Natural Language Processing (NLP) Market Size is valued at USD 14.53 Bn in 2022 and is predicted to reach USD 131.33 Bn by the year 2031 at a 27.8% CAGR during the forecast period for 2023-2031.
Natural Language Processing (NLP) works with artificial intelligence (AI) technology that uses voice-enabled artificial intelligence (AI) and conversational intelligence technologies to enable computers to read human language, derive meaning, and make communication more convenient. NLP deals with various aspects of text and language, including text analysis, language understanding, and language generation.
Factors such as the rising use of smart devices to facilitate smart environments fuel market growth. Furthermore, rising demand for advanced text analytics and increased use of the internet and linked devices are propelling the natural language processing market forward. Furthermore, NLP-based solutions are increasingly accepted across industries to improve customer experience. Furthermore, rising healthcare investment is likely to generate profitable growth possibilities for the industry.
However, the COVID-19 pandemic has had a devastating influence on the global economy, with governments around the world imposing lockdowns and restrictions. However, the COVID-19 epidemic has compelled businesses all over the world to migrate to digital platforms and deploy digital technology-based solutions to streamline their processes.
Some Major Key Players In The Natural Language Processing (NLP) Market:
The Natural Language Processing Market comprises some significant segments: Offering, Type, Application, Technology, and End-user. According to Offerings, the market is segmented as Solutions (Type(Platform, Software Tools), Deployment Mode (Cloud, On-Premises)) and Services (Professional Services (Training and consulting, System Integration & Implementation, Support & Maintenance), Managed Services)). Whereas, By Type, the market is divided into Rule-Based, Statistical, and Hybrid. As per the Application, the market is divided into Customer Experience Management, Virtual Assistants/Chat-Bots, Social Media Monitoring, Sentiment Analysis, Text Classification & Summarization, Employee Onboarding & Recruiting, Language Generation & Speech Recognition, Machine Translation, and Other applications.
As per the Technology segment, the market is segmented as Optical Character Recognition (OCR), Interactive Voice Response (IVR), Auto Coding, Text Analysis, Speech Analytics, Image & Pattern Recognition, Simulation & Modeling. At last, By End-user, the market is divided into BFSI, IT & ITeS, Retail & e-Commerce, Healthcare & Life Sciences, Transportation & Logistics, Government & Public Sector, Media & Entertainment, Manufacturing, Telecom, and Other End-user (Education, Automotive, Travel & Hospitality, And Energy & Utilities)).
The solutions category is expected to hold a major share of the global Natural Language Processing (NLP) Market in 2022. Factors such as increased corporate competitiveness, law, user needs, and project risks promote NLP use, which drives market growth. Furthermore, NLP solutions provide the capabilities required to analyze numerical and verbal data, which is useful to the industry. However, the services sector is predicted to develop the most. Adoption of NLP services delivers several benefits to industry End-user, including faster software implementation, maximizing the value of existing installations by optimizing them and lowering deployment costs and risks, among other things, which stimulates market growth.
The on-premises segment is likely to grow at a rapid rate in the global Natural Language Processing (NLP) Market. The on-premises NLP deployment provides complete data control, visibility, and authentication security controls. Furthermore, with built-in redundancy, it is easy to scale to meet corporate demand and improve productivity. The rising implementation of cloud-based NLP is expected to drive market growth.
The North America Natural Language Processing (NLP) Market is expected to record the maximum market share in terms of revenue in the near future. The region dominates AI and machine learning technology, making it one of the most important markets for natural language processing technologies. Furthermore, the presence of important market participants in the United States fosters innovation in the region, supporting the growth of natural language processing. Regional governments are also progressively encouraging the use of AI, ML, and NLP technologies, allowing market participants to increase their footprint in the region. During the forecast period, Asia Pacific is expected to increase at a rapid pace. The increase can be attributed to increased smartphone usage, rapid technical breakthroughs, economic digitization, and government initiatives in the region's developing countries.
| Report Attribute | Specifications |
| Market Size Value In 2022 | USD 14.53 Bn |
| Revenue Forecast In 2031 | USD 131.33 Bn |
| Growth Rate CAGR | CAGR of 27.8% from 2023 to 2031 |
| Quantitative Units | Representation of revenue in US$ Bn and CAGR from 2023 to 2031 |
| Historic Year | 2019 to 2022 |
| Forecast Year | 2023-2031 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Offering, Type, Application, Technology, End-user |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South Korea; South East Asia |
| Competitive Landscape | IBM (US), Microsoft (US), Google (US), AWS (US), Meta (US), 3M (US), Baidu (China), Apple (US), SAS Institute (US), IQVIA (UK), Oracle (US), Salesforce (US), OpenAI (US), Inbenta (US), LivePerson (US), SoundHound AI (US), MindMeld (US), Veritone (US), Dolbey (US), Automated Insights (US), Bitext (US), Conversica (US), UiPath (US), Addepto (US), RaGaVeRa (India), Observe.ai (US), Eigen (US), Gnani.ai (India), Crayon Data (Singapore), Narrativa (US), deepset (US), Ellipsis Health (US), DheeYantra (US), Verbit.ai (US), Rasa (US), MonkeyLearn (US), TextRazor (England), and Cohere (Canada). |
| Customization Scope | Free customization report with the procurement of the report, Modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
Natural Language Processing (NLP) Market By Offerings -
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Natural Language Processing (NLP) Market By Technology-
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Natural Language Processing (NLP) Market By Region-
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This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.