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System No. U0002-2006200715445000
Title (in Chinese) 資料探勘應用於資訊家電之一對一行銷之研究
Title (in English) The Research of Data Mining Techniques Applied To One to One Marketing of Information Appliances
Other Title
Institution 淡江大學
Department (in Chinese) 管理科學研究所碩士班
Department (in English) Graduate Institute of Management Science
Other Division
Other Division Name
Other Department/Institution
Academic Year 95
Semester 2
PublicationYear 96
Author's name (in Chinese) 蕭世忠
Author's name(in English) Shih Chung Hsiao
Student ID 694560029
Degree 碩士
Language English
Other Language
Date of Oral Defense 2007-06-01
Pagination 164page
Committee Member advisor - Shu-Hsien Liao
co-advisor - Ay-Hwa Liou
co-chair - Ker-Chang Chang
co-chair - Chen-chung Huang
Keyword (inChinese) 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
Keyword (in English) Data mining
one to one marketing
information appliances
relational database
Apriori algorithmn
Two-step clustering analysis
Classification and Regression Trees
Other Keywords
Subject
Abstract (in Chinese)
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。
Abstract (in English)
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.
Other Abstract
Table of Content (with Page Number)
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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