【レポートの概要（一部）】
TABLE OF CONTENTS
1 INTRODUCTION 15
1.1 OBJECTIVES OF THE STUDY 15
1.2 MARKET DEFINITION 15
1.3 MARKET SCOPE 16
1.3.1 YEARS CONSIDERED FOR THE STUDY 17
1.3.2 CURRENCY 17
1.4 STAKEHOLDERS 18
2 RESEARCH METHODOLOGY 19
2.1 RESEARCH DATA 20
2.1.1 SECONDARY DATA 20
2.1.2 PRIMARY DATA 20
2.1.2.1 Key industry insights 21
2.2 MARKET SIZE ESTIMATION 21
2.2.1 BOTTOM-UP APPROACH 22
2.2.2 TOP-DOWN APPROACH 22
2.3 RESEARCH ASSUMPTIONS 24
2.4 LIMITATIONS 24
3 EXECUTIVE SUMMARY 25
4 PREMIUM INSIGHTS 28
4.1 ATTRACTIVE MARKET OPPORTUNITIES IN THE ARTIFICIAL INTELLIGENCE IN RETAIL MARKET 28
4.2 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET: TECHNOLOGIES 28
4.3 LIFECYCLE ANALYSIS, BY REGION, 2017–2022 29
5 MARKET OVERVIEW AND INDUSTRY TRENDS 30
5.1 INTRODUCTION 30
5.2 MARKET DYNAMICS 30
5.2.1 DRIVERS 31
5.2.1.1 Increasing necessity for superior surveillance and monitoring at physically present retail stores 31
5.2.1.2 Growing awareness and application of AI in the retail industry 31
5.2.1.3 To enhance end-user experience, improve productivity, and generate more revenue 31
5.2.1.4 To maintain inventory accuracy and supply chain optimization 32
5.2.2 RESTRAINTS 32
5.2.2.1 Incompatibility concerns 32
5.2.3 OPPORTUNITIES 33
5.2.3.1 Increase in AI-based data analysis application 33
5.2.3.2 Growing number of smartphones 33
5.2.3.3 Increase in adoption of cloud-based technology solutions 33
5.2.4 CHALLENGES 34
5.2.4.1 Issues with diverse development framework, models, and mechanism in AI 34
5.2.4.2 Concerns over privacy and identity of individuals 34
5.2.4.3 Lack of skilled staff 35
5.3 INDUSTRY TRENDS 35
5.3.1 INTRODUCTION 35
5.3.2 USE CASES 35
5.3.2.1 Scenario 1 35
5.3.2.2 Scenario 2 36
5.3.2.3 Scenario 3 36
5.3.2.4 Scenario 4 36
5.3.2.5 Scenario 5 36
5.3.2.6 Scenario 6 36
5.3.2.7 Scenario 7 37
5.3.2.8 Scenario 8 37
5.3.2.9 Scenario 9 37
5.3.2.10 Scenario 10 37
5.3.2.11 Scenario 11 38
5.3.2.12 Scenario 12 38
5.4 REGULATORY IMPLICATIONS 38
5.4.1 INTRODUCTION 38
5.4.2 SARBANES-OXLEY ACT OF 2002 38
5.4.3 GENERAL DATA PROTECTION REGULATION 39
5.4.4 BASEL 39
6 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY TYPE 40
6.1 INTRODUCTION 41
6.2 ONLINE RETAIL 42
6.3 OFFLINE RETAIL 43
7 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY TECHNOLOGY 44
7.1 INTRODUCTION 45
7.2 MACHINE LEARNING AND DEEP LEARNING 46
7.2.1 FACIAL RECOGNITION 47
7.2.2 EMOTION DETECTION 47
7.3 NATURAL LANGUAGE PROCESSING 48
7.4 OTHERS 49
7.4.1 ANALYTICS 49
7.4.2 PROCESS AUTOMATION 49
8 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY SOLUTION 50
8.1 INTRODUCTION 51
8.2 PRODUCT RECOMMENDATION AND PLANNING 52
8.3 CUSTOMER RELATIONSHIP MANAGEMENT 53
8.4 VISUAL SEARCH 54
8.5 VIRTUAL ASSISTANT 55
8.6 PRICE OPTIMIZATION 56
8.7 PAYMENT SERVICES MANAGEMENT 56
8.8 SUPPLY CHAIN MANAGEMENT AND DEMAND PLANNING 57
8.9 OTHERS 58
9 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY SERVICE 60
9.1 INTRODUCTION 61
9.2 PROFESSIONAL SERVICES 62
9.3 MANAGED SERVICES 63
10 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS,
BY DEPLOYMENT MODE 64
10.1 INTRODUCTION 65
10.2 CLOUD 66
10.3 ON-PREMISES 67
11 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY APPLICATION 68
11.1 INTRODUCTION 69
11.2 PREDICTIVE MERCHANDISING 70
11.3 PROGRAMMATIC ADVERTISING 70
11.4 MARKET FORECASTING 71
11.5 IN-STORE VISUAL MONITORING AND SURVEILLANCE 72
11.6 LOCATION-BASED MARKETING 72
11.7 OTHERS 73
11.7.1 REAL-TIME PRICING AND INCENTIVES 73
11.7.2 REAL-TIME PRODUCT TARGETING 73
12 GEOGRAPHIC ANALYSIS 75
12.1 INTRODUCTION 76
12.2 NORTH AMERICA 77
12.2.1 NORTH AMERICA, BY TYPE 79
12.2.2 NORTH AMERICA, BY TECHNOLOGY 79
12.2.3 NORTH AMERICA, BY SOLUTION 80
12.2.4 NORTH AMERICA, BY SERVICE 80
12.2.5 NORTH AMERICA, BY DEPLOYMENT MODE 81
12.2.6 NORTH AMERICA, BY APPLICATION 81
12.3 EUROPE 82
12.3.1 EUROPE, BY TYPE 82
12.3.2 EUROPE, BY TECHNOLOGY 83
12.3.3 EUROPE, BY SOLUTION 83
12.3.4 EUROPE, BY SERVICE 84
12.3.5 EUROPE, BY DEPLOYMENT MODE 84
12.3.6 EUROPE, BY APPLICATION 85
12.4 ASIA PACIFIC 85
12.4.1 ASIA PACIFIC, BY TYPE 87
12.4.2 ASIA PACIFIC, BY TECHNOLOGY 87
12.4.3 ASIA PACIFIC, BY SOLUTION 88
12.4.4 ASIA PACIFIC, BY SERVICE 88
12.4.5 ASIA PACIFIC, BY DEPLOYMENT MODE 89
12.4.6 ASIA PACIFIC, BY APPLICATION 89
12.5 LATIN AMERICA 90
12.5.1 LATIN AMERICA, BY TYPE 90
12.5.2 LATIN AMERICA, BY TECHNOLOGY 90
12.5.3 LATIN AMERICA, BY SOLUTION 91
12.5.4 LATIN AMERICA, BY SERVICE 91
12.5.5 LATIN AMERICA, BY DEPLOYMENT MODE 92
12.5.6 LATIN AMERICA, BY APPLICATION 92
12.6 MIDDLE EAST AND AFRICA 93
12.6.1 MIDDLE EAST AND AFRICA, BY TYPE 93
12.6.2 MIDDLE EAST AND AFRICA, BY TECHNOLOGY 93
12.6.3 MIDDLE EAST AND AFRICA, BY SOLUTION 94
12.6.4 MIDDLE EAST AND AFRICA, BY SERVICE 94
12.6.5 MIDDLE EAST AND AFRICA, BY DEPLOYMENT MODE 95
12.6.6 MIDDLE EAST AND AFRICA, BY APPLICATION 95
13 COMPANY PROFILES 96
13.1 IBM 96
(Overview, Strength of Product Portfolio, Business Strategy Excellence, and Recent Developments)*
13.2 MICROSOFT 100
13.3 NVIDIA 103
13.4 AMAZON WEB SERVICES 107
13.5 ORACLE 110
13.6 SAP 113
13.7 INTEL 116
13.8 GOOGLE 119
13.9 SENTIENT TECHNOLOGIES 122
13.10 SALESFORCE 124
13.11 VISENZE 127
*Details on Overview, Strength of Product Portfolio, Business Strategy Excellence, and Recent Developments might not be captured in case of unlisted companies.
14 APPENDIX 129
14.1 INDUSTRY EXPERTS 129
14.2 DISCUSSION GUIDE 130
14.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 134
14.4 INTRODUCING RT: REAL-TIME MARKET INTELLIGENCE 136
14.5 RELATED REPORTS 137
14.6 AUTHOR DETAILS 138
【レポート販売概要】
■ タイトル：小売産業におけるAI（人工知能）の世界市場予測（～2022年）■ 英文：Artificial Intelligence in Retail Market by Type (Online, Offline), Technology (Machine Learning and Deep Learning, NLP), Solution, Service (Professional, Managed), Deployment Mode (Cloud, On-Premises), Application, Region - Global Forecast to 2022
■ 発行日：2017年10月24日
■ 調査会社：MarketsandMarkets
■ 商品コード：TC-5669
■ 調査対象地域：グローバル
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