In a recent published report, Kenneth Research has updated the market report for Natural Language Processing Market for 2024 till 2030. Report further now discusses; the various strategies to be adopted or being adopted by the business players across the globe at various levels in the value chain. In the view of the global economic slowdown, we further estimated that China, India, Japan and South Korea to recover fastest amongst all the countries in the Asian market. Germany, France, Italy, Spain to take the worst hit and this hit is expected to be regain 25% by the end of 2024- Positive Growth in the economic demand and supply.
U.S. Market recovers fast; In a release on May 4th 2024, the U.S. Bureau and Economic Analsysis and U.S. Census Bureau mentions the recovery in the U.S. International trade in March 2024. Exports in the country reached $200 billion, up by $12.4 billion in Feb 2024. Following the continuous incremental trend, imports tallied at $274.5 billion, picked up by $16.4 billion in Feb 2024. However, as COVID19 still haunts the economies across the globe, year-over-year (y-o-y) avergae exports in the U.S. declined by $7.0 billion from March 2024 till March 2024 whilest imports increased by $20.7 billion during the same time. This definitely shows how the market is trying to recover back and this will have a direct impact on the Healthcare/ICT/Chemical industries, creating a huge demand for Natural Language Processing Market products.
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In the United States, internet access to households grew from around 73% of the households in 2015 to close to 80% of the households in 2019. In Korea, it was around 98% in 2014 which grew to nearly 100% in 2024. On the other hand, in the Netherlands, internet access to households touched nearly 98% in 2019. Countries in the Latin America, such as Brazil and Mexico grew at a significant pace by about 1.45x and 1.63x respectively within a span of 4 years between 2015 and 2019.
Market Overview
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.
Market Analysis
According to 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
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.
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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.
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.
Benefits
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.
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Table of Contents
1. Industry Outlook 10
1.1 Industry Overview 10
1.2 Industry Trends 11
1.3 PEST Analysis 14
2 Report Outline 14
2.1 Report Scope 14
2.2 Report Summary 15
2.3 Research Methodology 16
2.4 Report Assumptions 17
3 Market Snapshot 18
3.1 Total Addressable Market 18
3.2 Segmented Addressable Market 18
3.3 Related Markets 18
3.3.1 Machine Learning Market 18
3.3.2 Artificial Intelligence Market 19
4 Market Outlook 20
4.1 Overview 20
4.2 Regulatory Bodies and Standards 21
4.3 Porter 5 (Five) Forces 21
5 Market Characteristics 23
5.1 Use Cases of Natural Language Processing 25
5.2 Market Segmentation 27
5.3 Market Dynamics 27
5.3.1 Drivers 28
5.3.1.1 High Volume of Unstructured Data 28
5.3.1.2 Enhanced utility of smart devices 29
5.3.1.3 Increased use of NLP in customer call centres 29
5.3.1.4 Increased demand for better customer experience 29
5.3.1.5 Expanding application areas 30
5.3.2 Restraints 30
5.3.2.1 Bridging the gap between humans and machines 30
5.3.2.2 Training of researchers 30
5.3.2.3 Loss of context and meaning 30
5.3.3 Opportunities 32
5.3.3.1 Development in Big Data Technologies 32
5.3.3.2 NLP will democratize data 32
5.3.3.3 Smart Search 32
5.3.3.4 Emergence of human like virtual assistants 32
5.4 DRO – Impact Analysis 32
6 Trends, Roadmap, and Projects 33
6.1 Market Trends & Impact 33
6.2 Technology Roadmap 34
7 Geographic Segmentation: Market Size and Analysis 35
7.1 Overview 35
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