What's driving the Cloud Natural Language Processing Market growth? Key Players are: Google, Microsoft, IBM, Dolbey Systems, Facebook, HP, AWS, Baidu, Netbase Solutions, Fuji Xerox, Lexalytics, SAS, Verint Systems

Asia Pacific is estimated to be the fastest growing region in the global cloud natural language processing market owing to the increasing adoption of the smart devices. Moreover, increasing investment in AI by Chinese players such as Baidu and Alibaba are also contributing significantly towards the revenue growth.

Global cloud Natural Language Processing (NLP) market is set to exceed USD 6 billion by 2024, according to this new research report. Growing adoption of the big data technologies such as Hadoop and IoT among organizations along with popularity of cloud deployment is one of the major factors that are fueling the growth of the digital data. Digital data is estimated to grow at a rate of 40% for next 10 years and by the year 2020 data generation is expected to reach 44 zettabytes per year. As the data increases, it leads to the requirement of an effective analytics solution to process the information, therefore, driving the cloud NLP market growth.

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Some of the vendors offering NLP solutions are Google, Microsoft, IBM, HP, AWS, Baidu, Dolbey Systems, Facebook, Netbase Solutions, Fuji Xerox, Lexalytics, SAS, and Verint Systems. Vendors are trying to capture the market with customized product portfolio, that will help them in gaining more market share. Also, they are collaborating with cloud providers to offer products and services especially, to SMEs. The market is quite attractive as majority of the players are ready to invest in technologies such as AI, deep learning, and machine learning.

Exponential growth of the digital data is one of the major factors that is driving the growth of the cloud natural language processing market. Data/information has emerged as one of the most important assets of the organizations. Companies are collecting, analyzing, and reporting vast volume of data for extracting meaningful insights to get competitive edge.

Healthcare applications are estimated to exhibit higher growth rate than other industry verticals. Healthcare organizations are widely adopting NLP solutions for analyzing clinical transcriptions, managing medical notes, and documentation purposes. Furthermore, these solutions also assist in reducing medication errors and adverse drug events by managing Health Information Management (HIM) systems. In addition, implementation of ICD-10 regulation and adoption of auto-coding technology in healthcare is further estimated to fuel the demand in the healthcare sector.Media & advertisement and the retail sector is also one of the major end-users of the cloud NLP market solutions. They are leveraging NLP capabilities along with social media analytics modules and CRM solutions for lead generation and brand recognition. Furthermore, increasing interest of the market players towards these industry verticals is also backing the growth of the market. For instance, IBM, HP, SAS and EasyAsk are providing NLP tools such as sentiment analysis and text analysis to these industry sectors.

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Report Content

Chapter 1. Methodology & Scope

1.1. Methodology

1.1.1. Initial data exploration

1.1.2. Statistical model and forecast

1.1.3. Industry insights and validation

1.1.4. Scope, definition and research parameters

1.2. Data Sources

1.2.1. Primary

1.2.2. Secondary

Chapter 2. Executive Summary

2.1. industry 360º synopsis, 2013 - 2024

2.1.1. Business trends

2.1.2. Regional trends

2.1.3. Product trends

2.1.4. Deployment model trends

2.1.5. Technology trends

2.1.6. Application trends

2.1.7. End-use trends

Chapter 3. Cloud NLP Industry Insights

3.1. Introduction

3.2. Industry segmentation

3.3. Industry landscape, 2013-2024

3.4. Industry ecosystem analysis

3.5. Cloud natural language processing architecture analysis

3.6. Cloud natural language processing features

3.7. Technology & innovation landscape

3.8. Regulatory landscape

3.9. Industry impact forces

3.9.1. Growth drivers

3.9.1.1. Increasing investment in AI in North America

3.9.1.2. Rising demand to improve customer experience

3.9.1.3. Growing digital data

3.9.1.4. Rapid adoption of smart devices

3.9.2. Industry pitfalls & challenges

3.9.2.1. Requirement for effective predictive technology

3.9.2.2. Low adoption trends

3.10. Growth potential analysis

3.11. Porter's analysis

3.12. PESTEL analysis

Chapter 4. Competitive Landscape

4.1. Introduction

4.2. Company analysis by major players, 2016

4.3. Company analysis by innovation leaders, 2016

4.4. Competitive landscape

Chapter 5. Cloud NLP Market, By Product

5.1. Cloud NLP market share by product, 2016 & 2024

5.2. Rule based

5.2.1. Rule based NLP, by region, 2013-2024

5.3. Statistical

5.3.1. Statistical NLP, by region, 2013-2024

5.4. Hybrid

5.4.1. Hybrid NLP, by region, 2013-2024

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