Global Natural Language Processing Market by Offerings (Hardware, Software and Services); By Technologies (Pattern and Image Recognition, Interactive Voice Response (IVR), Optical Character Recognition (OCR), Text Analytics, Speech Analytics, Classification and Categorization, Auto Coding, Professional Services and Support and Maintenance Services); By Verticals (Healthcare and Lifesciences, Retail and Consumer Goods, High Tech and Electronics, Media and Entertainment, BFSI, Manufacturing and Research and Education); By Regions (North America, EMEA, Latin America and APAC); Drivers, Opportunities, Restraints, Trends, and Forecast to 2023
In the present digitized world, 80% of the data generated is unstructured. Organizations are using natural language processing technology to unravel the meaning of such data to leverage business strategies and opportunities. A myriad of unstructured data is available online in the form of audio content, visual content and social footprints. Data has now become an asset for organizations. We have arrived into an era of automation of tedious cognitive tasks in businesses. Human beings fundamentally think, communicate and understand in an unstructured manner. Majority of the workflow in business and personal domain are either entirely controlled by humans or involves a human layer that converts the real-world inputs to computer inputs. NLP is gradually becoming ubiquitous in business enterprises and it has a wide array of functions ranging from chatbots and digital assistants such as Google Home, Siri and Alexa to compliance monitoring functions, business intelligence and analytics. Queries, email communication, videos, social media, support requests, customer reviews and so on are sources of useful insights that can be used to generate significant business value.
Natural language processing (NLP), also known as computational linguistics is an amalgamation of artificial intelligence, machine learning and linguistics. NLP is one of the most leveraged technologies in artificial intelligence and the growth of the technology is being propelled by the growth of related technologies such as deep learning and cognitive computing. NLP combines artificial intelligence, computer science and computational linguistics to help machines in reading texts by simulating the human ability of understanding languages. The technology offers competitive advantage to businesses in legal, media and digital ad services. Automotive, healthcare, education and the retail sectors are extensively investing in the technology, as NLP is continuously evolving and is capable of interpreting and adapting to a wide variety of human languages. Sentiment analysis is largely used in web and social media monitoring as it gives businesses access to the opinions of end-users about the organization and its services. Useful insights about customer preferences and attitudes can be obtained from the emoticons in social media. The use cases for natural language processing is diverse, covering customer service, autonomous vehicles, healthcare, banking, financial services and insurance (BFSI), manufacturing, retail and consumer goods, media and entertainment, research, education,high tech and electronics.
Technological mainstays namely Google, IBM, Microsoft and others are making significant investment in the field of natural language processing. NLP and text analytics have a major role to play in social media sentiment analysis, business intelligence, data governance, cognitive computing and business intelligence. Text analytics is a subset of NLP and is one amongst the two analytics options that NLP offers, alongside speech analytics. NLP helps in establishing relationships in documents, carrying out search, understanding the demarcations of sentences and phrases and determining names and places through semantic technologies. In the context of text analytics, NLP helps in identifying aspects of regulatory compliance, categorization, sentiment analysis and text clustering. NLP solutions are either statistics based, rule based or a hybrid.
According to Infoholic Research, the Global Natural Language Processing market is expected to grow at a CAGR of 18.78% during the forecast period 2017–2023. The market is driven by factors such as the availability of a high volume of unstructured data, enhanced utility of smart devices, increased use of NLP in call centers, increased demand for better customer experience and expansive application areas. The future potential of the market is promising owing to opportunities such as developments in big data technologies, democratization of data, smart search and the emergence of human-like virtual assistants. The market growth is curbed by restraining factors such as difficulties in bridging gaps between humans and machines, training of researchers and loss of context and meaning.
Segmentation by Offerings
The market has been segmented and analyzed by the following offerings: Software, Hardware and Services.
Segmentation by Technologies
The market has been segmented and analyzed by the following technologies: Pattern and Image Recognition, Interactive Voice Response (IVR), Optical Character Recognition (OCR), Text Analytics, Speech Analytics, Classification and Categorization, Auto Coding, Professional Services and Support and Maintenance Services.
Segmentation by Regions
The market has been segmented and analyzed by the following regions: North America, EMEA, APAC and Latin America.
Segmentation by Verticals
The market has been segmented and analyzed by the following verticals: Healthcare and Lifesciences, Retail and Consumer Goods, High Tech and Electronics, Media and Entertainment, BFSI, Manufacturing, and Research and Education.
The study covers and analyses the “Global Natural Language Processing Market”. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies relevant to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.
- The report covers drivers, restraints, and opportunities (DRO) affecting the market growth during the forecast period (2017–2023).
- It also contains an analysis of vendor profiles, which include financial health, business units, key business priorities, SWOT, strategies, and views.
- The report covers competitive landscape, which includes M&A, joint ventures and collaborations, and competitor comparison analysis.
- In the vendor profile section, for the companies that are privately held, financial information and revenue of segments will be limited.
- Industry Outlook
1.1.1. Industry Overview
1.1.2. Industry Trends
1.1.3. PEST Analysis
- Report Outline
2.1.1. Report Scope
2.1.2. Report Summary
2.1.3. Research Methodology
2.1.4. Report Assumptions
- Market Snapshot
3.1.1. Total Addressable Market
3.1.2. Segmented Addressable Market
3.1.3. Related Markets
3.1.4. Machine Learning Market
3.1.5. Artificial Intelligence Market
- Market Outlook
4.1.2. Regulatory Bodies and Standards
4.1.3. Porter 5 (Five) Forces
- Market Characteristics
5.1.1. Use Cases of Natural Language Processing
5.1.2. Market Segmentation
5.1.3. Market Dynamics
22.214.171.124. High Volume of Unstructured Data
126.96.36.199. Enhanced utility of smart devices
188.8.131.52. Increased use of NLP in customer call centers
184.108.40.206. Increased demand for better customer experience
220.127.116.11. Expanding application areas
18.104.22.168. Bridging the gap between humans and machines
22.214.171.124. Training of researchers
126.96.36.199. Loss of context and meaning
188.8.131.52. Development in Big Data Technologies
184.108.40.206. NLP will democratize data
220.127.116.11. Smart Search
18.104.22.168. Emergence of human like virtual assistants
5.1.7. DRO – Impact Analysis
- Trends, Roadmap, and Projects
6.1.1. Market Trends & Impact
6.1.2. Technology Roadmap
- Geographic Segmentation: Market Size and Analysis
22.214.171.124. North America
126.96.36.199. The UK
7.1.3. Asia Pacific
7.1.4. Latin America
7.2. Natural Language Processing Market by Offerings
7.2.1. Software Offerings
7.3. Natural Language Processing Market by Deployment Mode
7.4. Global Natural Language Processing Market by Technologies
7.4.1. Pattern and Image Recognition
7.4.2. Interactive Voice Response (IVR)
7.4.3. Optical Character Recognition (OCR)
7.4.4. Text Analytics
7.4.5. Speech Analytics
7.4.6. Classification and Categorization
7.4.7. Auto Coding
7.4.8. Professional Services
7.4.9. Support and Maintenance Services
7.4.10. Natural Language Processing Market by Verticals
7.4.11. Healthcare and Lifesciences
7.4.12. Retail and Consumer Goods
7.4.13. High Tech and Electronics
7.4.14. Media and Entertainment
7.4.17. Research and Education
- Vendors Profiles
8.1 Microsoft Corporation
8.1.2. Business Units
8.1.3. Microsoft Corporation in Natural Language Processing
8.1.4. Business Focus
8.1.5. SWOT Analysis
8.1.6. Business Strategies
8.1.7. IBM Corporation
8.1.9. Business Units
8.1.10. Geographic Revenue
8.1.11. IBM Corporation in Natural Language Processing
8.1.12. Business Focus
8.1.13. SWOT Analysis
8.1.14. Business Strategies
8.1.15. Google Inc.
8.1.17. Business Units
8.1.18. Geographic Revenue
8.1.19. Google Inc. in Natural Language Processing
8.1.20. Business Focus
8.1.21. SWOT Analysis
8.1.22. Business Strategies
1.1.1 Apple Inc.
188.8.131.52 Business units
184.108.40.206 Geographic revenue
220.127.116.11 Apple in Natural Language Processing
18.104.22.168 Business focus
22.214.171.124 SWOT analysis
126.96.36.199 Business strategies
- Companies to Watch for
9.1.2. Addstructure Offerings
9.2.2. Angel.ai Offerings
9.3. Klevu Oy
9.3.2. Klevu Offerings
9.4.2. Twiggle Offerings
9.5. Dialogflow (Formerly known as Api.ai)
9.5.2. Dialogflow Offerings
9.6. Mindmeld (Acquired by Cisco)
9.6.2. Mindmeld Offerings
9.7.2. DigitalGenius Offerings
9.8.2. inbenta Offerings
9.9. Satisfi Labs Inc.
9.9.2. Satisfi Labs Inc. Offerings
9.10.2. NetBase Offerings
TABLE 1 GLOBAL NATURAL LANGUAGE PROCESSING MARKET REVENUE BY REGIONS, 2017-2023 ($BILLION) 36
TABLE 2 GLOBAL NATURAL LANGUAGE PROCESSING MARKET REVENUE BY OFFERINGS, 2017-2023 ($BILLION) 43
TABLE 3 GLOBAL NATURAL LANGUAGE PROCESSING MARKET REVENUE BY DEPLOYMENT MODE, 2017-2023 ($BILLION) 47
TABLE 4 GLOBAL NATURAL LANGUAGE PROCESSING MARKET REVENUE BY TECHNOLOGIES, 2017-2023 ($BILLION) 52
TABLE 5 GLOBAL NATURAL LANGUAGE PROCESSING MARKET REVENUE BY VERTICALS, 2017-2023 ($BILLION) 59
Infoholic Research works on a holistic 360° approach in order to deliver high quality, validated and reliable information in our market reports. The Market estimation and forecasting involves following steps:
- Data Collation (Primary & Secondary)
- In-house Estimation (Based on proprietary data bases and Models)
- Market Triangulation
Market related information is congregated from both primary and secondary sources.
Involved participants from all global stakeholders such as Solution providers, service providers, Industry associations, thought leaders etc. across levels such as CXOs, VPs and managers. Plus, our in-house industry experts having decades of industry experience contribute their consulting and advisory services.
Include public sources such as regulatory frameworks, government IT spending, government demographic indicators, industry association statistics, and company publications along with paid sources such as Factiva, OneSource, Bloomberg among others.