應用資料採擷技術於顧客關係管理之消費行為研究－以某化粧品公司為例

A Study of Data Mining Application in Customer Behavior of CRM –a Case Study of a Cosmetics Company

徐來福(Lai-Fu Hsu)

指導教授 ： 張 百 棧 博士

元智大學/工程學院/工業工程與管理學系/碩士(2004年)

摘要

摘 要

現今電腦運算能力不斷躍進，資料儲存技術的大幅進步，以及市場競爭加遽，因此，顧客關係管理至為重要，化妝品公司藉由資料採擷(Data Mining)的各種分析方法與技術，進行分析、歸納與整合，淬取有用的資訊，找出顧客有興趣的樣式（Patterns），提供管理階層訂定決策的依據，並且在最短時間、最佳通路( Channel)、最適合的商品(Products) 及提供有需求顧客(Customers)之終極目標。本論文以資料採擷與類神經網路(Neural Network)方法，運用自組織映射圖網路(Self-Organizing Map, SOM)，及利用智力礦工（Intelligence Miner）之工具，將顧客的基本資料與消費記錄，做為輸入因子，藉由分群來分析其優異，作為在不同分群顧客中，進行不同需求的顧客區隔與不同的行銷活動，提升客戶服務品質與創造更高的利潤。

關鍵字

資料採擷、智力礦工、類神經網路、自組織映射圖網路

並列摘要

ABSTRACT

The operation ability of the computer is making a leap forward constantly now. Data storage technology keeps a rapid progress and the market competition is getting harder. Therefore, Customer Relationship Management (CRM) is so important today. The cosmetics company is using various kinds of analytical methods and technology provided by data mining to analyze, integrate, combine, quench and extract useful information to find out customer's interested patterns and offer the management level as the basis of making a decision. The ultimate goal of this demand is to provide customers with the shortest time, the best channel and the best products. This thesis tries to use the methods of data mining and Neural Network with the processing of self-organizing map (SOM) and the tools of Intelligence Miner. The customers’ personal information and consuming records are taken as input factors for cluster analysis. It separates different kind of customers who have different needs and different selling activities in order to promote customer service quality and create the higher profit.

並列關鍵字

Dada Mining, Intelligence Miner, Neural Network,Self-Organizing Map, SOM

參考文獻

41.陳德華，「混合特徵資料的自我組織特徵映射網路」，私立中原大學應用數學系，碩士論文，民國92年6月。

4. Daniel, R, Dolk.〝Integrated model management in the data warehouse ers,〞European Journal of Operational Research. 122, pp. 199-218, 2000.

5. Duboff, R. S.,〝Marketing to Maximize Profitability,〞The Journal of Business Strategy, Vol.13, No.6, pp. 10-13,1992.

6. Fayyad, U. M. et al.,〝Advances in Knowledge Discovery and Data Mining ,〞AAAI Press/The MIT Press, Menlo Park, CA, 1996.

7. Fayyad, U. W.,〝Data Mining and Knowledge Discovery ：Making Sense Out of Data,,〞IEEE Expert, Vol.11 No.5, pp.22-23, October 1996.