[250 Pages Report] Newly-released Automatic Content Recognition Market analysis report by Future Market Insights shows that global sales of the Automatic Content Recognition Market in 2021 were held at US$ 2 Billion. With 13.1% projected growth from 2022 to 2032, the market is expected to reach a valuation of US$ 7.9 Billion by 2032. Media & Entertainment is expected to be the highest revenue-generating segment, projected to grow at a CAGR of over 12.9% from 2022 to 2032.
| Attributes | Details |
|---|---|
| Automatic Content Recognition Market CAGR (2022 to 2032) | 13.1% |
| Automatic Content Recognition Market Size (2022) | US$ 2.3 Billion |
| Automatic Content Recognition Market Size (2032) | US$ 7.9 Billion |
| USA Market CAGR (2022 to 2032) | 11.9% |
| Key Companies Covered |
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As per the Global Automatic Content Recognition Market research by Future Market Insights - a market research and competitive intelligence provider, historically, from 2017 to 2021, the market value of the Automatic Content Recognition Market increased at around 16.6% CAGR. Global demand for automatic content identification solutions has increased as a result of the advent of automatic content recognition technologies in smart TVs and smartphones.
The increasing use of automatic content identification by media companies for audience assessment and broadcast monitoring is also expected to fuel the growth of the global market for autonomous content recognition. Due to the rise of smart devices such as smartphones, televisions, and wearables, there is a growing demand for content identification, recognition, and augmentation among enterprises and end users.
The usage of automatic content recognition systems and services by businesses led to a range of ways in which life hacking has become simpler. By launching music recognition software that combined digital fingerprinting technology, Shazam established the trend. Many companies adopted this strategy and released second-screen apps for smart TVs and smartphones that were based on automatic content identification. The popularity of on-demand video platforms like Netflix, Hotstar, YouTube, and Amazon Prime has increased the need for automatic content detection tools and services.
Smart TVs provide a wide range of increased processing and networking capabilities. Advanced functions on smart TVs include searching, talking, browsing, sharing, upgrading, and downloading. TV viewership in India climbed by 12% in 2018, according to the Broadcast Audience Research Council (BARC). Smartphones account for 70% of total online traffic; hence, content reorganization is critical for material synchronization and preserving uniformity and consistency of data.
The rising popularity of social media on smartphones is increasing demand for the Automatic Content Recognition market. The growing usage of second-screen devices, such as smartphones and wearables, for tracking unlawful users is propelling the market growth. According to a Netflix poll, more than 70% of its programs are viewed on smartphones and tablets rather than TVs.
The expanding number of viewers on YouTube and other social media sites necessitates a review of the overall number of viewers captured, resulting in a necessity for automatic content identification technologies. For example, in 2018, 2.32 Billion people utilized Facebook on a daily basis. The market is also projected to be driven by several manufacturers delivering innovative technologies for Automatic Content Recognition. Zeitera, a pioneer in digital audio and video fingerprinting technology, has developed a patented audio-video content identification and search system for smart TVs, smartphones, and tablets.
Principal Consultant
Data is a critical aspect that most businesses struggle to manage. It serves as the foundation of automatic content recognition technologies. Due to the inability to manage exabytes and petabytes of data, the risk of security breaches and data loss has increased. In order to avert breaches, businesses will need more comprehensive security and privacy protection as IoT becomes more ubiquitous.
The issue of security jeopardizes the advancement of digitalization. In today's competitive sector, marketing teams require real-time and secure data to create an amazing customer experience. Data is collected and virtually measured by organizations through a variety of touchpoints. The data types include public information, huge data, and customer-supplied microdata.
Watermarking and digital fingerprinting, two of the most often utilized automatic content identification technologies, have a few drawbacks that cause serious financial issues. The usage of digital fingerprinting has drawbacks in that users can change the data. However, there are even more fundamental issues with digital watermarking that provide significant difficulties for businesses using it for automatic content recognition. Including the watermark in the material adds a diversion to the media file in addition to altering the properties of the original file. As opposed to fingerprinting, watermarking offers a more secure method of identification.
During the projection period, North America is expected to be the largest Automatic Content Recognition Market. Every day, around 3.5 billion individuals use search engines to find information. As a result, Automatic Content Recognition is required for content identification and augmentation.
The improved healthcare infrastructure influences the demand for audio and visual identification technology. Furthermore, the expanding automobile sector in the USA and Canada is influencing the demand for speech recognition to improve communication systems integrated into automobiles, such as phone calls, audio devices, and navigation. In 2016, Canada's automotive industry, for example, featured 1,085 car manufacturers and 1,275 manufacturing facilities. As a result, the demand for voice recognition to do several automated tasks in a vehicle has increased.
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The USA is expected to account for the highest market of US$ 2.5 Billion by the end of 2032. Also, the market in the country is projected to account for an absolute dollar growth of US$ 1.7 Billion. In the USA, face, and speech recognition is utilized to collect and store data in electronic health records. As a result, various manufacturers developing software for the healthcare sector are expected to support market growth. For example, DrChrono released an Electronic Health Record in November 2017 that uses Apple's facial recognition to preserve electronic records. Drchrono contains around 10 Million patient medical records, which represents approximately 3% of the US population.
Market revenue through media and entertainment is forecasted to grow at a CAGR of over 12.9% from 2022 to 2032. The highest market share is served by media and entertainment. Automatic Content Recognition technology captures a few pixels of whatever the user is currently watching on TV. The data is subsequently transferred to the TV manufacturer's Automatic Content Recognition tracking program, which analyzes a variety of parameters. Automatic Content Recognition technology is available on websites where audiovisual content has already been verified. This can positively affect the development of applications targeting this market. Cell phones are becoming increasingly popular. For example, about 2.7 Billion smartphone users globally increased demand for the automatic content recognition market in 2018.
Various suppliers offer music-listening programs that synchronize the content using Automatic Content Recognition. Mufin GmbH, for example, is a prominent provider of automatic content recognition for Android apps when listening to music. Second-screen devices such as smartphones, tablets, and laptops are used by consumers to view TV shows and listen to music. Automatic Content Recognition synchronizes data and sends real-time information to the second screen user, paving the way for an automated content recognition market. According to Netflix survey data, more than 24% of the population watches television on a second screen.
Players in the market are constantly developing improved analytical solutions as well as extending their product offerings. The companies in the Automatic Content Recognition Market are focused on their alliances, technology collaborations, and product launch strategies.
Some of the recent developments of key Automatic Content Recognition providers are as follows:
Similarly, recent developments related to companies in Automatic Content Recognition Market have been tracked by the team at Future Market Insights, which are available in the full report.
The global Automatic Content Recognition Market is worth more than US$ 2.3 Billion at present.
The value of the Automatic Content Recognition Market is projected to increase at a CAGR of around 13.1% from 2022 to 2032.
The value of the Automatic Content Recognition Market increased at a CAGR of around 16.6% from 2017 to 2021.
The global demand for Automatic Content Recognition is being shaped by a considerable increase in the use of on-demand video platforms.
Automatic Content Recognition market growth in the USA is projected to expand at a CAGR of around 11.9% from 2022 to 2032.
Automatic Content Recognition market growth in China is projected to expand at a CAGR of around 14.1% from 2022 to 2032.
While the market in South Korea is expected to grow at nearly 13.6%, the market in Japan is projected to register a CAGR of nearly 12.5% from 2022 to 2032.
1. Executive Summary | Automatic Content Recognition Market
1.1. Global Market Outlook
1.2. Demand-side Trends
1.3. Supply-side Trends
1.4. Technology Roadmap Analysis
1.5. Analysis and Recommendations
2. Market Overview
2.1. Market Coverage / Taxonomy
2.2. Market Definition / Scope / Limitations
3. Market Background
3.1. Market Dynamics
3.1.1. Drivers
3.1.2. Restraints
3.1.3. Opportunity
3.1.4. Trends
3.2. Scenario Forecast
3.2.1. Demand in Optimistic Scenario
3.2.2. Demand in Likely Scenario
3.2.3. Demand in Conservative Scenario
3.3. Opportunity Map Analysis
3.4. Investment Feasibility Matrix
3.5. PESTLE and Porter’s Analysis
3.6. Regulatory Landscape
3.6.1. By Key Regions
3.6.2. By Key Countries
3.7. Regional Parent Market Outlook
4. Global Market Analysis 2017 to 2021 and Forecast, 2022 to 2032
4.1. Historical Market Size Value (US$ Million) Analysis, 2017 to 2021
4.2. Current and Future Market Size Value (US$ Million) Projections, 2022 to 2032
4.2.1. Y-o-Y Growth Trend Analysis
4.2.2. Absolute $ Opportunity Analysis
5. Global Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Solution
5.1. Introduction / Key Findings
5.2. Historical Market Size Value (US$ Million) Analysis By Solution, 2017 to 2021
5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Solution, 2022 to 2032
5.3.1. Real-time Content Analytics
5.3.2. Voice & Speech Recognition
5.3.3. Security & Copyright Management
5.3.4. Data Management & Metadata
5.3.5. Other
5.4. Y-o-Y Growth Trend Analysis By Solution, 2017 to 2021
5.5. Absolute $ Opportunity Analysis By Solution, 2022 to 2032
6. Global Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By End-User Industry
6.1. Introduction / Key Findings
6.2. Historical Market Size Value (US$ Million) Analysis By End-User Industry, 2017 to 2021
6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By End-User Industry, 2022 to 2032
6.3.1. IT & Telecommunication
6.3.2. Consumer Electronics
6.3.3. Media & Entertainment
6.3.4. Healthcare
6.3.5. Other
6.4. Y-o-Y Growth Trend Analysis By End-User Industry, 2017 to 2021
6.5. Absolute $ Opportunity Analysis By End-User Industry, 2022 to 2032
7. Global Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Region
7.1. Introduction
7.2. Historical Market Size Value (US$ Million) Analysis By Region, 2017 to 2021
7.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2022 to 2032
7.3.1. North America
7.3.2. Latin America
7.3.3. Europe
7.3.4. Asia Pacific
7.3.5. Middle East and Africa (MEA)
7.4. Market Attractiveness Analysis By Region
8. North America Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Country
8.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2017 to 2021
8.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2022 to 2032
8.2.1. By Country
8.2.1.1. United States of America
8.2.1.2. Canada
8.2.2. By Solution
8.2.3. By End-User Industry
8.3. Market Attractiveness Analysis
8.3.1. By Country
8.3.2. By Solution
8.3.3. By End-User Industry
8.4. Key Takeaways
9. Latin America Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Country
9.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2017 to 2021
9.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2022 to 2032
9.2.1. By Country
9.2.1.1. Brazil
9.2.1.2. Mexico
9.2.1.3. Rest of Latin America
9.2.2. By Solution
9.2.3. By End-User Industry
9.3. Market Attractiveness Analysis
9.3.1. By Country
9.3.2. By Solution
9.3.3. By End-User Industry
9.4. Key Takeaways
10. Europe Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Country
10.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2017 to 2021
10.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2022 to 2032
10.2.1. By Country
10.2.1.1. Germany
10.2.1.2. United Kingdom
10.2.1.3. France
10.2.1.4. Spain
10.2.1.5. Italy
10.2.1.6. Rest of Europe
10.2.2. By Solution
10.2.3. By End-User Industry
10.3. Market Attractiveness Analysis
10.3.1. By Country
10.3.2. By Solution
10.3.3. By End-User Industry
10.4. Key Takeaways
11. Asia Pacific Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Country
11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2017 to 2021
11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2022 to 2032
11.2.1. By Country
11.2.1.1. China
11.2.1.2. Japan
11.2.1.3. South Korea
11.2.1.4. India
11.2.1.5. Malaysia
11.2.1.6. Singapore
11.2.1.7. Australia
11.2.1.8. New Zealand
11.2.1.9. Rest of APAC
11.2.2. By Solution
11.2.3. By End-User Industry
11.3. Market Attractiveness Analysis
11.3.1. By Country
11.3.2. By Solution
11.3.3. By End-User Industry
11.4. Key Takeaways
12. MEA Market Analysis 2017 to 2021 and Forecast 2022 to 2032, By Country
12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2017 to 2021
12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2022 to 2032
12.2.1. By Country
12.2.1.1. GCC Countries
12.2.1.2. South Africa
12.2.1.3. Israel
12.2.1.4. Rest of MEA
12.2.2. By Solution
12.2.3. By End-User Industry
12.3. Market Attractiveness Analysis
12.3.1. By Country
12.3.2. By Solution
12.3.3. By End-User Industry
12.4. Key Takeaways
13. Key Countries Market Analysis
13.1. United States of America
13.1.1. Pricing Analysis
13.1.2. Market Share Analysis, 2021
13.1.2.1. By Solution
13.1.2.2. By End-User Industry
13.2. Canada
13.2.1. Pricing Analysis
13.2.2. Market Share Analysis, 2021
13.2.2.1. By Solution
13.2.2.2. By End-User Industry
13.3. Brazil
13.3.1. Pricing Analysis
13.3.2. Market Share Analysis, 2021
13.3.2.1. By Solution
13.3.2.2. By End-User Industry
13.4. Mexico
13.4.1. Pricing Analysis
13.4.2. Market Share Analysis, 2021
13.4.2.1. By Solution
13.4.2.2. By End-User Industry
13.5. Germany
13.5.1. Pricing Analysis
13.5.2. Market Share Analysis, 2021
13.5.2.1. By Solution
13.5.2.2. By End-User Industry
13.6. United Kingdom
13.6.1. Pricing Analysis
13.6.2. Market Share Analysis, 2021
13.6.2.1. By Solution
13.6.2.2. By End-User Industry
13.7. France
13.7.1. Pricing Analysis
13.7.2. Market Share Analysis, 2021
13.7.2.1. By Solution
13.7.2.2. By End-User Industry
13.8. Spain
13.8.1. Pricing Analysis
13.8.2. Market Share Analysis, 2021
13.8.2.1. By Solution
13.8.2.2. By End-User Industry
13.9. Italy
13.9.1. Pricing Analysis
13.9.2. Market Share Analysis, 2021
13.9.2.1. By Solution
13.9.2.2. By End-User Industry
13.10. China
13.10.1. Pricing Analysis
13.10.2. Market Share Analysis, 2021
13.10.2.1. By Solution
13.10.2.2. By End-User Industry
13.11. Japan
13.11.1. Pricing Analysis
13.11.2. Market Share Analysis, 2021
13.11.2.1. By Solution
13.11.2.2. By End-User Industry
13.12. South Korea
13.12.1. Pricing Analysis
13.12.2. Market Share Analysis, 2021
13.12.2.1. By Solution
13.12.2.2. By End-User Industry
13.13. Malaysia
13.13.1. Pricing Analysis
13.13.2. Market Share Analysis, 2021
13.13.2.1. By Solution
13.13.2.2. By End-User Industry
13.14. Singapore
13.14.1. Pricing Analysis
13.14.2. Market Share Analysis, 2021
13.14.2.1. By Solution
13.14.2.2. By End-User Industry
13.15. Australia
13.15.1. Pricing Analysis
13.15.2. Market Share Analysis, 2021
13.15.2.1. By Solution
13.15.2.2. By End-User Industry
13.16. New Zealand
13.16.1. Pricing Analysis
13.16.2. Market Share Analysis, 2021
13.16.2.1. By Solution
13.16.2.2. By End-User Industry
13.17. GCC Countries
13.17.1. Pricing Analysis
13.17.2. Market Share Analysis, 2021
13.17.2.1. By Solution
13.17.2.2. By End-User Industry
13.18. South Africa
13.18.1. Pricing Analysis
13.18.2. Market Share Analysis, 2021
13.18.2.1. By Solution
13.18.2.2. By End-User Industry
13.19. Israel
13.19.1. Pricing Analysis
13.19.2. Market Share Analysis, 2021
13.19.2.1. By Solution
13.19.2.2. By End-User Industry
14. Market Structure Analysis
14.1. Competition Dashboard
14.2. Competition Benchmarking
14.3. Market Share Analysis of Top Players
14.3.1. By Regional
14.3.2. By Solution
14.3.3. By End-User Industry
15. Competition Analysis
15.1. Competition Deep Dive
15.1.1. Audible Magic
15.1.1.1. Overview
15.1.1.2. Product Portfolio
15.1.1.3. Profitability by Market Segments
15.1.1.4. Sales Footprint
15.1.1.5. Strategy Overview
15.1.1.5.1. Marketing Strategy
15.1.2. Digimarc
15.1.2.1. Overview
15.1.2.2. Product Portfolio
15.1.2.3. Profitability by Market Segments
15.1.2.4. Sales Footprint
15.1.2.5. Strategy Overview
15.1.2.5.1. Marketing Strategy
15.1.3. ArcSoft
15.1.3.1. Overview
15.1.3.2. Product Portfolio
15.1.3.3. Profitability by Market Segments
15.1.3.4. Sales Footprint
15.1.3.5. Strategy Overview
15.1.3.5.1. Marketing Strategy
15.1.4. ACRCloud
15.1.4.1. Overview
15.1.4.2. Product Portfolio
15.1.4.3. Profitability by Market Segments
15.1.4.4. Sales Footprint
15.1.4.5. Strategy Overview
15.1.4.5.1. Marketing Strategy
15.1.5. Apple
15.1.5.1. Overview
15.1.5.2. Product Portfolio
15.1.5.3. Profitability by Market Segments
15.1.5.4. Sales Footprint
15.1.5.5. Strategy Overview
15.1.5.5.1. Marketing Strategy
15.1.6. Nuance Communications
15.1.6.1. Overview
15.1.6.2. Product Portfolio
15.1.6.3. Profitability by Market Segments
15.1.6.4. Sales Footprint
15.1.6.5. Strategy Overview
15.1.6.5.1. Marketing Strategy
15.1.7. Google
15.1.7.1. Overview
15.1.7.2. Product Portfolio
15.1.7.3. Profitability by Market Segments
15.1.7.4. Sales Footprint
15.1.7.5. Strategy Overview
15.1.7.5.1. Marketing Strategy
15.1.8. VoiceBase
15.1.8.1. Overview
15.1.8.2. Product Portfolio
15.1.8.3. Profitability by Market Segments
15.1.8.4. Sales Footprint
15.1.8.5. Strategy Overview
15.1.8.5.1. Marketing Strategy
15.1.9. DataScouting
15.1.9.1. Overview
15.1.9.2. Product Portfolio
15.1.9.3. Profitability by Market Segments
15.1.9.4. Sales Footprint
15.1.9.5. Strategy Overview
15.1.9.5.1. Marketing Strategy
15.1.10. Verbit
15.1.10.1. Overview
15.1.10.2. Product Portfolio
15.1.10.3. Profitability by Market Segments
15.1.10.4. Sales Footprint
15.1.10.5. Strategy Overview
15.1.10.5.1. Marketing Strategy
15.1.11. Microsoft
15.1.11.1. Overview
15.1.11.2. Product Portfolio
15.1.11.3. Profitability by Market Segments
15.1.11.4. Sales Footprint
15.1.11.5. Strategy Overview
15.1.11.5.1. Marketing Strategy
15.1.12. KT Corporation
15.1.12.1. Overview
15.1.12.2. Product Portfolio
15.1.12.3. Profitability by Market Segments
15.1.12.4. Sales Footprint
15.1.12.5. Strategy Overview
15.1.12.5.1. Marketing Strategy
16. Assumptions & Acronyms Used
17. Research Methodology
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Automatic Content Recognition Market
