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Global Image Recognition in Retail Market (2023-2028) Competitive Analysis, Impact of Covid-19, Impact of Economic Slowdown & Impending Recession, Ansoff Analysis

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  • 172 Pages
  • February 2024
  • Region: Global
  • Infogence Global Research
  • ID: 5515462

Deep neural networks have been designed for a variety of figure identification-related tasks, which have greatly surpassed traditional methods based on hand-crafted image features

The Global Image Recognition in Retail Market is estimated to be USD 2.71 Bn in 2023 and is expected to reach USD 6.75 Bn by 2028 growing at a CAGR of 19.98%.

Market Dynamics

Market dynamics are forces that impact the prices and behaviors of the stakeholders. These forces create pricing signals which result from the changes in the supply and demand curves for a given product or service. Forces of Market Dynamics may be related to macro-economic and micro-economic factors. There are dynamic market forces other than price, demand, and supply. Human emotions can also drive decisions, influence the market, and create price signals.

As the market dynamics impact the supply and demand curves, decision-makers aim to determine the best way to use various financial tools to stem various strategies for speeding the growth and reducing the risks.

Market Segmentations

  • The Global Image Recognition in Retail Market is segmented based on Component, Technology, Deployment, Applications, and Geography.
  • By Component, the market is classified into Hardware, Software, and Services.
  • By Technology, the market is classified into Barcode Recognition, Digital Image Processing, Object Recognition, Facial Recognition, and Others.
  • By Deployment, the market is classified into Cloud and On-Premises.
  • By Applications, the market is classified into Visual Product Search, Marketing & Advertising, Security & Surveillance, Vision Analytics, and Other.
  • By Geography, the market is classified into Americas, Europe, Middle-East & Africa, and Asia-Pacific.

Company Profiles

The report provides a detailed analysis of the competitors in the market. It covers the financial performance analysis for the publicly listed companies in the market. The report also offers detailed information on the companies' recent development and competitive scenario. Some of the companies covered in this report are Google LLC, Hitachi, Ltd., Honeywell International Inc., IBM Corp., Intelligent Retail, LTU Technologies, etc.

Countries Studied

  • America (Argentina, Brazil, Canada, Chile, Colombia, Mexico, Peru, United States, Rest of Americas)
  • Europe (Austria, Belgium, Denmark, Finland, France, Germany, Italy, Netherlands, Norway, Poland, Russia, Spain, Sweden, Switzerland, United Kingdom, Rest of Europe)
  • Middle-East and Africa (Egypt, Israel, Qatar, Saudi Arabia, South Africa, United Arab Emirates, Rest of MEA)
  • Asia-Pacific (Australia, Bangladesh, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Sri Lanka, Thailand, Taiwan, Rest of Asia-Pacific)

Competitive Quadrant

The report includes Competitive Quadrant, a proprietary tool to analyze and evaluate the position of companies based on their Industry Position score and Market Performance score. The tool uses various factors for categorizing the players into four categories. Some of these factors considered for analysis are financial performance over the last 3 years, growth strategies, innovation score, new product launches, investments, growth in market share, etc.

Ansoff Analysis

  • The report presents a detailed Ansoff matrix analysis for the Global Image Recognition in Retail Market. Ansoff Matrix, also known as Product/Market Expansion Grid, is a strategic tool used to design strategies for the growth of the company. The matrix can be used to evaluate approaches in four strategies viz. Market Development, Market Penetration, Product Development and Diversification. The matrix is also used for risk analysis to understand the risk involved with each approach.
  • The publisher analyses the Global Image Recognition in Retail Market using the Ansoff Matrix to provide the best approaches a company can take to improve its market position.
  • Based on the SWOT analysis conducted on the industry and industry players, the publisher has devised suitable strategies for market growth.

Why buy this report?

  • The report offers a comprehensive evaluation of the Global Image Recognition in Retail Market. The report includes in-depth qualitative analysis, verifiable data from authentic sources, and projections about market size. The projections are calculated using proven research methodologies.
  • The report has been compiled through extensive primary and secondary research. The primary research is done through interviews, surveys, and observation of renowned personnel in the industry.
  • The report includes an in-depth market analysis using Porter's 5 forces model, PESTLE Analysis, and the Ansoff Matrix. In addition, the impact of COVID-19 and the impact of economic slowdown & impending recession on the market are also featured in the report.
  • The report also includes the regulatory scenario in the industry, which will help you make a well-informed decision. The report discusses major regulatory bodies and major rules and regulations imposed on this sector across various geographies.
  • The report also contains the competitive analysis using Positioning Quadrants, the Proprietary competitive positioning tool.

Report Highlights:

  • A complete analysis of the market, including parent industry
  • Important market dynamics and trends
  • Market segmentation
  • Historical, current, and projected size of the market based on value and volume
  • Market shares and strategies of key players
  • Recommendations to companies for strengthening their foothold in the market

Table of Contents

1 Report Description
1.1 Study Objectives
1.2 Market Definition
1.3 Currency
1.4 Years Considered
1.5 Language
1.6 Key Stakeholders
2 Research Methodology
2.1 Research Process
2.2 Data Collection and Validation
2.2.1 Secondary Research
2.2.2 Primary Research
2.2.3 Models
2.3 Market Size Estimation
2.3.1 Bottom-Up Approach
2.3.2 Top-Down Approach
2.4 Assumptions of the Study
2.5 Limitations of the Study
3 Executive Summary
3.1 Introduction
3.2 Market Size, Segmentations, and Outlook
4 Market Dynamics
4.1 Drivers
4.1.1 Need to Increase On-Shelf Availability, Enhance Customer Experience, and Maximize ROI
4.1.2 Increasing the Use of Image Recognition Applications
4.1.3 Increasing Use of High Bandwidth Data Services
4.1.4 Increasing Demand for Security Applications and Products Enabled with Image Recognition Functions
4.2 Restraints
4.2.1 High Risk Related to Customer Data Thefts
4.2.2 High Cost of Installation of Image Recognition Systems
4.3 Opportunities
4.3.1 Growing Adoption of Cloud-Based Image Recognition Solutions
4.3.2 Rising Demand for Brand Recognition Among End Users
4.3.3 Increasing Demand for Big Data Analytics
4.4 Challenges
4.4.1 Low Resolution Image Size and Storage Issues
4.4.2 Growing Instances of Shoplifting
5 Market Analysis
5.1 Regulatory Scenario
5.2 Porter's Five Forces Analysis
5.3 PESTLE Analysis
5.4 SWOT Analysis
5.5 Impact of COVID-19
5.6 Impact of Economic Slowdown & Impending Recession
5.7 Ansoff Matrix Analysis
6 Global Image Recognition in Retail Market, By Component
6.1 Introduction
6.2 Hardware
6.3 Software
6.4 Services
7 Global Image Recognition in Retail Market, By Technology
7.1 Introduction
7.2 Barcode Recognition
7.3 Digital Image Processing
7.4 Object Recognition
7.5 Facial Recognition
7.6 Others
8 Global Image Recognition in Retail Market, By Deployment
8.1 Introduction
8.2 Cloud
8.3 On-Premises
9 Global Image Recognition in Retail Market, By Applications
9.1 Introduction
9.2 Visual Product Search
9.3 Marketing & Advertising
9.4 Security & Surveillance
9.5 Vision Analytics
9.6 Others
10 Americas' Image Recognition in Retail Market
10.1 Introduction
10.2 Argentina
10.3 Brazil
10.4 Canada
10.5 Chile
10.6 Colombia
10.7 Mexico
10.8 Peru
10.9 United States
10.10 Rest of Americas
11 Europe's Image Recognition in Retail Market
11.1 Introduction
11.2 Austria
11.3 Belgium
11.4 Denmark
11.5 Finland
11.6 France
11.7 Germany
11.8 Italy
11.9 Netherlands
11.10 Norway
11.11 Poland
11.12 Russia
11.13 Spain
11.14 Sweden
11.15 Switzerland
11.16 United Kingdom
11.17 Rest of Europe
12 Middle East and Africa's Image Recognition in Retail Market
12.1 Introduction
12.2 Egypt
12.3 Israel
12.4 Qatar
12.5 Saudi Arabia
12.6 South Africa
12.7 United Arab Emirates
12.8 Rest of MEA
13 APAC's Image Recognition in Retail Market
13.1 Introduction
13.2 Australia
13.3 Bangladesh
13.4 China
13.5 India
13.6 Indonesia
13.7 Japan
13.8 Malaysia
13.9 Philippines
13.10 Singapore
13.11 South Korea
13.12 Sri Lanka
13.13 Thailand
13.14 Taiwan
13.15 Rest of Asia-Pacific
14 Competitive Landscape
14.1 Competitive Quadrant
14.2 Market Share Analysis
14.3 Strategic Initiatives
14.3.1 M&A and Investments
14.3.2 Partnerships and Collaborations
14.3.3 Product Developments and Improvements
15 Company Profiles
15.1 Amazon.com, Inc.
15.2 Attrasoft, Inc.
15.3 Blippar
15.4 Google LLC
15.5 Hitachi, Ltd.
15.6 Honeywell International Inc.
15.7 Huawei Technologies Co., Ltd.
15.8 IBM Corp.
15.9 Intelligent Retail
15.10 LTU Technologies
15.11 Microsoft Corp.
15.12 NEC Corp.
15.13 Pattern Recognition Co. GMBH
15.14 Planorama
15.15 Qualcomm Technologies, Inc.
15.16 Ricoh Innovations Corp.
15.17 Snap2Insight Inc.
15.18 TRAX Retail
15.19 Toshiba Corp.
15.20 Wikitude GmbH
16 Appendix
16.1 Questionnaire

Companies Mentioned

  • Amazon.com, Inc.
  • Attrasoft, Inc.
  • Blippar
  • Google LLC
  • Hitachi, Ltd.
  • Honeywell International Inc.
  • Huawei Technologies Co., Ltd.
  • IBM Corp.
  • Intelligent Retail
  • LTU Technologies
  • Microsoft Corp.
  • NEC Corp.
  • Pattern Recognition Co. GMBH
  • Planorama
  • Qualcomm Technologies, Inc.
  • Ricoh Innovations Corp.
  • Snap2Insight Inc.
  • TRAX Retail
  • Toshiba Corp.
  • Wikitude GmbH

Table Information