机场VIP客户服务分析系统的设计与实现
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摘要
随着航空运输业的蓬勃发展,越来越多的航空公司之间的业务竞争日趋激烈,导致市场内的竞争压力提高,机票价格下跌,这为传统的航空公司造成了巨大的压力,也导致一些大航空公司和一些起步弱的新航空公司退出市场。首都空港贵宾服务管理有限公司是首都机场集团公司下属的机场地面贵宾服务机构,主要业务范围是为进出机场的政务贵宾、商务贵宾、商务嘉宾提供进出港服务、进出港延伸服务、普通旅客问讯服务、公务机飞行代理、飞行地面保障、机票出售等服务。随着组织机构不断扩充,接待服务量猛增,对服务质量提出更高要求,开发一套综合业务系统势在必行,同时,在新的系统上,对现有客户基础数据进行完善、挖掘并进行深度的需求和行为分析,提升VIP服务的质量,是当前信息化建设面临的新课题和新挑战。
本文设计开发了机场VIP客户服务分析系统,系统主要实现了对首都空港贵宾服务管理有限公司的VIP客户进行客户信息管理,客户服务管理,客户行为分析。其中客户信息管理又包括客户基本信息管理,会员卡管理,账户管理。客户服务管理包括航班查询,机票预订,酒店预订,电话值机等功能。客户行为分析首先对客户基本数据进行完善,通过客户行为对客户的满意度／忠诚度进行建模,分析客户的当前价值和潜在价值,利用数据挖掘中的聚类分析将VIP客户进行细分,找到对机场最重要客户,一般重要客户和次要客户,从而对其进行区别对待,进行一对一的服务,满足他们个性化的需求。将数据挖掘(Data Mining:DM)理论应用在机场VIP客户服务分析系统中,为数据挖掘技术在客户关系管理(Customer Relationship Management:CRM)中的应用做出了有益的尝试。
测试结果表明,系统具有良好的稳定性和可靠性,不仅有效地实现了机场VIP客户信息的管理,而且对客户服务进行准确地决策,极大地提高了公司的经济社会效益。
Along with the rapid development of China's voyage transportation, the service competition becomes more and more fierce between voyage companies day by day, resulting in that competing press in market increases and air ticket price plummets, which produces enormous press to traditional voyage companies, and leads to some great and weak voyage companies exiting the market. Capital airport VIP Service management limited company is a subordinate airport ground VIP service institution to capital airport group, and the main business is to provide service which including inward and outward service, extending inward and outward service, common travelers'enquiring service, official airplane fly agency, flight ground safeguard and selling air tickets and so on to the Home Affairs and business guests who get in and out the airport. With the expansion of the organization and increasing number of VIP guests, higher demands are raised to service quality, so developing a comprehensive business system is imperative, meanwhile completing the existing customer basic data, data mining and conducting deep requirement and action analysis in the new system, in order to promoting quality of VIP service, is the new task and challenge to current informationize construction.
The article designs and develops airport VIP customer service analysis system, which mainly realizes information management, customer service management and customer action analysis to capital airport VIP Service management limited company. Customer information management includes customer basic information management, membership card management, account management and so on. Customer service management includes querying scheduled flight, booking air ticket, booking hotel and telephone check-in service and so on. Customer action analysis completes customer basic data at first, modeling customers'satisfaction degree by their actions, analyzing current value and potential value of customers, and then subdividing the VIP customers with clustering analysis algorithm in data mining, and finally finding the most important customers, the second most important customers and the important customers in order to make a distinction among them, and give them different service by their level, satisfying their individual demands. Applying data mining theory to airport VIP customer service analysis system makes a beneficial trial in application of Customer Relationship Management (CRM) based on Data Mining (DM).
Test result makes clear that the system has good stability and reliability, which not only achieves effectively information management of airport VIP customers, but also makes accurately decision to customer service, so the system improves tremendous economic and society benefit to the company.
本文设计开发了机场VIP客户服务分析系统,系统主要实现了对首都空港贵宾服务管理有限公司的VIP客户进行客户信息管理,客户服务管理,客户行为分析。其中客户信息管理又包括客户基本信息管理,会员卡管理,账户管理。客户服务管理包括航班查询,机票预订,酒店预订,电话值机等功能。客户行为分析首先对客户基本数据进行完善,通过客户行为对客户的满意度／忠诚度进行建模,分析客户的当前价值和潜在价值,利用数据挖掘中的聚类分析将VIP客户进行细分,找到对机场最重要客户,一般重要客户和次要客户,从而对其进行区别对待,进行一对一的服务,满足他们个性化的需求。将数据挖掘(Data Mining:DM)理论应用在机场VIP客户服务分析系统中,为数据挖掘技术在客户关系管理(Customer Relationship Management:CRM)中的应用做出了有益的尝试。
测试结果表明,系统具有良好的稳定性和可靠性,不仅有效地实现了机场VIP客户信息的管理,而且对客户服务进行准确地决策,极大地提高了公司的经济社会效益。
Along with the rapid development of China's voyage transportation, the service competition becomes more and more fierce between voyage companies day by day, resulting in that competing press in market increases and air ticket price plummets, which produces enormous press to traditional voyage companies, and leads to some great and weak voyage companies exiting the market. Capital airport VIP Service management limited company is a subordinate airport ground VIP service institution to capital airport group, and the main business is to provide service which including inward and outward service, extending inward and outward service, common travelers'enquiring service, official airplane fly agency, flight ground safeguard and selling air tickets and so on to the Home Affairs and business guests who get in and out the airport. With the expansion of the organization and increasing number of VIP guests, higher demands are raised to service quality, so developing a comprehensive business system is imperative, meanwhile completing the existing customer basic data, data mining and conducting deep requirement and action analysis in the new system, in order to promoting quality of VIP service, is the new task and challenge to current informationize construction.
The article designs and develops airport VIP customer service analysis system, which mainly realizes information management, customer service management and customer action analysis to capital airport VIP Service management limited company. Customer information management includes customer basic information management, membership card management, account management and so on. Customer service management includes querying scheduled flight, booking air ticket, booking hotel and telephone check-in service and so on. Customer action analysis completes customer basic data at first, modeling customers'satisfaction degree by their actions, analyzing current value and potential value of customers, and then subdividing the VIP customers with clustering analysis algorithm in data mining, and finally finding the most important customers, the second most important customers and the important customers in order to make a distinction among them, and give them different service by their level, satisfying their individual demands. Applying data mining theory to airport VIP customer service analysis system makes a beneficial trial in application of Customer Relationship Management (CRM) based on Data Mining (DM).
Test result makes clear that the system has good stability and reliability, which not only achieves effectively information management of airport VIP customers, but also makes accurately decision to customer service, so the system improves tremendous economic and society benefit to the company.
引文
[1]刘家乐.基于数据仓库与数据挖掘的航空公司客户关系管理研究.[中国名航大学硕士学位论文].2007
[2]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.[天津大学硕士学位论文].2009
[3]Jiawei Han. Issues for On-Line Analytical Mining of Data Warehouses
[4]M. J. ZAKI, S. PARTHASA, W. LI. A localized algorithm for parallel association mining.9'h Annual ACM Symposium on Parallel Algorithms and Architectures, Newport, Rhode Island, June 2007
[5]代成宏.基于客户关系管理的企业业务流程重组.科技和产业,2008,8(1)：37-39
[6]刘兆龙.中国民航动态分析.计算机应用,2008,03
[7]李保林,陈延寿.数据挖掘技术在汽车销售领域的应用研究.湖北汽车工业学院,2010
[8]单友成,周满红,赵红.模糊型计划评审技术在海洋结构建造工期估算中的应用.天津大学学报,2009,11(2)：120-122
[9]Li J, Xu Y, Wang Y F, et al. Strongest association rulesmining for efficient applications. Dalian:Proceeding of the Fourth IEEE Conference on Service Systems and Service Management,2007,502-507
[10]夏昌明.数据挖掘在上海某电信局客户关系管理中的应用研究.[华东理工大学硕士学位论文].2010
[11]Ke Wang. Mining Frequent Itemsets Using Support Constraints. National University of SingaPore
[12]宋伟国.基于CF的个性化电子商务推荐系统研究.兰州：兰州大学,2010：15
[13]Jiawei Han, Micheline Kamber.数据挖掘：概念与技术.范明,孟小峰译.北京：机械工业出版社,2011.01：3
[14]Miehalski R S. Machine learning and data mining:Methods and applications. New York:Jone Wiley and Sons.2008
[15]李柏林.汽车售后服务故障件管理及数据挖掘技术的应用研究.成都：西南交通大学,2008
[16]邵峰晶,于忠清.数据挖掘原理与算法.北京：科学出版社出版,2009：164-170
[17]W. WANG, M. CHENG, C. F. LEE. Knowledge-based treatment planning for adolescent early intervention of mental healthcare:A hybrid case-based reasoning approach Expert Systems,2007, 24(4):232-251
[18]陈莉.数据库中的知识发现.西北大学学报,2005,22(5)：51-53
[19]郭秋萍.企业数据挖掘理论与实践.北京：黄河水利出版社,2005：18-36
[20]陈明亮.客户价值细分与保持策略研究.现代生产与管理技术,2006(16)：66-67
[21]刘夫涛.从OLAP、数据挖掘到OLAM.计算机世界,2009
[22]杨冬青.把握数据挖掘新动向.中国计算机报,2006
[23]Gehrke J. BOAT:Optimistic decision tree construction. InProc.1999 ACM-SIGMOD Int. Conf. Management of Data. PhiladelPhia, USA. June 1999:169-180
[24]刘秀梅.客户关系管理系统中数据挖掘技术的应用.武汉：华中师范大学,2004：1
[25]Fayyad U. Advances in knowledge discovery and data mining. California:AAAI/MIT Press, 2006
[26]R.ZAIANE. Malnmography Classification by an Association Rule-based Classifier, MDM/KDD 2008:International Workshop on Multimedia Data Mining, P62-68
[27]DUNHAM H. Data Mining-Introductory and Advanced ToP2ics. NewJersey:Prentice Hall, 2003.
[28]Robert Grossman, Simon Kasif, Reagan Moore, DavidRocke, JeffUllinan. "Data Mining Research:Opportunities and Challenges". A report of three NSF Workshops on Mining Large, Massive, and Distributed Data. January 2009
[29]夏维力,王鑫,王青松.数据挖掘技术在企业客户系管理中的应用研究.科技管理研究,2007,(6)：196-198
[30]吴川,关沉浮,柴天佑.数据仓库技术在移动通信业的应用.基础自动化,2009,9(1)：40-42
[31]赵玉勇,吴永明.在决策支持系统中应用数据仓库技术的研究.计算机系统应用,2009
[32]刘泉凤,陆蓓,王小华.文本挖掘中聚类算法的比较研究.计算机时代,2005,6
[33]赵俊杰.基于文本挖掘技术的论文抄袭判定研究.[合肥工业大学硕土学位论文].2009
[34]谭颖.文本挖掘中的聚类算法研究.[吉林大学硕十学位论文].2009
[35]李绪成等.挖掘关联规则中Apriori算法的一种改进.计算机工程,2008
[36]高洁,吉根林.文本分类技术研究.计算机应用研究,2004,7：28-34
[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.[天津大学硕士研究生学位论文].2009
[38]杨炳儒,张伟,钱榕.面向语义的精简化多关系频繁模式发现方法.中国工程科学,2008,10(9)：47-53
[39]Cunningham Colleen, Il-Yeol Song, Chen Peter P. Data Warehouse Design to Support Customer Relationship Management Analyses, Journal of Database Management,2006,17 (2):62-84
[40]张玉颖,姚家奕.浅析数据仓库在客户关系管理中的应用.价值工程,2004,3：127-128
[41]米天胜.数据仓库和数据挖掘在客户关系管理中的应用.情报技术,2005,9：18-20
[42]吴雪琴.浅析数据挖掘在CRM中的应用.电脑知识与技术,2008,9-10
[43]谢寰红.数据挖掘在证券公司CRM客户细分中的应用.开发研究与设计技术,2004,30(增刊)：553
[44]周欢.CRM中客户分类方法的研究与应用.计算机工程与设计,2008,29(3)：659-661
[45]于红霞,汪波,钱荣.基于三维客户分类价值体系的客户关系管理研究.商业经济与管理,2006,181(11)：43-47,67
[46]H. JIAWEI, K. MICHELINE数据挖掘概念与技术.第二版.范明,孟小峰译.北京：机械工业出版社,2007
[47]Pfeifer, Philip E, Haskins, Mark E, Conroy, Robert M. Customer Lifetime Value, Customer Profitability, and Treatment of Acquisition Spending, Journal of Managerial Issues.2005,1: 11-25
[48]Gupta Sunil, Hanssens Dominique, Hardie Bruce, Kahn Wiliam, Kumar V, Lin Nathaniel. Modeling Customer Lifetime Value. Journal of Service Research.2006,9 (2):139-155
[49]柴效武.客户生命周期价值法在银行客户价值衡量中的运用.海南金融,2006,10：38-41
[50]Gustafsson Anders, Johnson Michael D. The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention. Journal of Marketing,2005,69 (4):210-218
[51]洪燕冰,欧阳钟辉.数据挖掘技术与客户忠诚度.福建电脑,2007,11：5-6
[2]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.[天津大学硕士学位论文].2009
[3]Jiawei Han. Issues for On-Line Analytical Mining of Data Warehouses
[4]M. J. ZAKI, S. PARTHASA, W. LI. A localized algorithm for parallel association mining.9'h Annual ACM Symposium on Parallel Algorithms and Architectures, Newport, Rhode Island, June 2007
[5]代成宏.基于客户关系管理的企业业务流程重组.科技和产业,2008,8(1)：37-39
[6]刘兆龙.中国民航动态分析.计算机应用,2008,03
[7]李保林,陈延寿.数据挖掘技术在汽车销售领域的应用研究.湖北汽车工业学院,2010
[8]单友成,周满红,赵红.模糊型计划评审技术在海洋结构建造工期估算中的应用.天津大学学报,2009,11(2)：120-122
[9]Li J, Xu Y, Wang Y F, et al. Strongest association rulesmining for efficient applications. Dalian:Proceeding of the Fourth IEEE Conference on Service Systems and Service Management,2007,502-507
[10]夏昌明.数据挖掘在上海某电信局客户关系管理中的应用研究.[华东理工大学硕士学位论文].2010
[11]Ke Wang. Mining Frequent Itemsets Using Support Constraints. National University of SingaPore
[12]宋伟国.基于CF的个性化电子商务推荐系统研究.兰州：兰州大学,2010：15
[13]Jiawei Han, Micheline Kamber.数据挖掘：概念与技术.范明,孟小峰译.北京：机械工业出版社,2011.01：3
[14]Miehalski R S. Machine learning and data mining:Methods and applications. New York:Jone Wiley and Sons.2008
[15]李柏林.汽车售后服务故障件管理及数据挖掘技术的应用研究.成都：西南交通大学,2008
[16]邵峰晶,于忠清.数据挖掘原理与算法.北京：科学出版社出版,2009：164-170
[17]W. WANG, M. CHENG, C. F. LEE. Knowledge-based treatment planning for adolescent early intervention of mental healthcare:A hybrid case-based reasoning approach Expert Systems,2007, 24(4):232-251
[18]陈莉.数据库中的知识发现.西北大学学报,2005,22(5)：51-53
[19]郭秋萍.企业数据挖掘理论与实践.北京：黄河水利出版社,2005：18-36
[20]陈明亮.客户价值细分与保持策略研究.现代生产与管理技术,2006(16)：66-67
[21]刘夫涛.从OLAP、数据挖掘到OLAM.计算机世界,2009
[22]杨冬青.把握数据挖掘新动向.中国计算机报,2006
[23]Gehrke J. BOAT:Optimistic decision tree construction. InProc.1999 ACM-SIGMOD Int. Conf. Management of Data. PhiladelPhia, USA. June 1999:169-180
[24]刘秀梅.客户关系管理系统中数据挖掘技术的应用.武汉：华中师范大学,2004：1
[25]Fayyad U. Advances in knowledge discovery and data mining. California:AAAI/MIT Press, 2006
[26]R.ZAIANE. Malnmography Classification by an Association Rule-based Classifier, MDM/KDD 2008:International Workshop on Multimedia Data Mining, P62-68
[27]DUNHAM H. Data Mining-Introductory and Advanced ToP2ics. NewJersey:Prentice Hall, 2003.
[28]Robert Grossman, Simon Kasif, Reagan Moore, DavidRocke, JeffUllinan. "Data Mining Research:Opportunities and Challenges". A report of three NSF Workshops on Mining Large, Massive, and Distributed Data. January 2009
[29]夏维力,王鑫,王青松.数据挖掘技术在企业客户系管理中的应用研究.科技管理研究,2007,(6)：196-198
[30]吴川,关沉浮,柴天佑.数据仓库技术在移动通信业的应用.基础自动化,2009,9(1)：40-42
[31]赵玉勇,吴永明.在决策支持系统中应用数据仓库技术的研究.计算机系统应用,2009
[32]刘泉凤,陆蓓,王小华.文本挖掘中聚类算法的比较研究.计算机时代,2005,6
[33]赵俊杰.基于文本挖掘技术的论文抄袭判定研究.[合肥工业大学硕土学位论文].2009
[34]谭颖.文本挖掘中的聚类算法研究.[吉林大学硕十学位论文].2009
[35]李绪成等.挖掘关联规则中Apriori算法的一种改进.计算机工程,2008
[36]高洁,吉根林.文本分类技术研究.计算机应用研究,2004,7：28-34
[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.[天津大学硕士研究生学位论文].2009
[38]杨炳儒,张伟,钱榕.面向语义的精简化多关系频繁模式发现方法.中国工程科学,2008,10(9)：47-53
[39]Cunningham Colleen, Il-Yeol Song, Chen Peter P. Data Warehouse Design to Support Customer Relationship Management Analyses, Journal of Database Management,2006,17 (2):62-84
[40]张玉颖,姚家奕.浅析数据仓库在客户关系管理中的应用.价值工程,2004,3：127-128
[41]米天胜.数据仓库和数据挖掘在客户关系管理中的应用.情报技术,2005,9：18-20
[42]吴雪琴.浅析数据挖掘在CRM中的应用.电脑知识与技术,2008,9-10
[43]谢寰红.数据挖掘在证券公司CRM客户细分中的应用.开发研究与设计技术,2004,30(增刊)：553
[44]周欢.CRM中客户分类方法的研究与应用.计算机工程与设计,2008,29(3)：659-661
[45]于红霞,汪波,钱荣.基于三维客户分类价值体系的客户关系管理研究.商业经济与管理,2006,181(11)：43-47,67
[46]H. JIAWEI, K. MICHELINE数据挖掘概念与技术.第二版.范明,孟小峰译.北京：机械工业出版社,2007
[47]Pfeifer, Philip E, Haskins, Mark E, Conroy, Robert M. Customer Lifetime Value, Customer Profitability, and Treatment of Acquisition Spending, Journal of Managerial Issues.2005,1: 11-25
[48]Gupta Sunil, Hanssens Dominique, Hardie Bruce, Kahn Wiliam, Kumar V, Lin Nathaniel. Modeling Customer Lifetime Value. Journal of Service Research.2006,9 (2):139-155
[49]柴效武.客户生命周期价值法在银行客户价值衡量中的运用.海南金融,2006,10：38-41
[50]Gustafsson Anders, Johnson Michael D. The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention. Journal of Marketing,2005,69 (4):210-218
[51]洪燕冰,欧阳钟辉.数据挖掘技术与客户忠诚度.福建电脑,2007,11：5-6