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小売業向けAIの世界市場の予測 ~2022年:機械学習&ディープラーニング・NLP

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 MarketsandMarkets 商品コード 571020
出版日 ページ情報 英文 140 Pages
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本日の銀行送金レート: 1USD=108.13円で換算しております。
小売業向けAIの世界市場の予測 ~2022年:機械学習&ディープラーニング・NLP 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
出版日: 2017年10月24日 ページ情報: 英文 140 Pages
概要

世界の小売業向けAIの市場は予測期間中38.3%のCAGR (年間複合成長率) で推移し、2017年の9億9360万米ドルから、2022年には50億3400万米ドルの規模に成長すると予測されています。実店舗での優れた監視・モニタリング機能の必要性の拡大、業界内でのAIへの認識の向上と導入の拡大、ユーザーエクスペリエンスの向上、生産性の向上、RoI、インベントリーの精度、サプライチェーンの最適化など、様々な要因が同市場の成長を推進しています。

当レポートでは、世界の小売業向けAIの市場を調査し、市場の定義と概要、市場成長への影響因子および市場機会の分析、産業動向と利用事例、関連法規制、タイプ・技術・ソリューション・サービス・導入モード・用途・地域/主要国別の動向と市場規模の推移と予測、競合環境、主要事業者のプロファイルなどをまとめています。

FIGURE 7 GLOBAL ARTIFICIAL INTELLIGENCE IN RETAIL MARKET

第1章 イントロダクション

第2章 調査手法

第3章 エグゼクティブサマリー

第4章 重要考察

  • 魅力的な市場機会
  • 小売業向けAIの各種技術
  • ライフサイクル分析:地域別

第5章 市場概要・産業動向

  • イントロダクション
  • 市場力学
    • 推進因子
    • 抑制要因
    • 市場機会
    • 課題
  • 産業動向
    • イントロダクション
    • 利用事例
  • 法規制の影響

第6章 市場分析:タイプ別

  • イントロダクション
  • オンライン小売
  • オフライン小売

第7章 市場分析:技術別

  • イントロダクション
  • 機械学習・ディープラーニング
    • 顔認識
    • 感情検出
  • 自然言語処理
  • その他
    • アナリティクス
    • プロセスオートメーション

第8章 市場分析:ソリューション別

  • イントロダクション
  • 製品の推薦・プランニング
  • 顧客関係管理
  • ビジュアルサーチ
  • 仮想アシスタント
  • 価格の最適化
  • 決済サービス管理
  • サプライチェーン管理・需要プランニング
  • その他

第9章 市場分析:サービス別

  • イントロダクション
  • プロフェッショナルサービス
  • マネージドサービス

第10章 市場分析:導入モード別

  • イントロダクション
  • クラウド
  • オンプレミス

第11章 市場分析:用途別

  • イントロダクション
  • 予測的マーチャンダイジング
  • プログラマティックアドバタイジング
  • 市場予測
  • 店舗内ビジュアルモニタリング・監視
  • 位置情報ベースのマーケティング
  • その他
    • リアルタイムプライシング&インセンティブ
    • リアルタイム製品ターゲティング

第12章 地域分析

  • イントロダクション
  • 北米
  • 欧州
  • アジア太平洋
  • ラテンアメリカ
  • 中東・アフリカ

第13章 企業プロファイル

  • IBM
  • MICROSOFT
  • NVIDIA
  • AMAZON WEB SERVICES
  • ORACLE
  • SAP
  • INTEL
  • GOOGLE
  • SENTIENT TECHNOLOGIES
  • SALESFORCE
  • VISENZE

第14章 付録

図表

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)

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
目次
Product Code: TC 5669

"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:

  • 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%

The key vendors profiled in the report are as follows:

  • 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.

TABLE OF CONTENTS

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 (Overview, Strength of Product Portfolio, Business Strategy Excellence, and Recent Developments)*

  • 13.1. IBM
  • 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