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系統識別號 U0002-2006200715445000
中文論文名稱 資料探勘應用於資訊家電之一對一行銷之研究
英文論文名稱 The Research of Data Mining Techniques Applied To One to One Marketing of Information Appliances
校院名稱 淡江大學
系所名稱(中) 管理科學研究所碩士班
系所名稱(英) Graduate Institute of Management Science
學年度 95
學期 2
出版年 96
研究生中文姓名 蕭世忠
研究生英文姓名 Shih Chung Hsiao
學號 694560029
學位類別 碩士
語文別 英文
口試日期 2007-06-01
論文頁數 164頁
口試委員 指導教授-廖述賢
共同指導教授-劉艾華
委員-張克章
委員-黃振中
中文關鍵字 資料探勘  一對一行銷  資訊家電  關聯性資料庫  Apriori演算法  Two-step集群分析  CART決策樹 
英文關鍵字 Data mining  one to one marketing  information appliances  relational database  Apriori algorithmn  Two-step clustering analysis  Classification and Regression Trees 
學科別分類 學科別社會科學管理學
中文摘要 隨著近年來的資訊匯流發展趨勢和資訊家電的蓬勃發展,加上持續的網路科技爆炸,3C產業(電腦、通訊和電子產品)的產品已由以往的昂貴奢侈品變成了21世紀大眾日常生活的平價必需品。這項資訊革命伴隨著現今發熱的全球化透明市場效應不但大幅的提昇了3C產業內的競爭困難,且由於科技產品、新購買和付費方法、激烈的競爭和更多的行銷管道降低了廣告和大眾行銷法手的有效性,因此如今人們需要的是個人行銷,又稱為一對一行銷。
因此,在當下環境想達到雙贏局面的業者們必須找到方式使其產品和服務差異化,並且滿足顧客的需求和慾望,提昇顧客滿意度,進而提昇企業利潤。
此研究的目的為協助3C產業業者透過有效的顧客資料使用和分析下在目前高透明化及高競爭環境中達到該雙贏策略,進而提昇其競爭力。
本研究所建立的顧客資料庫透過數種資料探勘技術的應用,把大量的顧客資料庫作處理,分析,理解和視覺化,使其從無意義的資料轉換為企業有價值的寶貴知識。本研究所使用的資料採礦分析方法包含了Apriori演算法、Two-step集群分析和CART決策樹。
透過所產出之分析,以顧客為導向,擬定策略、行銷手法和新產品開發之建議,並提出對業者之管理意涵及研究結論。
英文摘要 As information technologies converges and information devices become more powerful, along with the continuing network technology’s boom, the 3C (Computer, Communication, and Consumer electronics) industry’s products have became from a luxurious product to an essential product of daily needs for most people in the 21st century. This digital revolution accompanied with today’s increasingly globalized market have not only raised the competition difficulty within the 3C industry, but also lowered the effectiveness of the traditional mass marketing due to changing households, complex technology-based products, new ways to shop and pay, intense competition, additional channels, and declining advertising effectiveness. Personal marketing is what the customers want nowadays.
Accordingly, practitioners who want to reach a win-win strategy under such conditions must somehow distinguish their products and services from those of their competitors, and increase customers’ satisfaction by fulfilling their needs and preferences, thus obtaining higher loyalties and profits.
The purpose of this research was to assist marketers of 3C industry at improving their competitiveness by reaching such win-win strategy in today’s highly competitive markets through effective utilization of customers’ data. This research has built a relational database and utilized various data mining techniques to help analyze, understand, and visualize the huge amounts of stored data about customers. Valuable information, knowledge patterns and rules have been extracted, analyzed, and interpreted from customers’ database by using Apriori algorithmn, Two-step clustering analysis, and CART (Classification and Regression Trees) as needed.
Consequently, reports, conclusions, and map of marketing have been drawn at the end suggesting to the marketers the need to obtain customer knowledge and feedback from the demand side and use them as a knowledge resource for establishing suitable one to one marketing offers, mix of products, and future product developments.
論文目次 TABLE OF CONTENTS
致謝辭 I
淡江大學研究生中文論文提要 II
ABSTRACT III
TABLE OF CONTENTS VI
LIST OF FIGURES IX
LIST OF TABLES XI
CHAPTER 1: INTRODUCTION 1
1.1 RESEARCH BACKGROUND AND MOTIVATION 1
1.2 RESEARCH OBJECTIVES 5
1.3 THESIS ORGANIZATION 5
CHAPTER 2: LITERATURE REVIEW 7
2.1 DIGITAL INTEGRATION AND DIGITAL CONVERGENCE 7
2.1.1 Introduction 7
2.1.2 Definition 7
2.1.3 Characteristics 10
2.2 INFORMATION APPLIANCE 11
2.2.1 Introduction 11
2.2.2 Definition 12
2.2.3 Characteristics and potential drawbacks 14
2.2.4 Classification 15
2.3 DIGITAL CONTENT 15
2.4 ONE TO ONE MARKETING 18
2.4.1 Introduction 18
2.4.2 One to one Marketing Concept 19
2.4.3 Required steps to implement a one to one marketing program 22
2.5 DATA MINING 27
2.5.1 Introduction 27
2.5.2 Definition 28
2.5.3 Functions and methods 31
2.5.4 Steps of DM process 33
2.5.5 Application of DM 34
2.6 CHAPTER CONCLUSION 35
CHAPTER 3: RESEARCH METHODOLOGIES 38
3.1 RESEARCH FRAMEWORK 38
3.2 ONTOLOGY DESIGN 40
3.2.1 Introduction and definition 40
3.2.2 Ontology types and building steps 42
3.2.3 Ontology design tools 44
3.2.4 Output format and languages 45
3.2.5 Ontology Design 47
3.3 RESEARCH DESIGN 53
3.3.1 Introduction 53
3.3.2 Questionnaire Design 54
3.3.3 Validity concerns 55
3.3.4 Reliability concerns 56
3.3.5 Sampling design 56
3.3.6 Sampling plan 58
3.4 RELATIONAL DATABASE DESIGN 59
3.4.1 Introduction to database system design 59
3.4.2 Conceptual design 60
3.4.3 Logical design (data model mapping) 64
3.4.4 Physical design 65
3.5 DATA ANALYSIS METHODS 66
3.5.1 Association rules analysis 67
3.5.2 Apriori algorithm 68
3.5.3 Clustering analysis 70
3.5.4 Classification and Regression Trees (CART) 72
3.6 SYSTEM FRAMEWORK 73
3.7 DATA ANALYSIS TOOLS: SPSS CLEMENTINE 74
CHAPTER 4: RESEARCH RESULTS 79
4.1 DESCRIPTION OF THE SAMPLE STRUCTURE 79
4.2 PRODUCT MIX 82
4.2.1 Pattern A: Devices in conjunction 82
4.3 PRODUCT KNOWLEDGE 85
4.3.1 Pattern B: Cell Phone 85
4.3.2 Pattern C: Notebook 90
4.3.3 Pattern D: PDA 95
4.3.4 Pattern E: Desktop PC 98
4.3.5 Pattern F: Set top box 102
4.3.6 Pattern G: Console game 105
4.4 PRODUCT DEVELOPMENT 110
4.4.1 Pattern H: PDA and All Contents 110
4.4.2 Pattern I: Set Top Box and All Contents 112
4.4.3 Pattern J: Console Game and All Contents 114
CHAPTER 5: MANAGERIAL IMPLICATIONS 117
5.1 PRODUCT MIX 117
5.2 PRODUCT KNOWLEDGE 118
5.3 PRODUCT DEVELOPMENT 121
CHAPTER 6: CONCLUSIONS AND SUGGESTIONS 124
6.1 RESEARCH LIMITATIONS 124
6.2 RESEARCH CONCLUSIONS 124
6.2.1 Product Mix 125
6.2.2 Product knowledge 126
6.2.3 Product development 127
6.3 FUTURE RESEARCH 128
BIBLIOGRAPHY 129
APPENDIX 141
APPENDIX I: ONTOLOGIES 141
APPENDIX II: QUESTIONNAIRE (DRAFT VERSION) 145
APPENDIX III: QUESTIONNAIRE (FINAL VERSION) 153
APPENDIX IV: TABLE OF TEST-RETEST’S RECORDS 157
APPENDIX V: REPORTS FOR DECISION SUPPORT 159
APPENDIX VI: CLEM EXPRESSIONS 163

LIST OF FIGURES
Figure 1.1. Taiwan’s internet users and rate of access. 2
Figure 1.2. Thesis organization 6
Figure 2.1. Main six areas involved in digital convergence 11
Figure 2.2. Direction of success 21
Figure 2.3. Typical KDD process 28
Figure 2.4. Data mining system components 29
Figure 3.1. Research framework 39
Figure 3.2. The stack of ontology markup languages 47
Figure 3.3. Ontology of user demographics. 49
Figure 3.4. Ontology of information devices. 50
Figure 3.5. Ontology of consumer behavior. 51
Figure 3.6. Complete ontology. 52
Figure 3.7. Screenview of Protégé environment. 53
Figure 3.8. Entity-relationship Model 63
Figure 3.9. Logical database model design 64
Figure 3.10. Physical database 66
Figure 3.11. Partition clustering 71
Figure 3.12. An example of dendrogram produced by hierarchical clustering 72
Figure 3.13. Data mining system framework 74
Figure 3.14. Clementine’s workbench for this research (Partial) 77
Figure 4.1. Web graph for pattern A 84
Figure 4.2. Map of marketing for product mix 84
Figure 4.3. CART tree graph for pattern B 86
Figure 4.4. Web graph for pattern B (Before adjustment) 88
Figure 4.5. Web graph for pattern B (After adjustment). 88
Figure 4.6. Web graph for pattern C (Before adjustment) 91
Figure 4.7. Web graph for pattern C (After adjustment) 92
Figure 4.8. Clustering for pattern C (Partial). 94
Figure 4.9. Web graph for pattern D (Before adjustment) 96
Figure 4.10. Web graph for pattern D (After adjustment) 96
Figure 4.11. CART tree for pattern E 98
Figure 4.12. Web graph for pattern E (Before adjustment) 100
Figure 4.13. Web graph for pattern E (After adjustment) 100
Figure 4.14. Web graph for pattern F (Before adjustment) 103
Figure 4.15. Web graph for pattern F (After adjustment) 103
Figure 4.16. Web graph for pattern G (Before adjustment) 106
Figure 4.17. Web graph for pattern G (After adjustment) 107
Figure 4.18. Map of marketing for product knowledge 109
Figure 4.19. Web graph for pattern H (Before adjustment) 111
Figure 4.20. Web graph for pattern H (After adjustment) 111
Figure 4.21. Web graph for pattern I (Before adjustment) 113
Figure 4.22. Web graph for pattern I (After adjustment) 113
Figure 4.23. Web graph for pattern J (Before adjustment) 115
Figure 4.24. Web graph for pattern J (After adjustment) 115
Figure 4.25. Map of marketing for product development 116
Figure 5.1. Report for one-to-one marketers (Rule RA1 with no STB) 117
Figure 5.2. Report for one-to-one marketers (Rule RA1) 118
Figure 5.3. Report for one-to-one marketers (Rule RB2) 119
Figure 5.4. Report for one-to-one marketers (Rule RC1 with no notebook) 121
Figure 6.1. Conclusions for product mix 126
Figure 6.2. Conclusions for product knowledge 127
Figure 6.3. Conclusions for product development 128

LIST OF TABLES
TABLE 1.1. MARKET SIZE OF SMARTPHONE, PDA, AND HANDHELD COMPUTER. 3
TABLE 2.1. DEFINITIONS OF DIGITAL CONVERGENCE 8
TABLE 2.2. DEFINITIONS OF INFORMATION APPLIANCES 13
TABLE 2.3. CLASSIFICATION OF INFORMATION APPLIANCES 15
TABLE 2.4. DEFINITION OF DIGITAL CONTENT 17
TABLE 2.5. COMPARISON BETWEEN TRADITIONAL MASS MARKETING AND 1:1 MARKETING. 21
TABLE 2.6. STEPS INVOLVED IN THE IMPLEMENTATION OF 1:1 MARKETING – IDENTIFY 22
TABLE 2.7. STEPS INVOLVED IN THE IMPLEMENTATION OF 1:1 MARKETING – DIFFERENTIATE. 23
TABLE 2.8. STEPS INVOLVED IN THE IMPLEMENTATION OF 1:1 MARKETING – INTERACT. 24
TABLE 2.9. STEPS INVOLVED IN THE IMPLEMENTATION OF 1:1 MARKETING – CUSTOMIZE. 26
TABLE 2.10. DEFINITIONS OF DATA MINING 30
TABLE 2.11. RANKING OF DM METHODS THAT PEOPLE USED FREQUENTLY IN THE LAST YEAR. 32
TABLE 2.12. PROPOSALS OF DM STEPS. 33
TABLE 2.13. RANKING OF THE FIELDS WHERE PEOPLE HAVE APPLIED DATA MINING. 35
TABLE 3.1. DEFINITION OF ONTOLOGY 41
TABLE 3.2. SAMPLE DESIGN TYPE - CHOICE CONSIDERATIONS. 57
TABLE 3.3. CONCEPTS OF ENTITY TYPES AND ATTRIBUTES 61
TABLE 3.4. LISTS OF ENTITIES AND ATTRIBUTES OF THE ER MODEL 61
TABLE 3.5. SUMMARY OF KEY CHARACTERISTICS FOR EACH SOFTWARE PACKAGE. 75
TABLE 3.6. RANKING OF DM TOOLS PEOPLE USED IN 2006 76
TABLE 4.1. SAMPLES 79
TABLE 4.2. GENDER, AGE AND MARITAL STATUS. 79
TABLE 4.3. EDUCATION LEVEL AND INCOME LEVEL. 80
TABLE 4.4. AREA AND TOP 5 JOBS. 81
TABLE 4.5. DAILY USAGE TIME OF INTERNET. 81
TABLE 4.6. OWNED DEVICES 82
TABLE 4.7. ASSOCIATION RULES FOR PATTERN A 83
TABLE 4.8. ASSOCIATION RULES FOR PATTERN B 86
TABLE 4.9. CLUSTERING FOR PATTERN B. 89
TABLE 4.10. ASSOCIATION RULES FOR PATTERN C 90
TABLE 4.11. CLUSTERING FOR PATTERN C. 92
TABLE 4.12. ASSOCIATION RULES FOR PATTERN D 95
TABLE 4.13. CLUSTERING FOR PATTERN D. 97
TABLE 4.14. ASSOCIATION RULES FOR PATTERN E 99
TABLE 4.15. CLUSTERING FOR PATTERN E. 101
TABLE 4.16. ASSOCIATION RULES FOR PATTERN F. 102
TABLE 4.17. CLUSTERING FOR PATTERN F. 104
TABLE 4.18. ASSOCIATION RULES FOR PATTERN G 105
TABLE 4.19. CLUSTERING FOR PATTERN G. 107
TABLE 4.20. ASSOCIATION RULES FOR PATTERN H 110
TABLE 4.21. ASSOCIATION RULES FOR PATTERN I 112
TABLE 4.22. ASSOCIATION RULES FOR PATTERN J. 114
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