客戶關係管理(CRM, Customer Relationship Management)在學術上已被廣泛地探討。先進科技發達的今天，雖然給企業帶來了很大的衝擊與競爭，卻也助長了CRM的應用。資料挖掘的技術在CRM扮演很重要的角色，最常見的例子是關聯性法則應用在零售業及電子商務，挖掘隱藏性的產品關聯作為CRM的行銷決策，如店內陳列、交叉銷售、客戶分群等等，證實有顯著的成效與報酬率。然而，以關係行銷為主的企業對企業B2B (Business-to-Business)產業在採用客戶關係管理的同時，因為商業模式的不同，應用資料挖掘技術在B2B CRM系統上卻不多，甚至有些經理人員認為觀念上是不可行的。本研究將透過理論文獻上的參考結合實務上的探討，針對個案公司目前在客戶關係管理的運作，進行診斷並提出建議。另外本研究嘗試性將資料挖掘技術應用在個案公司的交易資料庫，試圖以關聯性法則挖掘跨不同品類的產品關聯知識，作為個案公司推薦相關產品給同性質或來自同一區(有相同的文化)的客戶，並作為其樣品間的商品展示陳列參考。實驗結果是資料挖掘也是可以應用在CRM B2B的環境下，惟對文字或資料的處理與資料倉儲建置需要產業的知識與經驗來定義資料變數，再將挖掘結果整合併入行銷決策系統。
資料挖掘尚有更多的技術可以運用在CRM B2B，CRM的目標為收集訊息、分析客戶與產品知識，將有助訂定一對一行銷策略、客製化客服策略來增加客戶價值，提高客戶忠誠度，最終目標提高企業收益，創造競爭優勢。本篇論文強調CRM在貿易產業也很重要，資料挖掘更是可以作為CRM分析工具。在末章將提出管理上的意函與對未來的研究方向建議。

Customer Relationship Management (CRM) has been addressed with great interest among scholars and executives. The advanced technology brings tremendous impact and competition in nowadays business environment. At the same time, it facilitates the application of CRM. Data mining acts one of components to CRM system. Among many techniques, Association Rule is one of techniques to be evident effective with best return in retailing and e-commerce as marketing decision support. The new marketing strategy includes: in-store merchandising shelf display, cross-selling, customer segmentation, and supply chain management etc. Nevertheless, data mining approach in B2B (Business-to-Business) industry has been seen impossible by some managers in conception wise. Only a few CRM vendors act as a catalyst. Firstly, this research attempts to diagnose CRM in the case study with literature review and practical experience, moreover, to propose managerial implication and suggestion for the topic. Secondly, with data mining approach to its transaction database to find the relation between sub-sub categories for product recommendation and showroom merchandising display. According to the result of experiment, it proves applicable in CRM B2B. The concern raised shall be the information collection and data format that stored in the database. Then by giving variables and attributes with practical insights in the field to construct the database for better analysis. However, the case company is used to rely on personal judgment and seems lack of analytic tools for decision making system. The ultimate objective of CRM in B2B is to create customized marketing strategy to increase customer value, enhance customer royalty and satisfaction to gain business revenue with competitive advantages. This research aims to demonstrate a logical analysis for case study’s marketing strategy decision support. CRM is obviously necessary in trading industry, while an analytical tool is also vital to have a logical base for CRM strategy rather than personal bias and judgment when dealing with large amount of information and data. Managerial implication for the case company has been concluded in chapter five. Suggestions both for case company in aspect of Data Mining and future study will be proposed in the last chapter.

Table of Content
Acknowledgements	I
淡江大學研究生論文中文提要	II
Abstract:	IV
Abbreviations	VI
Table of Contents	VII
List of Figures	X
List of Tables	XII
Chapter 1: Introduction	1
1.1	Background & Motivation	1
1.2	Research Objective	3
1.3	Research Structure and Flow	4
1.4	Research Scope	7
Chapter 2: Literature Review	9
2.1	B2B characteristic and differences with B2C	9
2.2	Relationship marketing in B2B	11
2.3	Customer Relationship Management	13
2.3.1	CRM Definition	13
2.3.2	CRM Function	13
2.3.3	Perspective in CRM	16
2.3.4	CRM Strategy	18
2.4	Customer Knowledge	19
2.5	Customer Lifetime Value	21
2.6	CRM Technology	29
2.7	Data Mining	31
2.7.1	Data, Information, and Knowledge	33
2.7.2	Data Warehouses and Data Mining	34
2.7.3	Data Mining vs. CRM in B2B	36
Chapter 3: Diagnose CRM System in T Company	39
3.1	Methods	39
3.2	Trading business environment	40
3.3	Case Study	40
3.3.1	T Trading Company Brief	41
3.3.2	Management Philosophy	42
3.3.3	Household Products	43
3.3.4	Business Process	44
3.3.5	IT infrastructure relating CRM	46
3.3.6	Organization and Customer Service Department	49
3.3.7	Marketing Activity – Showroom visit	51
3.3.8	Sales & Communication Channel	51
3.4	Diagnose and Issues Identified	53
3.5	Data Mining System Design	54
3.5.1	Data Preparation	54
3.5.2	Relational Design	54
3.5.3	Physical Design	56
3.5.4	Conceptual Design	57
3.5.5	System Framework	59
3.5.6	Data mining Tool– SPSS Clementine	61
3.6	Data Mining Process	63
3.6.1	Association rules	63
3.6.2	Apriori Algorithm	65
Chapter 4: Data Mining Result	67
4.1	Description of the data structure	67
4.2	Product mix	69
4.2.1	Pattern A: Product Mix for Hard-Line Business Unit	69
4.2.2	Pattern B: Product Mix for Home Improvement Business Unit	71
4.3	Customer Type vs. Products	72
4.3.1	Pattern A1: Customer Type vs. Hard-Line Business Unit Sub-Sub Category	73
4.3.2	Pattern B1: Customer type vs. Home Improvement Business Unit sub-sub category	75
4.3.3	Pattern C: Customer type vs. Seasonal Business Unit sub-sub category	77
4.3.4	Pattern D: Customer type vs. Electrical & Lighting Business Unit sub-sub category	78
4.3.5	Pattern E: Customer type vs. SIT Business Unit sub-sub category	78
4.4	Communication Channel vs. Sub-Sub Category	79
4.4.1	Pattern A2: Communication Channel vs. Hard-Line Business Unit Sub-sub Category	79
4.4.2	Pattern B2: Communication Channel vs. Home Improvement Business Unit sub-sub category	81
4.4	Findings	82
Chapter 5: Managerial Implication	84
5.1	Data Warehouse in B2B CRM is Also Necessary	84
5.2	Data Mining Approach in CRM B2B is Feasible	86
5.3	CRM is a Continuous Project	87
5.4	IT is one of CRM Success factors	88
Chapter 6: Limitation and suggestion	90
6.1	Lessons learned	90
6.2	Research Limitation	91
6.3	Suggestions	91
6.3.1	For T Company	92
6.3.2	For future research	93
6.4	Conclusion	94
References	95
APPENDIX: Definition of Customer type	103

List of Figures
Figure 1.1 Flow chart of research	   6
Figure 2.1 CRM implementation model	   15
Figure 2.2 Integrated marketing knowledge management	   21
Figure 2.3 CRM applications, supported by ERP/data warehouse, link front and back office functions	   27
Figure 2.4 CRM IT operational model	   28
Figure 2.5 Data mining model	   33
Figure 2.6 From data to information to knowledge	   34
Figure 2.7 Data Mining Process	   38
Figure 3.1 T Trading Company – global trading	   42
Figure 3.2 Five business unit table in T Company	   44
Figure 3.3 Business Process in T Company	   45
Figure 3.4 IT supporting system in T Company	   48
Figure 3.5 Organization structure	   50
Figure 3.6 Database association chart	   56
Figure 3.7 E-R Model	   58
Figure 3.8 Data mining system framework	   60
Figure 3.9 Clementine’s workbench for this research (partial)	   62
Figure 3.10 Apriori Algorithm	   66
Figure 4.1 Sales weight % by country	   69
Figure 4.2 Web graph for Pattern A (after adjustment)	   71
Figure 4.3 Web graph of Pattern B (after adjustment)	   72
Figure 4.4 Web graph for Pattern A1 (after adjustment)	   75
Figure 4.5 Web graph for Pattern B1 (after adjustment)	   76
Figure 4.6 Web graph for Pattern C (after adjustment)	   77
Figure 4.7 Web graph for Pattern D (after adjustment)	   78
Figure 4.8 Web graph for Pattern E (before adjustment)	   79
Figure 4.9 Web graph for Pattern A2 (after adjustment)	   81
Figure 4.10 Web graph for Pattern B2 (after adjustment)	   82
Figure 5.1 Data Warehouse Architecture	   86

List of Tables
Table 2.1 Business characteristic comparison between B2B and B2C	   10
Table 2.2 Perspectives in CRM	   15
Table 3.1 Master databases stored in the system	   47
Table 3.2 Communication channel	   52
Table 3.3 Relational database field	   56
Table 3.4 Data mining tools used for a real project: Top 10 Rankings	   61
Table 4.1 Transaction record by business unit	   67
Table 4.2 Number of customer from different country	   68
Table 4.3 Association Rules for Pattern A	   70
Table 4.4 Association Rules for Patter B	   71
Table 4.5 Association Rule for Pattern A1	   74
Table 4.6 Association Rule for Pattern B1	   76
Table 4.7 Association Rule for Pattern C	   77
Table 4.8 Association Rule for Pattern A2	   80
Table 4.9 Association Rule for Pattern B2	   81

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