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Global Smart Retail Market to reach USD XXX billion by the end of 2029.

Global Smart Retail Market Size study & Forecast, by Solution (Hardware, Software) by Application (Visual Marketing, Smart Label, Smart Payment System, Intelligent System, Others) and Regional Analysis, 2022-2029

Product Code: ICTNGT-99846829
Publish Date: 13-05-2023
Page: 200

Global Smart Retail Market is valued approximately USD XXX billion in 2021 and is anticipated to grow with a healthy growth rate of more than XX% over the forecast period 2022-2029. Smart retail refers to the use of advanced technologies, such as artificial intelligence (AI), machine learning, and the Internet of Things (IoT), to enhance the customer shopping experience, optimize inventory management, and streamline operational processes. Smart retail solutions typically leverage data analytics to gain insights into customer behavior, preferences, and buying patterns, which can be used to create personalized experiences and offer targeted promotions. The key factor driving the market growth is growing adoption of big data analytics, machine learning, blockchain, and artificial intelligence technologies by retailers, rising use of robots and automation to offer shopping convenience to customers, and changing customer expectations leading to increased use of smart technologies by retailers is anticipated to create the lucrative demand for the market during forecast period. However, one of the key trends fueling market growth is the growing adoption of technologies like artificial intelligence (AI), virtual reality (VR), augmented reality (AR), and the internet of things (IoT) to improve the shopping experience for consumers, enable accurate inventory management, and enhance store operations. Because technology enables brands to interact with customers via their mobile devices, augmented reality is particularly transforming the retail sector. Adopting augmented reality (AR) in marketing aids in fostering customer relationships, improving the customer experience, and ultimately boosting sales.

Moreover, the rising adoption of robots and automation in retail stores to offer convenience shopping experience to customers is anticipated to boost the market growth. In Jan. 2020, Bossa Nova Robotics, a company based in the Bay area, has announced the collaboration with Walmart Inc. to deliver shelf-scanning inventory robots to 1,000 Walmart shops across the US. Additionally, rising investment by the market players for the development of smart stores and growing adoption of connected devices is anticipated to create the lucrative opportunity for the market during forecast period. However, the rising maintenance costs of advanced high-end computing systems and high risks of customer data thefts stifles market growth throughout the forecast period of 2022-2029.

The key regions considered for the Global Smart Retail Market study includes Asia Pacific, North America, Europe, Latin America, and Rest of the World. North America is the dominating region across the world in terms of market share, owing to the presence of key market players, growing adoption of connected devices and rising number of retail stores in the region. Whereas, Asia Pacific is also anticipated to exhibit highest growth rate over the forecast period, owing to factors such as increasing deployment of smart retail solutions such as big data analytics, reception robots, intelligent dressing mirrors, and shopping guide robots in countries such as China and Japan would create lucrative growth prospects for the Smart Retail market across Asia Pacific region.

Major market player included in this report are:
Amazon.com, Inc.
NCR Corporation
PAX Global Technology Limited
Ingenico, Fiserv, Inc.
Google LLC
Honeywell International Inc.
Huawei Technologies Co., Ltd.
LG Display Co., Ltd.
Intel Corporation
Samsung Electronics

Recent Developments in the Market:
Ø In January 2020, Intel Corporation collaborated with Sensormatic Solutions, a Johnson Controls division, to provide merchants scalable, AI-powered solutions. Sensormatic Solutions will also use the Intel Distribution of OpenVINO toolkits and Intel models when distributing its products to retailers.

Global Smart Retail Market Report Scope:
Historical Data 2019-2020-2021
Base Year for Estimation 2021
Forecast period 2022-2029
Report Coverage Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
Segments Covered Solution, Application, Region
Regional Scope North America; Europe; Asia Pacific; Latin America; Rest of the World
Customization Scope Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Solution:
Hardware
Software
By Application:
Visual Marketing
Smart Label
Smart Payment System
Intelligent System
Others

By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE
Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
RoLA
Rest of the World

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2029 (USD Billion)
1.2.1. Smart Retail Market, by Region, 2019-2029 (USD Billion)
1.2.2. Smart Retail Market, by Solution, 2019-2029 (USD Billion)
1.2.3. Smart Retail Market, by Application, 2019-2029 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Smart Retail Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Smart Retail Market Dynamics
3.1. Smart Retail Market Impact Analysis (2019-2029)
3.1.1. Market Drivers
3.1.1.1. Growing adoption of big data analytics, machine learning, blockchain, and artificial intelligence technologies by retailers
3.1.1.2. Rising use of robots and automation to offer shopping convenience to customers
3.1.1.3. Changing customer expectations leading to increased use of smart technologies by retailers
3.1.2. Market Challenges
3.1.2.1. Rising maintenance costs of advanced high-end computing systems
3.1.2.2. High risks of customer data thefts
3.1.3. Market Opportunities
3.1.3.1. Rising investment by the market players for the development of smart stores
3.1.3.2. Growing adoption of connected devices
Chapter 4. Global Smart Retail Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Futuristic Approach to Porter’s 5 Force Model (2019-2029)
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. Industry Experts Prospective
4.7. Analyst Recommendation & Conclusion
Chapter 5. Risk Assessment: COVID-19 Impact
5.1. Assessment of the overall impact of COVID-19 on the industry
5.2. Pre COVID-19 and post COVID-19 Market scenario
Chapter 6. Global Smart Retail Market, by Solution
6.1. Market Snapshot
6.2. Global Smart Retail Market by Solution, Performance – Potential Analysis
6.3. Global Smart Retail Market Estimates & Forecasts by Solution 2019-2029 (USD Billion)
6.4. Smart Retail Market, Sub Segment Analysis
6.4.1. Hardware
6.4.2. Software
Chapter 7. Global Smart Retail Market, by Application
7.1. Market Snapshot
7.2. Global Smart Retail Market by Application, Performance – Potential Analysis
7.3. Global Smart Retail Market Estimates & Forecasts by Application 2019-2029 (USD Billion)
7.4. Smart Retail Market, Sub Segment Analysis
7.4.1. Visual Marketing
7.4.2. Smart Label
7.4.3. Smart Payment System
7.4.4. Intelligent System
7.4.5. Others
Chapter 8. Global Smart Retail Market, Regional Analysis
8.1. Smart Retail Market, Regional Market Snapshot
8.2. North America Smart Retail Market
8.2.1. U.S. Smart Retail Market
8.2.1.1. Solution breakdown estimates & forecasts, 2019-2029
8.2.1.2. Application breakdown estimates & forecasts, 2019-2029
8.2.2. Canada Smart Retail Market
8.3. Europe Smart Retail Market Snapshot
8.3.1. U.K. Smart Retail Market
8.3.2. Germany Smart Retail Market
8.3.3. France Smart Retail Market
8.3.4. Spain Smart Retail Market
8.3.5. Italy Smart Retail Market
8.3.6. Rest of Europe Smart Retail Market
8.4. Asia-Pacific Smart Retail Market Snapshot
8.4.1. China Smart Retail Market
8.4.2. India Smart Retail Market
8.4.3. Japan Smart Retail Market
8.4.4. Australia Smart Retail Market
8.4.5. South Korea Smart Retail Market
8.4.6. Rest of Asia Pacific Smart Retail Market
8.5. Latin America Smart Retail Market Snapshot
8.5.1. Brazil Smart Retail Market
8.5.2. Mexico Smart Retail Market
8.5.3. Rest of Latin America Smart Retail Market
8.6. Rest of The World Smart Retail Market

Chapter 9. Competitive Intelligence
9.1. Top Market Strategies
9.2. Company Profiles
9.2.1. Amazon.com, Inc.
9.2.1.1. Key Information
9.2.1.2. Overview
9.2.1.3. Financial (Subject to Data Availability)
9.2.1.4. Product Summary
9.2.1.5. Recent Developments
9.2.2. NCR Corporation
9.2.3. PAX Global Technology Limited
9.2.4. Ingenico, Fiserv, Inc.
9.2.5. Google LLC
9.2.6. Honeywell International Inc.
9.2.7. Huawei Technologies Co., Ltd.
9.2.8. LG Display Co., Ltd.
9.2.9. Intel Corporation
9.2.10. Samsung Electronics
Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
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Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
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