跳到主要內容

臺灣博碩士論文加值系統

(18.208.126.232) 您好!臺灣時間:2022/08/12 02:24
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:李桂龍
研究生(外文):Lee, Guey-Long
論文名稱:資料探勘技術應用於銀行客戶關係管理之研究-以國軍同袍儲蓄會為例
論文名稱(外文):A study of Data Mining Application in Banking CRM-The Case for R.O.C Military Personnel Saving Administration
指導教授:莊謙亮莊謙亮引用關係傅振華傅振華引用關係
學位類別:碩士
校院名稱:國防管理學院
系所名稱:國防資訊研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:106
中文關鍵詞:資料探勘客戶關係管理線上分析處理軍人儲蓄作業
外文關鍵詞:data miningCRMOLAPmilitary saving transaction process
相關次數:
  • 被引用被引用:2
  • 點閱點閱:810
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:4
隨著資訊科技的進步,客戶關係管理與資訊科技的整合,在進入廿一世紀之際,常被許多企業列為資訊科技投資計劃之第一優先。而國軍同袍儲蓄會所經辦的軍人儲蓄業務,近年來存款人數金額均有持續遞減的情形,嚴重影響軍儲營運,是儲蓄會必須面對且急於解決的問題。
本研究利用資料探勘與線上分析處理之技術,探討如何在軍儲龐大的歷史資料庫中,找出隱藏有用的資訊,並參考現行銀行在客戶關係管理上的相關研究,建構一適用於軍人儲蓄作業下之客戶關係管理架構以提高對軍儲客戶的服務品質,同時也希望藉由資料探勘後的結果,掌握軍人儲蓄存款的脈動,以提供未來在軍儲基金財務管理、軍儲相關財務政策之規劃及主官決策之參考。
經過實際資料的探勘後,發現雖然基於某些主客觀因素,造成存款人數與金額逐年下滑,但仍有某些特定族群對軍儲仍深具信心,不論續存率或存款意願均極高,本研究也特別針對這些族群的特性加以探討並提出增進客戶關係管理建議與具體做法,期能對儲蓄會有效區隔存戶與掌握存戶行為及其特性有所助益。
As a progress in information technology, it is privilege for enterprises to integrate customer relationship management (CRM) and information technology in the 21th century. The R.O.C Military Personnel Saving Administration (PSA) processes the military saving transactions. In recent years, there existed a decreasing trend in the saving people and amount of money. This situation significantly impacts the operation of PSA. PSA must face this problem and solve it.
Referring the CRM of current banking, this study tries to use the technologies of data mining and on-line analytical processing (OLAP) to find some valuable information in a huge historical database, and this information can be applied to the CRM for PSA to improve its services and handle the trend of customers’ savings. The mined information could be the source information for PSA’s executive information system (EIS) or the decision information for PSA’s policy planning.
This study results show several interested phenomena in the PSA business. Although the number of saving people and the amount of money of PSA decrease, there are still some specific saving groups which are highly interested in depositing their money to PSA. These groups would be stable customer sources of PSA. The characteristics of these groups were analyzed in this study. This study also does some analysis about the potential customers to try to understand their features. For the different customer groups, this study also made some suggestions to PSA in order to enhance its CRM and increases its number of saving people and its amount of money.
1. 緒論 1
1.1研究背景與動機 1
1.1.1研究背景 1
1.1.2研究動機 2
1.2研究目的 4
1.3研究範圍與限制 5
1.3.1研究範圍 5
1.3.2研究限制 5
1.4 研究方法與程序 6
1.4.1研究方法 6
1.4.2研究程序 7
1.5論文架構 9
2. 文獻探討 12
2.1資料探勘 12
2.1.1何謂資料探勘 12
2.1.2資料探勘的功能 13
2.1.3資料探勘的步驟 15
2.2資料倉儲與線上分析處理 16
2.2.1資料倉儲的定義 16
2.2.2資料倉儲與資料探勘的關係 17
2.2.3線上分析處理 18
2.2.4線上分析處理的分類與技術架構 20
2.2.5線上分析探勘 22
2.2.6線上分析處理與資料探勘之關係 24
2.3客戶關係管理 25
2.3.1客戶關係管理的意義與架構 25
2.3.2客戶關係管理的分類與應用 27
2.3.3客戶關係管理與資訊科技之關係 29
3. 探勘模式與資料彙總 33
3.1研究工具簡介 34
3.2 研究模式及資料彙總 35
3.2.1 原始資料庫 37
3.2.2 資料整理 39
3.2.3 多維度資料庫(MDDB) 42
3.2.4 產生資料方塊(Data Cubes) 43
3.2.5 線上分析處理/線上分析探勘與資料探勘 45
3.2.6 資料展現 46
4. 資料探勘與評析 47
4.1存戶類別分析 47
4.2退員存款結構分析 51
4.2.1領俸金額區間分析 52
4.2.2存戶年齡分析 55
4.2.3存戶地區分析 57
4.2.4新舊俸金發放制度與存款意願分析 60
4.3其他存款結構分析 63
4.3.1存款人階級分析 63
4.3.2存款金額來源分析 67
4.3.3存戶階級與存款金額來源交叉分析 70
4.4從資料縱深面分析 73
4.5決策樹分類法分析 75
4.6分析結果彙整 78
5. 結論與未來研究方向 82
5.1結論 82
5.2論文貢獻 83
5.3未來研究方向 84
[1] 林文修,「Data Mining探索」,叡揚資訊,經營決策論壇13期  http://www.gss.com.tw/gsseis/13/mining.htm
[2] 莊雅蓁,「資料挖掘的意義與方法」,  http://lips.lis.ntu.edu.tw/ycchuang/study/othersubject/datawarehouse/DataMining.htm
[3] 漢康科技網頁,「產品介紹」,http://bi.fast.com.tw
[4] Jiawei Han, and Micheline Kamber, ”Data Mining Concepts and Techniques”,Morgan Kaufmann publishers, Chapter 1,p6。
[5] Z. Huang, ”Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values”, Proc. Data Mining and Knowledge Discovery , 1998.
[6] Frank Ravat, Olivier Teste, and Gilles Zurfluh, ”Toward Data Warehouse Design”, 1999, communication of the ACM.
[7] 蘇隄,「企業建構資料倉儲的六項關鍵議題」,遠擎管理顧問公司,顧客關係管理深度解析第十一篇,頁133-134。
[8] 張中平(2001.11.5),「OLAP(on-line analytical processing )介紹」,簡報資料。
[9] J. Han, ”OLAP Mining: An Integration of OLAP with Data Mining”, Proceedings of 1997 IFIP Conference on Data Semantics(DS-7), Leysin, Switzerladn, Oct 1997, pp.1-11.
[10] S. Goil, and A. Choudhary, ”A parallel scalable infrastructure for OLAP and data mining”, Database Engineering and Applications, IDEAS’99. International Symposium Proceedings, 1999, pp.178-186.
[11] D. Chatziantoniou, T. Johnson, M. Akinde, and S. Kim, ” The MD-join: an operator for complex OLAP”, Data Engineering, 2001. Proceedings. 17th International Conference on , 2001 ,pp. 524 —533.
[12] Jiawei Han, and Micheline Kamber, ”Data Mining Concepts and Techniques”,Morgan Kaufmann publishers, Chapter 2,pp.39-98。
[13] Audiman(1999.6.10),「什麼是Data Mining」,簡報資料。
http://dinosaur.soft.iecs.fcu.edu.tw/~dlyang/DataMining/一B1/index.htm
[14] 安訊資訊系統公司,「整合企業經營策略與顧客關係管理」,遠擎管理顧問公司,顧客關係管理深度解析第四篇,頁47。
[15] 陳曉開,「成功地發展及執行持續性的關係行銷」,遠擎管理顧問公司,顧客關係管理深度解析第八篇,頁88-89,整理自賣肯錫公司董事John Ott於台灣第一屆顧客關係管理研討會之演講。
[16] 葉涼川,「CRM Data Mining應用系統建置」,第三章頁3-5,台北:麥格羅˙希爾,譯自Alex Berson,Stephen Smith & Kurt Thearling。
[17] 林義堡,「運用資訊科技推動顧客關係管理」,遠擎管理顧問公司,顧客關係管理深度解析第六篇,頁58-71。
[18] 郭育成,「銀行業客戶關係管理應用現況與等級分析之研究」,  http://hawang.im.tku.edu.tw/hwang_paper/crm
[19] 史博言(民國88.11),「1999年度台灣業者之顧客關係管理運用現狀調查報告」,電子化企業經理人報告,頁9-15。
[20] 余小均(民國88.12),「CRM搭起企業與客戶的橋樑」,資訊與電腦,頁79-82。
[21] John Ott, “Successfully development and Implementing Continuous relationship management”, November 2000, eBusiness executive report, pp.26-30.
[22] Wayland, and Cole, “Customer connections”, Harvard Business School Press, November 1999, pp.404-431.
[23] 聯合勤務總司令部印頒(民國87.4.10),「國軍軍人儲蓄作業手冊」。
[24] 國軍同袍儲蓄會編印(民國88.2),「軍中儲蓄創辦四十週年紀念專輯-軍中儲蓄四十年」。
[25] 元智大學商業智慧實驗室,「商業智慧介紹及應用」,http://140.138.148.80/1507a/Tech.htm
[26] 莊濟誠,「資料挖掘-找出隱藏在你的資料中的寶藏」http://ibmdb2.cc.nctu.edu.tw/SoftwartToday/199702/page30.html
[27] 生命力國際股份有限公司,「Web效能分析工具」,http://www.xpower.com.tw/html/hrm-soft-main.htm
[28] 張瑋倫(民國89),「應用資料挖掘學習方法探討顧客關係管理問題」,輔仁大學資訊管理研究所碩士論文。
[29] 蔡永恆(民國89),「應用資挖掘技術研究銀行顧客消費行為」,靜宜大學資訊管理研究所碩士論文。
[30] A. Shoshani, ”OLAP and Statistical Databases: Similarities and Differences”, ACM Transactions on Database System, 1997, pp.185-196.
[31] A. Balmin, Y. Papakonstantinou, and T. Papadimitriou, ”Optimization of Hypothetical Queries in an OLAP Environment”, Data Engineering, 2000. Proceedings. 16th International Conference on , 2000, pp. 311 —311.
[32] Chang-Sup Park, Myoung Ho Kim, and Yoon-Joon Lee, ”Rewriting OLAP queries using materialized views and dimension hierarchies in data warehouses”, Data Engineering, 2001. Proceedings. 17th International Conference on , 2001, pp. 515 —523.
[33] Ching-Tien Ho, J. Bruck, and R. Agrawal, ”Partial-sum queries in OLAP data cubes using covering codes “, Computers, IEEE Transactions on , Dec. 1998 ,Vol.47 Issue.12 pp. 1326 —1340.
[34] H. Hasan, and P. Hyland, “Using OLAP and multidimensional data for decision making”, Sept.-Oct. 2001, IT Professional , Vol.3 Issue.5 pp.44-50.
[35] Jeff Caldwell, ”Building a sustainable E-Business CRM strategy”, Agency Sales Magazine, May 2000, pp.23-27.
[36] Karl Hahn, and Carsten Sapia, ” Automatically Generating OLAP Schemata from Conceptual Graphical”, International Workshop on Data Warehousing and OLAP.
[37] K. Hahn, C. Sapia, and M. Blaschka,(2000), ”Automatically Generating OLAP Schemata from Conceptual Graphical Models”, International Workshop on Data Warehousing and OLAP.
[38] M. Alavi, and P. Carlson,(1992),“A Review of MIS Research and Disciplinary Development.”, journal of Management Information System, Vol.8, NO.4, pp.45-62.
[39] Michael J.A. Berry, and Gordon Linoff, ”Data Mining Techniques For Marketing Sales, and Customer Support”, John Wiley & Sons, 1997,New York.
[40] Marcel Holsheimer, ”Data mining by business users: integrating data mining in business processes”, Tutorial notes for ACM SIGKDD 1999 international conference on Knowledge discovery and data mining, 1999, pp.266-291.
[41] O. Mangisengi, A.M. Tjoa, and R.R.Wagner, ” Metadata management concept for multidimensional OLAP data based on object-oriented concepts”, Web Information Systems Engineering, 2000. Proceedings of the First International Conference on , 2000, Vol.1, pp. 358 —365.
[42] Peggy Menconi, ”Building a great customer relationship management strategy”, August 1999, ARM research.
[43] Rpbert "Mike" Gardner, and Jack Bieker, “Solving Tough Semiconductor Manufacturing Problems Using Data Mining ” IEEE/SEMI Advanced Semiconductor Manufacturing Conference ,2000, pp. 46-55.
[44] Russ M. Dabbas, and Hung-Nan, ”Mining semiconductor manufacturing data for productivity improvement - an integrated relational database approach”, Computers in Industry, 2000, Vol.45, pp.29-44.
[45] R. Lehn, V. Lambert, and M.-P. Nachouki, ”Data warehousing tool''s architecture: from multidimensional analysis to data mining” ,Database and Expert Systems Applications, 1997. Proceedings., Eighth International Workshop on , 1997, pp. 636 —643.
[46] S.C. Hui, and G. Jha, ”Data mining for customer service support”, Information & management, 2000, Vol.38, pp.1-13.
[47] S. Geffner, D. Agrawal, A. El Abbadi, and T. Smith, ”Relative prefix sums: an efficient approach for querying dynamic OLAP data cubes”, Data Engineering, 1999. Proceedings., 15th International Conference on , 1999, pp. 328 —335.
[48] Tom Coyle, ”Finding your best customer”, America’s Community Banker, September 1999, pp.26-29.
[49] T.B. Pedersen, and C.S. Jensen, ”Multidimensional data modeling for complex data”, Data Engineering, 1999. Proceedings., 15th International Conference on , 1999, pp. 336 —345.
[50] V. Markl, F. Ramsak, and R. Bayer, ” Improving OLAP performance by multidimensional hierarchical clustering “,Database Engineering and Applications, 1999. IDEAS ''99. International Symposium Proceedings , 1999,pp. 165 —177.
[51] Wen-Yang Lin, and I-Chung Kuo, ” OLAP data cubes configuration with genetic algorithms”, Systems, Man, and Cybernetics, 2000 IEEE International Conference on , 2000, Vol.3, pp. 1984 —1989.
[52] Yoon, and Younghoc, ”Discovery knowledge in corporate databases”, Information System Management , Spring 1999.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top