||Data Mining Approach in Customer Relationship Management: Case Study of a Taiwanese Household Exporter
||Executive Master's Program of Business Administration (EMBA) in International Commerce
Customer Relationship Management
||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。
||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
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
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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