臺灣博碩士論文加值系統

在現今激烈的商業環境中比競爭對手更快掌握顧客的需求是企業獲利的關鍵。企業為了瞭解客戶的需求以及消費模式，常利用資料探勘(Data mining)中的關聯法則(Association rules)去分析出客戶的購買習慣，也就是俗稱的購物籃分析(Market-basket analysis)。利用購物籃分析我們可以了解該在何時對何種顧客進行何種促銷計畫，進而發展出更周全的客戶關係管理機制(Customer relationship management)。
　　以往的購物籃分析方法，或許能準確地分辨客戶的屬性，但是在顧客對於企業的貢獻程度方面則較無著墨。本論文則是藉由分析VIP顧客的購物行為進而發現關鍵商品。藉此關鍵商品預測為來購買此商品的客戶是否為VIP顧客。
　　本論文實驗的結果總共找到四項關鍵商品。每項關鍵商品在驗證資料中對VIP顧客的區別比率皆在60%以上，其中兩項關鍵商品更可以高達70%以上。 意即購買到關鍵商品的顧客，有60%以上的機率是我們欲開發的VIP顧客。因為本論文研究僅需要可辨識的會員編號，所以對於僅有會員編號的匿名資料探勘方法亦有相當的貢獻。

In today's competitive business environment faster than competitors to grasp the needs of customers is the key to create corporate profits. In order to understand customers’ needs and consumption patterns, enterprises usually using Data Mining and association rules to analyze customers' buying habits, also known as market basket analysis. We can use market basket analysis to understand what the customers' want and carry on propriety promotion plans, furthermore we can develop a customer relationship management strategy.
Ordinary thesis about basket analysis may be able to accurately identify the customer's property, but the contribution of the customer for the enterprise is no mention.
By using the aforementioned method In this paper, we total find a of four key commodities. Each key products in the testing data for VIP customers distinction ratios are more than 60%, of which two key products but also up to 70% or more. This means that customers buy key products, which are more than 60% probabilities who we want to develop a VIP customer. Because this thesis requires only recognizable membership number, so the only member number anonymous data mining methods are also considerable contributions.

+中文摘要 i
中文摘要 ii
目錄 iii
圖目錄 v
表目錄 vi

第一章　緒論 1
1.1　研究背景 1
1.2　研究動機 1
1.3　研究目的 2
1.4　研究範圍與限制 2
第二章　文獻探討 4
2.1　顧客關係管理(CRM) 4
2.1.1　顧客關係管理之起源 4
2.1.2　顧客關係管理之定義 6
2.1.3　顧客關係管理之流程 7
2.2　資料探勘（Data Mining） 8
2.3　RFM分析法 11
第三章　研究方法 13
3.1　研究架構 13
3.2　篩選原始資料 14
3.3　區分顧客貢獻度 15
3.4　商品出現比率 15
3.5　找出關鍵商品 16
3.6　驗證實驗成功率 17
第四章　研究分析與結果 19
4.1　資料描述與前置處理 19
4.1.1　資料描述 19
4.1.2　資料前置處理 19
4.2　計算商品出現比率 21
4.3　找出關鍵商品 22
4.4　驗證資料 23
4.5　研究討論 24
第五章　結論與未來研究建議 26
5.1　結論 26
5.2　未來研究建議 27

參考文獻 28
　　英文文獻 28
　　中文文獻 29

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