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
“Need to offer seamless user-experience to customer and forecast future outcomes to make better strategic decision is expected to propel the AI in retail market growth”
The global AI in retail market size is expected to grow from USD 993.6 million in 2017 to USD 5,034.0 million by 2022, at a Compound Annual Growth Rate (CAGR) of 38.3%. Increasing necessity for superior surveillance and monitoring at a physical store, growing awareness and application of AI in the retail industry, enhanced user-experience, improved productivity, Return on Investment (RoI), mainlining inventory accuracy, and supply chain optimization are some of the key factors fueling the growth of this market. Emergence of machine learning, deep learning, and Natural Language Processing (NLP) technology are expected to develop the AI-based solution for retail and thus, will create opportunities for the growth of this market. However, issues with diverse development framework, models, mechanism in AI; concern over privacy and identity of the individual; and lack of skilled staff are few major challenges in the AI in retail market.
“Predictive merchandising application is expected to hold the largest market size during the forecast period”
The predictive merchandising application has numerous added benefits resulting in one of the highest rated application in the retail industry. It is also known as personalized product recommendations or automated merchandising. This application is beneficial for both eCommerce and stores for optimizing purchase, provide allocation, and product assortment. Therefore, it is the most sought-after application of AI retail solution that will generate the highest revenue in the market as compared to other applications.
“North America is expected to have the largest market size during the forecast period”
Among regions, North America is the highest contributor in the adoption and implementation of AI in retail. The region, including the US and Canada, has shown increased investments in the market, and several vendors have evolved to cater to the rapidly growing market. In the present-day situation, diverse organizations in the retail and eCommerce in North America are extensively implementing AI solutions. Moreover, many retailers in the region are technically advanced and are evolving to increase revenue and sales at the same time to decrease operational expenses. IBM, Google, Microsoft, NVIDIA, Intel, and Amazon Web Services are some of the companies that provide AI in retail products and services in North America, contributing to the highest revenue generated by the region.
In the process of determining and verifying the market size for several segments and subsegments gathered through secondary research, extensive primary interviews were conducted with the key people. The break-up of the profiles of the primary participants is given below:
1. IBM (US)
2. Microsoft (US)
3. Amazon Web Services (US)
4. Oracle (US)
5. SAP (Germany)
6. Intel (US)
7. NVIDIA (US)
8. Google (US)
9. Sentient technologies (US)
10. Salesforce (US)
11. ViSenze (Singapore)
Research Coverage
The report is majorly segmented into types, technologies, solutions, services, deployment modes, applications, and region. Further, AI in retail market based on type includes online (eCommerce) and offline (brick-and-mortar store) retail. Technology segment is sub-segmented into machine learning and deep learning, NLP, and others which include analytics and process automation. Solution segment in the report comprises product recommendation and planning, customer relationship management, visual search, virtual assistant, price optimization, payment services management, supply chain management and demand planning, and others which include website and content optimization, space planning, fraud detection, and franchise management. Professional services and managed services are segmented under services segment. Further, deployment mode includes cloud and on-premise deployment, whereas application segment includes predictive merchandising, programmatic advertising, market forecasting, in-store visual monitoring and surveillance, location-based marketing, and others (real-time pricing and incentives, and real-time product targeting). The regions are segmented into North America, Europe, APAC, Latin America, and Middle East and Africa (MEA).
Reasons to buy the report
The report will help the market leaders/new entrants in this market in the following ways:
1. The report segments the market into various subsegments, hence it covers the market comprehensively. It provides the closest approximations of the revenue numbers for the overall market and the subsegments. The market numbers are further split across different regions.
2. The report helps stakeholders to understand the pulse of the market and provides them with information on the key market drivers, restraints, challenges, and opportunities.
3. This report will help stakeholders to better understand the competitors and gain more insights to enhance their position in the business. The competitive landscape section includes new product launches/developments; partnerships and collaborations; mergers and acquisitions; and expansions.
The global AI in retail market size is expected to grow from USD 993.6 million in 2017 to USD 5,034.0 million by 2022, at a Compound Annual Growth Rate (CAGR) of 38.3%. Increasing necessity for superior surveillance and monitoring at a physical store, growing awareness and application of AI in the retail industry, enhanced user-experience, improved productivity, Return on Investment (RoI), mainlining inventory accuracy, and supply chain optimization are some of the key factors fueling the growth of this market. Emergence of machine learning, deep learning, and Natural Language Processing (NLP) technology are expected to develop the AI-based solution for retail and thus, will create opportunities for the growth of this market. However, issues with diverse development framework, models, mechanism in AI; concern over privacy and identity of the individual; and lack of skilled staff are few major challenges in the AI in retail market.
“Predictive merchandising application is expected to hold the largest market size during the forecast period”
The predictive merchandising application has numerous added benefits resulting in one of the highest rated application in the retail industry. It is also known as personalized product recommendations or automated merchandising. This application is beneficial for both eCommerce and stores for optimizing purchase, provide allocation, and product assortment. Therefore, it is the most sought-after application of AI retail solution that will generate the highest revenue in the market as compared to other applications.
“North America is expected to have the largest market size during the forecast period”
Among regions, North America is the highest contributor in the adoption and implementation of AI in retail. The region, including the US and Canada, has shown increased investments in the market, and several vendors have evolved to cater to the rapidly growing market. In the present-day situation, diverse organizations in the retail and eCommerce in North America are extensively implementing AI solutions. Moreover, many retailers in the region are technically advanced and are evolving to increase revenue and sales at the same time to decrease operational expenses. IBM, Google, Microsoft, NVIDIA, Intel, and Amazon Web Services are some of the companies that provide AI in retail products and services in North America, contributing to the highest revenue generated by the region.
In the process of determining and verifying the market size for several segments and subsegments gathered through secondary research, extensive primary interviews were conducted with the key people. The break-up of the profiles of the primary participants is given below:
- By Company: Tier 1: 18%, Tier 2: 48%, and Tier 3: 34%
- By Designation: C-level: 22%, Director level: 43%, and Others: 35%
- By Region: North America: 23%, Europe: 48%, APAC: 16%, and MEA: 13%
1. IBM (US)
2. Microsoft (US)
3. Amazon Web Services (US)
4. Oracle (US)
5. SAP (Germany)
6. Intel (US)
7. NVIDIA (US)
8. Google (US)
9. Sentient technologies (US)
10. Salesforce (US)
11. ViSenze (Singapore)
Research Coverage
The report is majorly segmented into types, technologies, solutions, services, deployment modes, applications, and region. Further, AI in retail market based on type includes online (eCommerce) and offline (brick-and-mortar store) retail. Technology segment is sub-segmented into machine learning and deep learning, NLP, and others which include analytics and process automation. Solution segment in the report comprises product recommendation and planning, customer relationship management, visual search, virtual assistant, price optimization, payment services management, supply chain management and demand planning, and others which include website and content optimization, space planning, fraud detection, and franchise management. Professional services and managed services are segmented under services segment. Further, deployment mode includes cloud and on-premise deployment, whereas application segment includes predictive merchandising, programmatic advertising, market forecasting, in-store visual monitoring and surveillance, location-based marketing, and others (real-time pricing and incentives, and real-time product targeting). The regions are segmented into North America, Europe, APAC, Latin America, and Middle East and Africa (MEA).
Reasons to buy the report
The report will help the market leaders/new entrants in this market in the following ways:
1. The report segments the market into various subsegments, hence it covers the market comprehensively. It provides the closest approximations of the revenue numbers for the overall market and the subsegments. The market numbers are further split across different regions.
2. The report helps stakeholders to understand the pulse of the market and provides them with information on the key market drivers, restraints, challenges, and opportunities.
3. This report will help stakeholders to better understand the competitors and gain more insights to enhance their position in the business. The competitive landscape section includes new product launches/developments; partnerships and collaborations; mergers and acquisitions; and expansions.
1 INTRODUCTION
1.1 OBJECTIVES OF THE STUDY
1.2 MARKET DEFINITION
1.3 MARKET SCOPE
1.3.1 YEARS CONSIDERED FOR THE STUDY
1.3.2 CURRENCY
1.4 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Key industry insights
2.2 MARKET SIZE ESTIMATION
2.2.1 BOTTOM-UP APPROACH
2.2.2 TOP-DOWN APPROACH
2.3 RESEARCH ASSUMPTIONS
2.4 LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE MARKET OPPORTUNITIES IN THE ARTIFICIAL INTELLIGENCE IN RETAIL MARKET
4.2 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET: TECHNOLOGIES
4.3 LIFECYCLE ANALYSIS, BY REGION, 2017–2022
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Increasing necessity for superior surveillance and monitoring at physically present retail stores
5.2.1.2 Growing awareness and application of AI in the retail industry
5.2.1.3 To enhance end-user experience, improve productivity, and generate more revenue
5.2.1.4 To maintain inventory accuracy and supply chain optimization
5.2.2 RESTRAINTS
5.2.2.1 Incompatibility concerns
5.2.3 OPPORTUNITIES
5.2.3.1 Increase in AI-based data analysis application
5.2.3.2 Growing number of smartphones
5.2.3.3 Increase in adoption of cloud-based technology solutions
5.2.4 CHALLENGES
5.2.4.1 Issues with diverse development framework, models, and mechanism in AI
5.2.4.2 Concerns over privacy and identity of individuals
5.2.4.3 Lack of skilled staff
5.3 INDUSTRY TRENDS
5.3.1 INTRODUCTION
5.3.2 USE CASES
5.3.2.1 Scenario 1
5.3.2.2 Scenario 2
5.3.2.3 Scenario 3
5.3.2.4 Scenario 4
5.3.2.5 Scenario 5
5.3.2.6 Scenario 6
5.3.2.7 Scenario 7
5.3.2.8 Scenario 8
5.3.2.9 Scenario 9
5.3.2.10 Scenario 10
5.3.2.11 Scenario 11
5.3.2.12 Scenario 12
5.4 REGULATORY IMPLICATIONS
5.4.1 INTRODUCTION
5.4.2 SARBANES-OXLEY ACT OF 2002
5.4.3 GENERAL DATA PROTECTION REGULATION
5.4.4 BASEL
6 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY TYPE
6.1 INTRODUCTION
6.2 ONLINE RETAIL
6.3 OFFLINE RETAIL
7 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY TECHNOLOGY
7.1 INTRODUCTION
7.2 MACHINE LEARNING AND DEEP LEARNING
7.2.1 FACIAL RECOGNITION
7.2.2 EMOTION DETECTION
7.3 NATURAL LANGUAGE PROCESSING
7.4 OTHERS
7.4.1 ANALYTICS
7.4.2 PROCESS AUTOMATION
8 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY SOLUTION
8.1 INTRODUCTION
8.2 PRODUCT RECOMMENDATION AND PLANNING
8.3 CUSTOMER RELATIONSHIP MANAGEMENT
8.4 VISUAL SEARCH
8.5 VIRTUAL ASSISTANT
8.6 PRICE OPTIMIZATION
8.7 PAYMENT SERVICES MANAGEMENT
8.8 SUPPLY CHAIN MANAGEMENT AND DEMAND PLANNING
8.9 OTHERS
9 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY SERVICE
9.1 INTRODUCTION
9.2 PROFESSIONAL SERVICES
9.3 MANAGED SERVICES
10 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY DEPLOYMENT MODE
10.1 INTRODUCTION
10.2 CLOUD
10.3 ON-PREMISES
11 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY APPLICATION
11.1 INTRODUCTION
11.2 PREDICTIVE MERCHANDISING
11.3 PROGRAMMATIC ADVERTISING
11.4 MARKET FORECASTING
11.5 IN-STORE VISUAL MONITORING AND SURVEILLANCE
11.6 LOCATION-BASED MARKETING
11.7 OTHERS
11.7.1 REAL-TIME PRICING AND INCENTIVES
11.7.2 REAL-TIME PRODUCT TARGETING
12 GEOGRAPHIC ANALYSIS
12.1 INTRODUCTION
12.2 NORTH AMERICA
12.2.1 NORTH AMERICA, BY TYPE
12.2.2 NORTH AMERICA, BY TECHNOLOGY
12.2.3 NORTH AMERICA, BY SOLUTION
12.2.4 NORTH AMERICA, BY SERVICE
12.2.5 NORTH AMERICA, BY DEPLOYMENT MODE
12.2.6 NORTH AMERICA, BY APPLICATION
12.3 EUROPE
12.3.1 EUROPE, BY TYPE
12.3.2 EUROPE, BY TECHNOLOGY
12.3.3 EUROPE, BY SOLUTION
12.3.4 EUROPE, BY SERVICE
12.3.5 EUROPE, BY DEPLOYMENT MODE
12.3.6 EUROPE, BY APPLICATION
12.4 ASIA PACIFIC
12.4.1 ASIA PACIFIC, BY TYPE
12.4.2 ASIA PACIFIC, BY TECHNOLOGY
12.4.3 ASIA PACIFIC, BY SOLUTION
12.4.4 ASIA PACIFIC, BY SERVICE
12.4.5 ASIA PACIFIC, BY DEPLOYMENT MODE
12.4.6 ASIA PACIFIC, BY APPLICATION
12.5 LATIN AMERICA
12.5.1 LATIN AMERICA, BY TYPE
12.5.2 LATIN AMERICA, BY TECHNOLOGY
12.5.3 LATIN AMERICA, BY SOLUTION
12.5.4 LATIN AMERICA, BY SERVICE
12.5.5 LATIN AMERICA, BY DEPLOYMENT MODE
12.5.6 LATIN AMERICA, BY APPLICATION
12.6 MIDDLE EAST AND AFRICA
12.6.1 MIDDLE EAST AND AFRICA, BY TYPE
12.6.2 MIDDLE EAST AND AFRICA, BY TECHNOLOGY
12.6.3 MIDDLE EAST AND AFRICA, BY SOLUTION
12.6.4 MIDDLE EAST AND AFRICA, BY SERVICE
12.6.5 MIDDLE EAST AND AFRICA, BY DEPLOYMENT MODE
12.6.6 MIDDLE EAST AND AFRICA, BY APPLICATION
13 COMPANY PROFILES
13.1 IBM
(Overview, Strength of Product Portfolio, Business Strategy Excellence, and Recent Developments)*
13.2 MICROSOFT
13.3 NVIDIA
13.4 AMAZON WEB SERVICES
13.5 ORACLE
13.6 SAP
13.7 INTEL
13.8 GOOGLE
13.9 SENTIENT TECHNOLOGIES
13.10 SALESFORCE
13.11 VISENZE
*Details on Overview, Strength of Product Portfolio, Business Strategy Excellence, and Recent Developments might not be captured in case of unlisted companies.
14 APPENDIX
14.1 INDUSTRY EXPERTS
14.2 DISCUSSION GUIDE
14.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.4 INTRODUCING RT: REAL-TIME MARKET INTELLIGENCE
14.5 RELATED REPORTS
14.6 AUTHOR DETAILS
1.1 OBJECTIVES OF THE STUDY
1.2 MARKET DEFINITION
1.3 MARKET SCOPE
1.3.1 YEARS CONSIDERED FOR THE STUDY
1.3.2 CURRENCY
1.4 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Key industry insights
2.2 MARKET SIZE ESTIMATION
2.2.1 BOTTOM-UP APPROACH
2.2.2 TOP-DOWN APPROACH
2.3 RESEARCH ASSUMPTIONS
2.4 LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE MARKET OPPORTUNITIES IN THE ARTIFICIAL INTELLIGENCE IN RETAIL MARKET
4.2 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET: TECHNOLOGIES
4.3 LIFECYCLE ANALYSIS, BY REGION, 2017–2022
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Increasing necessity for superior surveillance and monitoring at physically present retail stores
5.2.1.2 Growing awareness and application of AI in the retail industry
5.2.1.3 To enhance end-user experience, improve productivity, and generate more revenue
5.2.1.4 To maintain inventory accuracy and supply chain optimization
5.2.2 RESTRAINTS
5.2.2.1 Incompatibility concerns
5.2.3 OPPORTUNITIES
5.2.3.1 Increase in AI-based data analysis application
5.2.3.2 Growing number of smartphones
5.2.3.3 Increase in adoption of cloud-based technology solutions
5.2.4 CHALLENGES
5.2.4.1 Issues with diverse development framework, models, and mechanism in AI
5.2.4.2 Concerns over privacy and identity of individuals
5.2.4.3 Lack of skilled staff
5.3 INDUSTRY TRENDS
5.3.1 INTRODUCTION
5.3.2 USE CASES
5.3.2.1 Scenario 1
5.3.2.2 Scenario 2
5.3.2.3 Scenario 3
5.3.2.4 Scenario 4
5.3.2.5 Scenario 5
5.3.2.6 Scenario 6
5.3.2.7 Scenario 7
5.3.2.8 Scenario 8
5.3.2.9 Scenario 9
5.3.2.10 Scenario 10
5.3.2.11 Scenario 11
5.3.2.12 Scenario 12
5.4 REGULATORY IMPLICATIONS
5.4.1 INTRODUCTION
5.4.2 SARBANES-OXLEY ACT OF 2002
5.4.3 GENERAL DATA PROTECTION REGULATION
5.4.4 BASEL
6 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY TYPE
6.1 INTRODUCTION
6.2 ONLINE RETAIL
6.3 OFFLINE RETAIL
7 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY TECHNOLOGY
7.1 INTRODUCTION
7.2 MACHINE LEARNING AND DEEP LEARNING
7.2.1 FACIAL RECOGNITION
7.2.2 EMOTION DETECTION
7.3 NATURAL LANGUAGE PROCESSING
7.4 OTHERS
7.4.1 ANALYTICS
7.4.2 PROCESS AUTOMATION
8 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY SOLUTION
8.1 INTRODUCTION
8.2 PRODUCT RECOMMENDATION AND PLANNING
8.3 CUSTOMER RELATIONSHIP MANAGEMENT
8.4 VISUAL SEARCH
8.5 VIRTUAL ASSISTANT
8.6 PRICE OPTIMIZATION
8.7 PAYMENT SERVICES MANAGEMENT
8.8 SUPPLY CHAIN MANAGEMENT AND DEMAND PLANNING
8.9 OTHERS
9 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY SERVICE
9.1 INTRODUCTION
9.2 PROFESSIONAL SERVICES
9.3 MANAGED SERVICES
10 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY DEPLOYMENT MODE
10.1 INTRODUCTION
10.2 CLOUD
10.3 ON-PREMISES
11 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET ANALYSIS, BY APPLICATION
11.1 INTRODUCTION
11.2 PREDICTIVE MERCHANDISING
11.3 PROGRAMMATIC ADVERTISING
11.4 MARKET FORECASTING
11.5 IN-STORE VISUAL MONITORING AND SURVEILLANCE
11.6 LOCATION-BASED MARKETING
11.7 OTHERS
11.7.1 REAL-TIME PRICING AND INCENTIVES
11.7.2 REAL-TIME PRODUCT TARGETING
12 GEOGRAPHIC ANALYSIS
12.1 INTRODUCTION
12.2 NORTH AMERICA
12.2.1 NORTH AMERICA, BY TYPE
12.2.2 NORTH AMERICA, BY TECHNOLOGY
12.2.3 NORTH AMERICA, BY SOLUTION
12.2.4 NORTH AMERICA, BY SERVICE
12.2.5 NORTH AMERICA, BY DEPLOYMENT MODE
12.2.6 NORTH AMERICA, BY APPLICATION
12.3 EUROPE
12.3.1 EUROPE, BY TYPE
12.3.2 EUROPE, BY TECHNOLOGY
12.3.3 EUROPE, BY SOLUTION
12.3.4 EUROPE, BY SERVICE
12.3.5 EUROPE, BY DEPLOYMENT MODE
12.3.6 EUROPE, BY APPLICATION
12.4 ASIA PACIFIC
12.4.1 ASIA PACIFIC, BY TYPE
12.4.2 ASIA PACIFIC, BY TECHNOLOGY
12.4.3 ASIA PACIFIC, BY SOLUTION
12.4.4 ASIA PACIFIC, BY SERVICE
12.4.5 ASIA PACIFIC, BY DEPLOYMENT MODE
12.4.6 ASIA PACIFIC, BY APPLICATION
12.5 LATIN AMERICA
12.5.1 LATIN AMERICA, BY TYPE
12.5.2 LATIN AMERICA, BY TECHNOLOGY
12.5.3 LATIN AMERICA, BY SOLUTION
12.5.4 LATIN AMERICA, BY SERVICE
12.5.5 LATIN AMERICA, BY DEPLOYMENT MODE
12.5.6 LATIN AMERICA, BY APPLICATION
12.6 MIDDLE EAST AND AFRICA
12.6.1 MIDDLE EAST AND AFRICA, BY TYPE
12.6.2 MIDDLE EAST AND AFRICA, BY TECHNOLOGY
12.6.3 MIDDLE EAST AND AFRICA, BY SOLUTION
12.6.4 MIDDLE EAST AND AFRICA, BY SERVICE
12.6.5 MIDDLE EAST AND AFRICA, BY DEPLOYMENT MODE
12.6.6 MIDDLE EAST AND AFRICA, BY APPLICATION
13 COMPANY PROFILES
13.1 IBM
(Overview, Strength of Product Portfolio, Business Strategy Excellence, and Recent Developments)*
13.2 MICROSOFT
13.3 NVIDIA
13.4 AMAZON WEB SERVICES
13.5 ORACLE
13.6 SAP
13.7 INTEL
13.8 GOOGLE
13.9 SENTIENT TECHNOLOGIES
13.10 SALESFORCE
13.11 VISENZE
*Details on Overview, Strength of Product Portfolio, Business Strategy Excellence, and Recent Developments might not be captured in case of unlisted companies.
14 APPENDIX
14.1 INDUSTRY EXPERTS
14.2 DISCUSSION GUIDE
14.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.4 INTRODUCING RT: REAL-TIME MARKET INTELLIGENCE
14.5 RELATED REPORTS
14.6 AUTHOR DETAILS
LIST OF TABLES
Table 1 CURRENCY EXCHANGE RATE
Table 2 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 3 ONLINE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 4 OFFLINE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 5 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 6 MACHINE LEARNING AND DEEP LEARNING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 7 NATURAL LANGUAGE PROCESSING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 8 OTHERS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 9 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 10 PRODUCT RECOMMENDATION AND PLANNING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 11 CUSTOMER RELATIONSHIP MANAGEMENT: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 12 VISUAL SEARCH: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 13 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 14 PRICE OPTIMIZATION: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 15 PAYMENT SERVICES MANAGEMENT: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 16 SUPPLY CHAIN MANAGEMENT AND DEMAND PLANNING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 17 OTHERS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 18 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 19 PROFESSIONAL SERVICES: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 20 MANAGED SERVICES: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 21 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 22 CLOUD: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 23 ON-PREMISES: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 24 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 25 PREDICTIVE MERCHANDISING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 26 PROGRAMMATIC ADVERTISING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 27 MARKET FORECASTING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 28 IN-STORE VISUAL MONITORING AND SURVEILLANCE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 29 LOCATION-BASED MARKETING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 30 OTHERS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 31 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 32 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 33 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 34 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 35 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 36 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 37 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 39 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 40 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 41 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 42 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 43 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 44 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 45 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 46 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 47 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 48 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 49 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 50 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 51 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 52 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 53 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 54 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 55 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 56 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 57 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 58 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 59 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 60 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 61 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 62 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 1 CURRENCY EXCHANGE RATE
Table 2 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 3 ONLINE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 4 OFFLINE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 5 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 6 MACHINE LEARNING AND DEEP LEARNING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 7 NATURAL LANGUAGE PROCESSING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 8 OTHERS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 9 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 10 PRODUCT RECOMMENDATION AND PLANNING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 11 CUSTOMER RELATIONSHIP MANAGEMENT: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 12 VISUAL SEARCH: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 13 VIRTUAL ASSISTANT: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 14 PRICE OPTIMIZATION: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 15 PAYMENT SERVICES MANAGEMENT: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 16 SUPPLY CHAIN MANAGEMENT AND DEMAND PLANNING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 17 OTHERS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 18 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 19 PROFESSIONAL SERVICES: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 20 MANAGED SERVICES: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 21 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 22 CLOUD: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 23 ON-PREMISES: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 24 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 25 PREDICTIVE MERCHANDISING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 26 PROGRAMMATIC ADVERTISING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 27 MARKET FORECASTING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 28 IN-STORE VISUAL MONITORING AND SURVEILLANCE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 29 LOCATION-BASED MARKETING: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 30 OTHERS: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 31 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
Table 32 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 33 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 34 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 35 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 36 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 37 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 39 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 40 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 41 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 42 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 43 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 44 EUROPE: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 45 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 46 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 47 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 48 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 49 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 50 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 51 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 52 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 53 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 54 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 55 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 56 LATIN AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
Table 57 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TYPE, 2015–2022 (USD MILLION)
Table 58 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY TECHNOLOGY, 2015–2022 (USD MILLION)
Table 59 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SOLUTION, 2015–2022 (USD MILLION)
Table 60 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
Table 61 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
Table 62 MIDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SIZE, BY APPLICATION, 2015–2022 (USD MILLION)
LIST OF FIGURES
Figure 1 MARKETS COVERED
Figure 2 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET: RESEARCH DESIGN
Figure 3 BREAKDOWN OF PRIMARY INTERVIEWS: BY COMPANY, DESIGNATION, AND REGION
Figure 4 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
Figure 5 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
Figure 6 DATA TRIANGULATION
Figure 7 GLOBAL ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SNAPSHOT (2017–2022)
Figure 8 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SNAPSHOT, BY SOLUTION AND SERVICE
Figure 9 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SNAPSHOT, BY REGION
Figure 10 DEMAND FOR PERSONALIZED PRODUCT RECOMMENDATION IS ONE OF THE MAJOR FACTORS DRIVING THE OVERALL GROWTH OF THE AI IN RETAIL MARKET DURING THE FORECAST PERIOD
Figure 11 NATURAL LANGUAGE PROCESSING TECHNOLOGY IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 12 ASIA PACIFIC IS EXPECTED TO EXHIBIT THE HIGHEST GROWTH POTENTIAL DURING THE FORECAST PERIOD
Figure 13 NORTH AMERICA, AND PRODUCT RECOMMENDATION AND PLANNING ARE EXPECTED TO HAVE THE HIGHEST MARKET SHARES DURING THE FORECAST PERIOD
Figure 14 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
Figure 15 OFFLINE TYPE IS EXPECTED TO GROW WITH A HIGHER GROWTH RATE IN THE AI IN RETAIL MARKET DURING THE FORECAST PERIOD
Figure 16 NATURAL LANGUAGE PROCESSING SEGMENT IS EXPECTED TO BE THE FASTEST GROWING TECHNOLOGY IN THE ARTIFICIAL INTELLIGENCE IN RETAIL MARKET
Figure 17 VISUAL SEARCH SEGMENT IS EXPECTED TO BE THE FASTEST GROWING SOLUTION DURING THE FORECAST PERIOD
Figure 18 MANAGED SERVICES SEGMENT IS EXPECTED TO BE THE FASTER GROWING SEGMENT DURING THE FORECAST PERIOD
Figure 19 ON-PREMISES DEPLOYMENT MODE IS EXPECTED TO HAVE THE HIGHER GROWTH RATE IN THE ARTIFICIAL INTELLIGENCE IN RETAIL MARKET DURING THE FORECAST PERIOD
Figure 20 IN-STORE VISUAL MONITORING AND SURVEILLANCE SEGMENT IS EXPECTED TO BE THE FASTEST GROWING APPLICATION DURING THE FORECAST PERIOD
Figure 21 ASIA PACIFIC IS EXPECTED TO BE THE MOST ATTRACTIVE MARKET FOR RETAILERS DURING THE FORECAST PERIOD
Figure 22 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SNAPSHOT
Figure 23 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SNAPSHOT
Figure 24 IBM: COMPANY SNAPSHOT
Figure 25 MICROSOFT: COMPANY SNAPSHOT
Figure 26 NVIDIA: COMPANY SNAPSHOT
Figure 27 AMAZON WEB SERVICES: COMPANY SNAPSHOT
Figure 28 ORACLE: COMPANY SNAPSHOT
Figure 29 SAP: COMPANY SNAPSHOT
Figure 30 INTEL: COMPANY SNAPSHOT
Figure 31 GOOGLE: COMPANY SNAPSHOT
Figure 32 SALESFORCE: COMPANY SNAPSHOT
Figure 1 MARKETS COVERED
Figure 2 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET: RESEARCH DESIGN
Figure 3 BREAKDOWN OF PRIMARY INTERVIEWS: BY COMPANY, DESIGNATION, AND REGION
Figure 4 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
Figure 5 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
Figure 6 DATA TRIANGULATION
Figure 7 GLOBAL ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SNAPSHOT (2017–2022)
Figure 8 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SNAPSHOT, BY SOLUTION AND SERVICE
Figure 9 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SNAPSHOT, BY REGION
Figure 10 DEMAND FOR PERSONALIZED PRODUCT RECOMMENDATION IS ONE OF THE MAJOR FACTORS DRIVING THE OVERALL GROWTH OF THE AI IN RETAIL MARKET DURING THE FORECAST PERIOD
Figure 11 NATURAL LANGUAGE PROCESSING TECHNOLOGY IS EXPECTED TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
Figure 12 ASIA PACIFIC IS EXPECTED TO EXHIBIT THE HIGHEST GROWTH POTENTIAL DURING THE FORECAST PERIOD
Figure 13 NORTH AMERICA, AND PRODUCT RECOMMENDATION AND PLANNING ARE EXPECTED TO HAVE THE HIGHEST MARKET SHARES DURING THE FORECAST PERIOD
Figure 14 ARTIFICIAL INTELLIGENCE IN RETAIL MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
Figure 15 OFFLINE TYPE IS EXPECTED TO GROW WITH A HIGHER GROWTH RATE IN THE AI IN RETAIL MARKET DURING THE FORECAST PERIOD
Figure 16 NATURAL LANGUAGE PROCESSING SEGMENT IS EXPECTED TO BE THE FASTEST GROWING TECHNOLOGY IN THE ARTIFICIAL INTELLIGENCE IN RETAIL MARKET
Figure 17 VISUAL SEARCH SEGMENT IS EXPECTED TO BE THE FASTEST GROWING SOLUTION DURING THE FORECAST PERIOD
Figure 18 MANAGED SERVICES SEGMENT IS EXPECTED TO BE THE FASTER GROWING SEGMENT DURING THE FORECAST PERIOD
Figure 19 ON-PREMISES DEPLOYMENT MODE IS EXPECTED TO HAVE THE HIGHER GROWTH RATE IN THE ARTIFICIAL INTELLIGENCE IN RETAIL MARKET DURING THE FORECAST PERIOD
Figure 20 IN-STORE VISUAL MONITORING AND SURVEILLANCE SEGMENT IS EXPECTED TO BE THE FASTEST GROWING APPLICATION DURING THE FORECAST PERIOD
Figure 21 ASIA PACIFIC IS EXPECTED TO BE THE MOST ATTRACTIVE MARKET FOR RETAILERS DURING THE FORECAST PERIOD
Figure 22 NORTH AMERICA: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SNAPSHOT
Figure 23 ASIA PACIFIC: ARTIFICIAL INTELLIGENCE IN RETAIL MARKET SNAPSHOT
Figure 24 IBM: COMPANY SNAPSHOT
Figure 25 MICROSOFT: COMPANY SNAPSHOT
Figure 26 NVIDIA: COMPANY SNAPSHOT
Figure 27 AMAZON WEB SERVICES: COMPANY SNAPSHOT
Figure 28 ORACLE: COMPANY SNAPSHOT
Figure 29 SAP: COMPANY SNAPSHOT
Figure 30 INTEL: COMPANY SNAPSHOT
Figure 31 GOOGLE: COMPANY SNAPSHOT
Figure 32 SALESFORCE: COMPANY SNAPSHOT
