應用資料採礦技術探討顧客關係管理之研究－以雜誌出版業為例

Application of Data Mining in Customer Relationship Management-A Case Study of Magazine Publishing

10.6338/JDA.201110_6(5).0005

魏莞婷(Wan-Ting Wei)；丁台怡(Tai-I Ting)

顧客關係管理 ； 資料採礦 ； 集群分析 ； 隨機森林 ； CRM ； Data Mining ； Cluster Analysis ； Random Forest

Journal of Data Analysis

6卷5期（2011 / 10 / 01）

77 - 90

繁體中文

現代商業活動已經成為顧客導向的時代，顧客是企業賴以生存之根本，企業如何在高速脈動的產業變化中，維持營運與獲利成長，並且挖掘出對企業最有價值的顧客以及找出具有潛在獲利價值的顧客，這將是顧客關係管理CRM（Customer Relationship Management）中非常關鍵的議題。但在企業紛紛導入顧關係管理的同時，如何較他人得到更有價值的隱藏資訊，去瞭解顧客、為顧客量身訂作客製化產品及服務、維繫顧客忠誠度及和顧客建立良好的互動關係，則成為了致勝的關鍵。因此，資料採礦（Data Mining）就成為應用於顧客關係管理的重要技術之一。本研究利用某財經雜誌出版業之資料，區隔出不同價值的顧客，分析不同價值區隔的特性及消費習慣，進而提供企業作為行銷策略制定的參考。

Nowadays, commercial activity goes to the customer-oriented era. Customers are the fundamentals to a company. In this industries-changeable period, there are three key factors in CRM. (Customer Relationship Management): (1) How enterprises to remain in operation and get profitable growth. (2) How to find out the most valued clients. (3) How to discover clients who may bring most value of the potential profit.However, when more and more enterprises introduce the thesis of CRM. (Customer Relationship Management) into their company, there are some essential points that cannot be ignored; e.g. how to get the more valuable hidden information, how to understand the clients' needs deeply; provide Configuration-To-Order service, and keep good connection with clients. Therefore, Data Mining approach is one of the important techniques of applying CRM. The research adopts the information from a financial magazine publisher that distinguishes the different clients with different values and analyzes the consuming habits accordingly. And then provide enterprises with the research results as references of developing marketing strategies.

1. Boosting and Random Forest for Visual Recognition: www.iis.ee.ic.ac.uk/tkkim/iccv09_tutorial
2. www.crisp-dm.org
3. Berry, M.J.A.,Linoff, G.S.(1997).Data Mining Techniques: for Marketing, Sale, and Customer Support.New York:Wiley Computer.
4. Bhatia, A.(1999).Customer Relationship Management.toolbox Portal for CRM.
5. Breiman, L.(2001).Random Forest.Machine Learning,45,5-32.
6. Conlon, G.(1999).No Turing Black.Sales and Marketing Management,50-55.
7. Fayyad, U. M.,Piatetsky-Shapiro, G.,Smyth, P.(1996).Data Mining To Knowledge Discovery: An Overview.Menlo Park, CA:AAAI Press/MIT Press.
8. Frawley, W. J.,Piatetsky-Shapiro, G.,Matheus, C.(1991).Knowledge Discovery in Databases: An Overview.Cambridge, MA:AAAI Press/MIT Press.
9. Gartner Group(2000).,Stamford, CT:.
10. Heskett, J.L.,Sasser, W.E.(1997).Schlesinger LA. The service profit chain.NY:Free Press.
11. Kalakota, R.,Robinson, M.(1999).E-Business: Roadmap for Success.Massachusetts:Addison-Wesley.
12. Koch, R.(1998).The 80/20 Princple: The Secret of Achieving More with Less.New York:Doubleday.
13. Krzanowski, W.J.(1988).Principles of multivariate analysis: A user's perspective.Oxford:Clarendon Press.
14. Malhotra, N.K.(1999).Marketing research: An applied orientation (3rd ed.).Prentice-Hall, New Jersey.
15. Peppers, D.,Rogers, M.,Dorf, B.(1999).Is your company ready for one-to-one marketing?.Harvard Business Review,1999(January-February),151-160.
16. SCN Education B.V.(ed.),Vieweg(ed.),Verlagsgesellschaft, Sohn(ed.)(2001).Customer Relationship Management.The Netherlands:
17. Spengler, B.(1999).Eyes on the Customer.Computerworld,33,60-62.
18. Swift, R. S.(2001).Accelerating Customer Relationships using CRM and Relationship Technologies.Prentice-Hall PTR.
19. 林傑斌(2002)。資料採掘與OLAP理論與實務。台北市:文魁股份有限公司。
20. 謝邦昌、蘇志雄、鄭宇庭、葉劭緯(2005)。資料採礦與商業智慧─SQL Server 2005。台北市:鼎茂圖書出版股份有限公司。

1. 魏莞婷、郭訓志、江志民、丁台怡(2011)。運用資料挖掘技術探索顧客購買圖書特性。數據分析，6(6)，147-158。