊数据挖掘及客户生命周期价值与客户满意度研究[D].硕士学位,天津大学管理学院,2009.
[38]王刚,王元亮.基于时间序列模型数据挖掘的商务智能[J].实践与经验,2009,6.
[39]许玲.利用信息资源的开发与整合挖掘未来移动商务潜力[D].硕士学位,华东师范大学,2008.
[40]娄景辉.数据挖掘技术在移动运营商客户关系管理中的应用研究[D].硕士学位,解放军信息工程大学,2008.
[41]鲁耀斌,邓朝华,陈致豫.移动商务的应用模式和采纳研究[M].科学出版社,2008.
[42]詹川.国内移动商务研究分析[J].中国管理信息化,2010,13(4).
[43]Davis F.1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology[J].MIS quarterly,13(3): 319-340.
[44]赛迪网[0L]:http://www.ccidnet.com/
[2]E. W. T. Ngai, A Guansekaran. A Review for Mobile Commerce Research and Applications [J].Decision Support System,2007,43(1):3-15.
[3]柴梦竹.基于Web的数据挖掘研究综述[J].职大学报,2010.
[4]贾泽露,刘耀林.可视化空间数据挖掘研究综述[J].地理空间信息,2009,7(6).
[5]邓维维,彭宏,郑启伦.基于数据流的移动数据挖掘研究综述[J].计算机应用研究.
[6]杨春.数据挖掘在移动通信业高价值客户关系管理中的应用研究[D].硕士学位,吉林大学,2007.
[7]Courtheaux, R. J. Database Marketing:Developing a Profitable Mailing Pian. CatalogAge, June-July.1986.
[8]吴春阳.数据挖掘在电子商务旅游线路推荐系统中的应用研究[D].硕士学位,重庆交通大学,2009.
[9]陈明亮,李怀祖.客户价值细分与保持策略研究成组技术与生产现代化.2001(4)：23-27.
[10]潘越.基于CLV与客户忠诚的客户细分方法研究.大连理工大学.2004.
[11]孟卫平.数据挖掘在移动电子商务中的应用研究[J].信息化,2010.
[12]陈峰.基于决策树和相异度算法的移动通信客户分类方法[J].计算机应用,2009,29(8).
[13]聂捷楠,葛波,任伟.基于手机短信的电子商务平台设计与实现[J].微计算机技术.2006.
[14]Siau, K., Ee-Peng. Lim, Z. Shen. Mobile Commerce, Promise, Challenges and Research Agenda [J]. Journal of Database Management, 2001:12 (3).
[15]N. A.Mylonopoulos, G. I.Doukidis. Mobile Business:Technological pluralism, social assimilation, and growth [J]. International Journal of Electronic Commerce.2003,8(1):5-22.
[16]T. P. Liang, C. P. Wei. Introduction to the Special Issue:Mobile Commerce Applications [J]. International Journal of Electronic Commerce, 2004,8(3):7-17.
[17]Turban, E., and King, D. Introduction to E-Commerce [M]. Upper Saddle River, NJ Prentice Hall.2003.
[18]Guropp Andreas, Pousttchi Key. The Mobility-M-framework for Application of Mobile Technology in Business Processes[C]. INFORMATIK 2005-Informatik LIVE.Bonn,2005:523-527.
[19]Brian E. Mennecke Applications [M], Idea Troy J. Strader, Mobile Commerce:Technology, Theory, and Group Publishing, Hershey, PA, 2003.
[20]魏丽燕.移动商务商业模式比较研究及创新[D].硕士学位,北京邮电大学,2008.
[21]中国互联网络信息中心(CNNIC).第29次中国互联网络发展状况统计报告,2012,1.
[22]元昌安.数据挖掘原理与spss clementine应用宝典[M].北京：电子工业出版社,2009.
[27]廖单,余英泽.数据挖掘和数据仓库及其在电信业中的应用[J].重庆邮电学院学报：自然科学版.2000,12(4)：31-34.
[28]刘霞,王爱民.数据挖掘技术在CRM中的应用[J].现代管理科学,2004,3：84-85.
[29]王扶东,马玉芳.基于数据挖掘的客户细分方法的研究[J].计算机工程与应用,2011,47(4).
[30]李宝东,宋瀚涛.数据挖掘在客户关系管理(CRM)中的应用[J].计算机应用研究,2002,19(10)：71-74.
[31]Kamakura, W. A., M. A, Wedel, M., Rosa, F.,&Mazzon, J. A. (2003). Cross-s elling through database marketing a mixed data factor data factor analyzer for data augmentation and prediction[J]. International Journal of Research in Marketing,2003(20):45-65.
[32]王新军,胡曼.数据挖掘技术在寿险业交叉销售中的应用[J].保险研究,2009,6.
[33]T. Redman. Data Quality:Management and Technology. Bantam Books[M],1992.
[34]蔡丽艳,杜永久.基于数据挖掘的移动通信业客户流失分析研究[J].微计算机信息,2010,26(10).
[35]桂洁,段继光,杨素科.客户流失预测数据挖掘方法对比分析[J].电脑学习,2010,4.
[36]Gustafsson Anders, Johnson Michael D, The Effects Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention [J], Journal of Marketing,2005,69(4):210-218.
[37]单友成.CRM中模糊数据挖掘及客户生命周期价值与客户满意度研究[D].硕士学位,天津大学管理学院,2009.
[38]王刚,王元亮.基于时间序列模型数据挖掘的商务智能[J].实践与经验,2009,6.
[39]许玲.利用信息资源的开发与整合挖掘未来移动商务潜力[D].硕士学位,华东师范大学,2008.
[40]娄景辉.数据挖掘技术在移动运营商客户关系管理中的应用研究[D].硕士学位,解放军信息工程大学,2008.
[41]鲁耀斌,邓朝华,陈致豫.移动商务的应用模式和采纳研究[M].科学出版社,2008.
[42]詹川.国内移动商务研究分析[J].中国管理信息化,2010,13(4).
[43]Davis F.1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology[J].MIS quarterly,13(3): 319-340.
[44]赛迪网[0L]:http://www.ccidnet.com/