Machine Learning Chip Market Report - (Growth and Size by 2031)
The Machine Learning Chip Market is expected to register a CAGR of 36.2% from 2024 to 2031, with a market size expanding from US$ XX million in 2024 to US$ XX Million by 2031.
The report is segmented by Chip Type (ASIC, GPU, FPGA, CPU, Others), Industry (BFSI, Media and Advertising, Retail, IT and Telecom, Healthcare, Automotive and Transportation, Others). The global analysis is further broken-down at regional level and major countries. The report offers the value in USD for the above analysis and segments
Purpose of the ReportThe report Machine Learning Chip Market by The Insight Partners aims to describe the present landscape and future growth, top driving factors, challenges, and opportunities. This will provide insights to various business stakeholders, such as:
- Technology Providers/Manufacturers: To understand the evolving market dynamics and know the potential growth opportunities, enabling them to make informed strategic decisions.
- Investors: To conduct a comprehensive trend analysis regarding the market growth rate, market financial projections, and opportunities that exist across the value chain.
- Regulatory bodies: To regulate policies and police activities in the market with the aim of minimizing abuse, preserving investor trust and confidence, and upholding the integrity and stability of the market.
Machine Learning Chip Market Segmentation
Chip Type- ASIC
- GPU
- FPGA
- CPU
- Others
- BFSI
- Media and Advertising
- Retail
- IT and Telecom
- Healthcare
- Automotive and Transportation
- Others
Strategic Insights
Machine Learning Chip Market Growth Drivers- Explosion of AI and Machine Learning Applications: The rapid expansion of artificial intelligence (AI) and machine learning (ML) applications across industries is a significant driver for the machine learning chip market. These applications, ranging from voice assistants and facial recognition to self-driving cars and robotics, demand specialized hardware to process vast amounts of data efficiently. As AI becomes more integral to various sectors such as healthcare, finance, and manufacturing, the need for machine learning chips capable of executing complex algorithms with high speed and accuracy is surging, fueling market growth.
- Need for Enhanced Computational Power and Efficiency: Traditional processors like CPUs are increasingly struggling to handle the computational demands of machine learning algorithms, which often require parallel processing and massive data throughput. Machine learning chips, including GPUs, TPUs, and FPGAs, are specifically designed to address these challenges. They offer high-performance computing capabilities, optimized for parallel processing and energy efficiency, allowing for faster training of machine learning models and reducing the time to derive meaningful insights from large datasets, thus driving adoption across industries.
- Proliferation of Edge Computing and IoT Devices: With the rise of edge computing and Internet of Things (IoT) devices, there is a growing demand for machine learning chips capable of performing real-time processing directly at the edge, rather than relying on centralized cloud-based systems. Edge devices such as smartphones, wearables, autonomous vehicles, and smart cameras require low-latency, high-efficiency ML chips to process data locally. This trend is accelerating as industries demand faster, more reliable decision-making with reduced reliance on cloud infrastructure, creating strong growth opportunities for machine learning chips in edge devices
- Development of Specialized AI/ML Processors: A key trend in the machine learning chip market is the increasing development of specialized processors designed specifically for AI and ML workloads. Companies like NVIDIA, Google, and Intel are advancing the design of Application-Specific Integrated Circuits (ASICs) and Tensor Processing Units (TPUs) that can accelerate machine learning processes more effectively than general-purpose processors. These custom chips are optimized for specific AI applications, such as image recognition, language processing, and predictive analytics, and are becoming essential for high-performance computing in AI systems.
- Integration of Machine Learning Chips in Consumer Electronics: Machine learning chips are becoming integral components in consumer electronics, such as smartphones, smart speakers, laptops, and even home appliances. These devices utilize ML chips to power applications like voice assistants, facial recognition, and personalized recommendations. As consumers demand smarter, more intuitive products, the need for machine learning chips in everyday devices continues to rise, pushing the trend of integrating AI-powered features into consumer electronics. This trend is helping to expand the machine learning chip market beyond traditional industrial applications into consumer-facing technology.
- Focus on Energy-Efficient Machine Learning Chips: With the increasing complexity of machine learning models, there is a growing focus on developing energy-efficient chips to handle AI workloads. As training deep learning models becomes more computationally intensive, the energy consumption associated with these tasks rises dramatically, leading to higher operational costs. To address this, chip manufacturers are emphasizing power-efficient designs for AI processors, such as using low-power FPGAs and advanced cooling techniques. This trend not only reduces energy costs but also supports the sustainability goals of companies that rely on large-scale machine learning deployments.
- Growth of Cloud-based AI Services: The rapid growth of cloud computing and the adoption of AI-as-a-Service models present significant opportunities for machine learning chips. Cloud providers, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, are investing heavily in machine learning infrastructure to offer AI solutions at scale. This shift to cloud-based AI services increases the demand for specialized chips, such as TPUs and GPUs, to accelerate the processing of AI tasks in data centers. With more companies moving to the cloud to access AI capabilities, the demand for advanced machine learning chips is set to grow significantly.
- Expansion of AI in Autonomous Vehicles: Autonomous vehicles (AVs) are one of the most promising sectors driving the demand for machine learning chips. AVs rely heavily on machine learning for real-time decision-making, navigation, object detection, and safety systems. Machine learning chips capable of processing sensor data from cameras, LiDAR, and radar are critical to the development of self-driving technologies. As the autonomous vehicle market continues to expand globally, manufacturers of machine learning chips have a significant opportunity to provide the high-performance, low-latency chips required for these advanced systems.
- Adoption of AI in Healthcare and Diagnostics: The integration of AI and machine learning in healthcare, particularly in diagnostics and personalized medicine, offers a significant opportunity for the machine learning chip market. Medical devices and systems that use machine learning to analyze medical images, genetic data, and patient records require specialized chips capable of processing large volumes of complex data quickly and accurately. As healthcare systems globally embrace AI to improve patient outcomes, reduce costs, and enhance decision-making, the demand for machine learning chips in this sector is set to soar, creating vast growth potential for chip manufacturers
Market Report Scope
Key Selling Points
- Comprehensive Coverage: The report comprehensively covers the analysis of products, services, types, and end users of the Machine Learning Chip Market, providing a holistic landscape.
- Expert Analysis: The report is compiled based on the in-depth understanding of industry experts and analysts.
- Up-to-date Information: The report assures business relevance due to its coverage of recent information and data trends.
- Customization Options: This report can be customized to cater to specific client requirements and suit the business strategies aptly.
The research report on the Machine Learning Chip Market can, therefore, help spearhead the trail of decoding and understanding the industry scenario and growth prospects. Although there can be a few valid concerns, the overall benefits of this report tend to outweigh the disadvantages.
REGIONAL FRAMEWORK
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- Request discounts available for Start-Ups & Universities
Report Coverage
Revenue forecast, Company Analysis, Industry landscape, Growth factors, and Trends
Segment Covered
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Regional Scope
North America, Europe, Asia Pacific, Middle East & Africa, South & Central America
Country Scope
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Frequently Asked Questions
The report can be delivered in PDF/PPT format; we can also share excel dataset based on the request
Some of the customization options available based on the request are an additional 3-5 company profiles and country-specific analysis of 3-5 countries of your choice. Customizations are to be requested/discussed before making final order confirmation# as our team would review the same and check the feasibility
The Machine Learning Chip Market is estimated to witness a CAGR of 36.2% from 2023 to 2041
Rise of Digitalization, Expansion of the IT Industry, Demand for Smart Devices
Emphasis on Interpretability in AI Models
TABLE OF CONTENTS
1. INTRODUCTION
1.1. SCOPE OF THE STUDY
1.2. THE INSIGHT PARTNERS RESEARCH REPORT GUIDANCE
1.3. MARKET SEGMENTATION
1.3.1 Machine Learning Chip Market - By Chip Type
1.3.2 Machine Learning Chip Market - By Industry
1.3.3 Machine Learning Chip Market - By Region
1.3.3.1 By Country
2. KEY TAKEAWAYS
3. RESEARCH METHODOLOGY
4. MACHINE LEARNING CHIP MARKET LANDSCAPE
4.1. OVERVIEW
4.2. PORTER'S FIVE FORCES ANALYSIS
4.2.1 Bargaining Power of Buyers
4.2.1 Bargaining Power of Suppliers
4.2.1 Threat of Substitute
4.2.1 Threat of New Entrants
4.2.1 Competitive Rivalry
4.3. ECOSYSTEM ANALYSIS
4.4. EXPERT OPINIONS
5. MACHINE LEARNING CHIP MARKET - KEY MARKET DYNAMICS
5.1. KEY MARKET DRIVERS
5.2. KEY MARKET RESTRAINTS
5.3. KEY MARKET OPPORTUNITIES
5.4. FUTURE TRENDS
5.5. IMPACT ANALYSIS OF DRIVERS AND RESTRAINTS
6. MACHINE LEARNING CHIP MARKET - GLOBAL MARKET ANALYSIS
6.1. MACHINE LEARNING CHIP - GLOBAL MARKET OVERVIEW
6.2. MACHINE LEARNING CHIP - GLOBAL MARKET AND FORECAST TO 2027
6.3. MARKET POSITIONING/MARKET SHARE
7. MACHINE LEARNING CHIP MARKET - REVENUE AND FORECASTS TO 2027 - CHIP TYPE
7.1. OVERVIEW
7.2. CHIP TYPE MARKET FORECASTS AND ANALYSIS
7.3. ASIC
7.3.1. Overview
7.3.2. ASIC Market Forecast and Analysis
7.4. GPU
7.4.1. Overview
7.4.2. GPU Market Forecast and Analysis
7.5. FPGA
7.5.1. Overview
7.5.2. FPGA Market Forecast and Analysis
7.6. CPU
7.6.1. Overview
7.6.2. CPU Market Forecast and Analysis
7.7. OTHERS
7.7.1. Overview
7.7.2. Others Market Forecast and Analysis
8. MACHINE LEARNING CHIP MARKET - REVENUE AND FORECASTS TO 2027 - INDUSTRY
8.1. OVERVIEW
8.2. INDUSTRY MARKET FORECASTS AND ANALYSIS
8.3. BFSI
8.3.1. Overview
8.3.2. BFSI Market Forecast and Analysis
8.4. MEDIA AND ADVERTISING
8.4.1. Overview
8.4.2. Media and Advertising Market Forecast and Analysis
8.5. RETAIL
8.5.1. Overview
8.5.2. Retail Market Forecast and Analysis
8.6. IT AND TELECOM
8.6.1. Overview
8.6.2. IT and Telecom Market Forecast and Analysis
8.7. HEALTHCARE
8.7.1. Overview
8.7.2. Healthcare Market Forecast and Analysis
8.8. AUTOMOTIVE AND TRANSPORTATION
8.8.1. Overview
8.8.2. Automotive and Transportation Market Forecast and Analysis
8.9. OTHERS
8.9.1. Overview
8.9.2. Others Market Forecast and Analysis
9. MACHINE LEARNING CHIP MARKET REVENUE AND FORECASTS TO 2027 - GEOGRAPHICAL ANALYSIS
9.1. NORTH AMERICA
9.1.1 North America Machine Learning Chip Market Overview
9.1.2 North America Machine Learning Chip Market Forecasts and Analysis
9.1.3 North America Machine Learning Chip Market Forecasts and Analysis - By Chip Type
9.1.4 North America Machine Learning Chip Market Forecasts and Analysis - By Industry
9.1.5 North America Machine Learning Chip Market Forecasts and Analysis - By Countries
9.1.5.1 United States Machine Learning Chip Market
9.1.5.1.1 United States Machine Learning Chip Market by Chip Type
9.1.5.1.2 United States Machine Learning Chip Market by Industry
9.1.5.2 Canada Machine Learning Chip Market
9.1.5.2.1 Canada Machine Learning Chip Market by Chip Type
9.1.5.2.2 Canada Machine Learning Chip Market by Industry
9.1.5.3 Mexico Machine Learning Chip Market
9.1.5.3.1 Mexico Machine Learning Chip Market by Chip Type
9.1.5.3.2 Mexico Machine Learning Chip Market by Industry
9.2. EUROPE
9.2.1 Europe Machine Learning Chip Market Overview
9.2.2 Europe Machine Learning Chip Market Forecasts and Analysis
9.2.3 Europe Machine Learning Chip Market Forecasts and Analysis - By Chip Type
9.2.4 Europe Machine Learning Chip Market Forecasts and Analysis - By Industry
9.2.5 Europe Machine Learning Chip Market Forecasts and Analysis - By Countries
9.2.5.1 Germany Machine Learning Chip Market
9.2.5.1.1 Germany Machine Learning Chip Market by Chip Type
9.2.5.1.2 Germany Machine Learning Chip Market by Industry
9.2.5.2 France Machine Learning Chip Market
9.2.5.2.1 France Machine Learning Chip Market by Chip Type
9.2.5.2.2 France Machine Learning Chip Market by Industry
9.2.5.3 Italy Machine Learning Chip Market
9.2.5.3.1 Italy Machine Learning Chip Market by Chip Type
9.2.5.3.2 Italy Machine Learning Chip Market by Industry
9.2.5.4 United Kingdom Machine Learning Chip Market
9.2.5.4.1 United Kingdom Machine Learning Chip Market by Chip Type
9.2.5.4.2 United Kingdom Machine Learning Chip Market by Industry
9.2.5.5 Russia Machine Learning Chip Market
9.2.5.5.1 Russia Machine Learning Chip Market by Chip Type
9.2.5.5.2 Russia Machine Learning Chip Market by Industry
9.2.5.6 Rest of Europe Machine Learning Chip Market
9.2.5.6.1 Rest of Europe Machine Learning Chip Market by Chip Type
9.2.5.6.2 Rest of Europe Machine Learning Chip Market by Industry
9.3. ASIA-PACIFIC
9.3.1 Asia-Pacific Machine Learning Chip Market Overview
9.3.2 Asia-Pacific Machine Learning Chip Market Forecasts and Analysis
9.3.3 Asia-Pacific Machine Learning Chip Market Forecasts and Analysis - By Chip Type
9.3.4 Asia-Pacific Machine Learning Chip Market Forecasts and Analysis - By Industry
9.3.5 Asia-Pacific Machine Learning Chip Market Forecasts and Analysis - By Countries
9.3.5.1 Australia Machine Learning Chip Market
9.3.5.1.1 Australia Machine Learning Chip Market by Chip Type
9.3.5.1.2 Australia Machine Learning Chip Market by Industry
9.3.5.2 China Machine Learning Chip Market
9.3.5.2.1 China Machine Learning Chip Market by Chip Type
9.3.5.2.2 China Machine Learning Chip Market by Industry
9.3.5.3 India Machine Learning Chip Market
9.3.5.3.1 India Machine Learning Chip Market by Chip Type
9.3.5.3.2 India Machine Learning Chip Market by Industry
9.3.5.4 Japan Machine Learning Chip Market
9.3.5.4.1 Japan Machine Learning Chip Market by Chip Type
9.3.5.4.2 Japan Machine Learning Chip Market by Industry
9.3.5.5 South Korea Machine Learning Chip Market
9.3.5.5.1 South Korea Machine Learning Chip Market by Chip Type
9.3.5.5.2 South Korea Machine Learning Chip Market by Industry
9.3.5.6 Rest of Asia-Pacific Machine Learning Chip Market
9.3.5.6.1 Rest of Asia-Pacific Machine Learning Chip Market by Chip Type
9.3.5.6.2 Rest of Asia-Pacific Machine Learning Chip Market by Industry
9.4. MIDDLE EAST AND AFRICA
9.4.1 Middle East and Africa Machine Learning Chip Market Overview
9.4.2 Middle East and Africa Machine Learning Chip Market Forecasts and Analysis
9.4.3 Middle East and Africa Machine Learning Chip Market Forecasts and Analysis - By Chip Type
9.4.4 Middle East and Africa Machine Learning Chip Market Forecasts and Analysis - By Industry
9.4.5 Middle East and Africa Machine Learning Chip Market Forecasts and Analysis - By Countries
9.4.5.1 South Africa Machine Learning Chip Market
9.4.5.1.1 South Africa Machine Learning Chip Market by Chip Type
9.4.5.1.2 South Africa Machine Learning Chip Market by Industry
9.4.5.2 Saudi Arabia Machine Learning Chip Market
9.4.5.2.1 Saudi Arabia Machine Learning Chip Market by Chip Type
9.4.5.2.2 Saudi Arabia Machine Learning Chip Market by Industry
9.4.5.3 U.A.E Machine Learning Chip Market
9.4.5.3.1 U.A.E Machine Learning Chip Market by Chip Type
9.4.5.3.2 U.A.E Machine Learning Chip Market by Industry
9.4.5.4 Rest of Middle East and Africa Machine Learning Chip Market
9.4.5.4.1 Rest of Middle East and Africa Machine Learning Chip Market by Chip Type
9.4.5.4.2 Rest of Middle East and Africa Machine Learning Chip Market by Industry
9.5. SOUTH AND CENTRAL AMERICA
9.5.1 South and Central America Machine Learning Chip Market Overview
9.5.2 South and Central America Machine Learning Chip Market Forecasts and Analysis
9.5.3 South and Central America Machine Learning Chip Market Forecasts and Analysis - By Chip Type
9.5.4 South and Central America Machine Learning Chip Market Forecasts and Analysis - By Industry
9.5.5 South and Central America Machine Learning Chip Market Forecasts and Analysis - By Countries
9.5.5.1 Brazil Machine Learning Chip Market
9.5.5.1.1 Brazil Machine Learning Chip Market by Chip Type
9.5.5.1.2 Brazil Machine Learning Chip Market by Industry
9.5.5.2 Argentina Machine Learning Chip Market
9.5.5.2.1 Argentina Machine Learning Chip Market by Chip Type
9.5.5.2.2 Argentina Machine Learning Chip Market by Industry
9.5.5.3 Rest of South and Central America Machine Learning Chip Market
9.5.5.3.1 Rest of South and Central America Machine Learning Chip Market by Chip Type
9.5.5.3.2 Rest of South and Central America Machine Learning Chip Market by Industry
10. INDUSTRY LANDSCAPE
10.1. MERGERS AND ACQUISITIONS
10.2. AGREEMENTS, COLLABORATIONS AND JOIN VENTURES
10.3. NEW PRODUCT LAUNCHES
10.4. EXPANSIONS AND OTHER STRATEGIC DEVELOPMENTS
11. MACHINE LEARNING CHIP MARKET, KEY COMPANY PROFILES
11.1. ADVANCED MICRO DEVICES INC.
11.1.1. Key Facts
11.1.2. Business Description
11.1.3. Products and Services
11.1.4. Financial Overview
11.1.5. SWOT Analysis
11.1.6. Key Developments
11.2. ALPHABET INC.
11.2.1. Key Facts
11.2.2. Business Description
11.2.3. Products and Services
11.2.4. Financial Overview
11.2.5. SWOT Analysis
11.2.6. Key Developments
11.3. AMAZON WEB SERVICES, INC.
11.3.1. Key Facts
11.3.2. Business Description
11.3.3. Products and Services
11.3.4. Financial Overview
11.3.5. SWOT Analysis
11.3.6. Key Developments
11.4. BITMAIN TECHNOLOGY HOLDING COMPANY
11.4.1. Key Facts
11.4.2. Business Description
11.4.3. Products and Services
11.4.4. Financial Overview
11.4.5. SWOT Analysis
11.4.6. Key Developments
11.5. CEREBRAS SYSTEMS
11.5.1. Key Facts
11.5.2. Business Description
11.5.3. Products and Services
11.5.4. Financial Overview
11.5.5. SWOT Analysis
11.5.6. Key Developments
11.6. INTEL CORPORATION
11.6.1. Key Facts
11.6.2. Business Description
11.6.3. Products and Services
11.6.4. Financial Overview
11.6.5. SWOT Analysis
11.6.6. Key Developments
11.7. NVIDIA CORPORATION
11.7.1. Key Facts
11.7.2. Business Description
11.7.3. Products and Services
11.7.4. Financial Overview
11.7.5. SWOT Analysis
11.7.6. Key Developments
11.8. QUALCOMM TECHNOLOGIES, INC.
11.8.1. Key Facts
11.8.2. Business Description
11.8.3. Products and Services
11.8.4. Financial Overview
11.8.5. SWOT Analysis
11.8.6. Key Developments
11.9. SAMSUNG ELECTRONICS
11.9.1. Key Facts
11.9.2. Business Description
11.9.3. Products and Services
11.9.4. Financial Overview
11.9.5. SWOT Analysis
11.9.6. Key Developments
11.10. XILINX
11.10.1. Key Facts
11.10.2. Business Description
11.10.3. Products and Services
11.10.4. Financial Overview
11.10.5. SWOT Analysis
11.10.6. Key Developments
12. APPENDIX
12.1. ABOUT THE INSIGHT PARTNERS
12.2. GLOSSARY OF TERMS
The List of Companies
1. Advanced Micro Devices Inc.
2. Alphabet Inc.
3. Amazon Web Services, Inc.
4. Bitmain Technology Holding Company
5. Cerebras Systems
6. Intel Corporation
7. Nvidia Corporation
8. Qualcomm Technologies, Inc.
9. Samsung Electronics
10. Xilinx
The Insight Partners performs research in 4 major stages: Data Collection & Secondary Research, Primary Research, Data Analysis and Data Triangulation & Final Review.
- Data Collection and Secondary Research:
As a market research and consulting firm operating from a decade, we have published many reports and advised several clients across the globe. First step for any study will start with an assessment of currently available data and insights from existing reports. Further, historical and current market information is collected from Investor Presentations, Annual Reports, SEC Filings, etc., and other information related to company’s performance and market positioning are gathered from Paid Databases (Factiva, Hoovers, and Reuters) and various other publications available in public domain.
Several associations trade associates, technical forums, institutes, societies and organizations are accessed to gain technical as well as market related insights through their publications such as research papers, blogs and press releases related to the studies are referred to get cues about the market. Further, white papers, journals, magazines, and other news articles published in the last 3 years are scrutinized and analyzed to understand the current market trends.
- Primary Research:
The primarily interview analysis comprise of data obtained from industry participants interview and answers to survey questions gathered by in-house primary team.
For primary research, interviews are conducted with industry experts/CEOs/Marketing Managers/Sales Managers/VPs/Subject Matter Experts from both demand and supply side to get a 360-degree view of the market. The primary team conducts several interviews based on the complexity of the markets to understand the various market trends and dynamics which makes research more credible and precise.
A typical research interview fulfils the following functions:
- Provides first-hand information on the market size, market trends, growth trends, competitive landscape, and outlook
- Validates and strengthens in-house secondary research findings
- Develops the analysis team’s expertise and market understanding
Primary research involves email interactions and telephone interviews for each market, category, segment, and sub-segment across geographies. The participants who typically take part in such a process include, but are not limited to:
- Industry participants: VPs, business development managers, market intelligence managers and national sales managers
- Outside experts: Valuation experts, research analysts and key opinion leaders specializing in the electronics and semiconductor industry.
Below is the breakup of our primary respondents by company, designation, and region:
Once we receive the confirmation from primary research sources or primary respondents, we finalize the base year market estimation and forecast the data as per the macroeconomic and microeconomic factors assessed during data collection.
- Data Analysis:
Once data is validated through both secondary as well as primary respondents, we finalize the market estimations by hypothesis formulation and factor analysis at regional and country level.
- 3.1 Macro-Economic Factor Analysis:
We analyse macroeconomic indicators such the gross domestic product (GDP), increase in the demand for goods and services across industries, technological advancement, regional economic growth, governmental policies, the influence of COVID-19, PEST analysis, and other aspects. This analysis aids in setting benchmarks for various nations/regions and approximating market splits. Additionally, the general trend of the aforementioned components aid in determining the market's development possibilities.
- 3.2 Country Level Data:
Various factors that are especially aligned to the country are taken into account to determine the market size for a certain area and country, including the presence of vendors, such as headquarters and offices, the country's GDP, demand patterns, and industry growth. To comprehend the market dynamics for the nation, a number of growth variables, inhibitors, application areas, and current market trends are researched. The aforementioned elements aid in determining the country's overall market's growth potential.
- 3.3 Company Profile:
The “Table of Contents” is formulated by listing and analyzing more than 25 - 30 companies operating in the market ecosystem across geographies. However, we profile only 10 companies as a standard practice in our syndicate reports. These 10 companies comprise leading, emerging, and regional players. Nonetheless, our analysis is not restricted to the 10 listed companies, we also analyze other companies present in the market to develop a holistic view and understand the prevailing trends. The “Company Profiles” section in the report covers key facts, business description, products & services, financial information, SWOT analysis, and key developments. The financial information presented is extracted from the annual reports and official documents of the publicly listed companies. Upon collecting the information for the sections of respective companies, we verify them via various primary sources and then compile the data in respective company profiles. The company level information helps us in deriving the base number as well as in forecasting the market size.
- 3.4 Developing Base Number:
Aggregation of sales statistics (2020-2022) and macro-economic factor, and other secondary and primary research insights are utilized to arrive at base number and related market shares for 2022. The data gaps are identified in this step and relevant market data is analyzed, collected from paid primary interviews or databases. On finalizing the base year market size, forecasts are developed on the basis of macro-economic, industry and market growth factors and company level analysis.
- Data Triangulation and Final Review:
The market findings and base year market size calculations are validated from supply as well as demand side. Demand side validations are based on macro-economic factor analysis and benchmarks for respective regions and countries. In case of supply side validations, revenues of major companies are estimated (in case not available) based on industry benchmark, approximate number of employees, product portfolio, and primary interviews revenues are gathered. Further revenue from target product/service segment is assessed to avoid overshooting of market statistics. In case of heavy deviations between supply and demand side values, all thes steps are repeated to achieve synchronization.
We follow an iterative model, wherein we share our research findings with Subject Matter Experts (SME’s) and Key Opinion Leaders (KOLs) until consensus view of the market is not formulated – this model negates any drastic deviation in the opinions of experts. Only validated and universally acceptable research findings are quoted in our reports.
We have important check points that we use to validate our research findings – which we call – data triangulation, where we validate the information, we generate from secondary sources with primary interviews and then we re-validate with our internal data bases and Subject matter experts. This comprehensive model enables us to deliver high quality, reliable data in shortest possible time.
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