The global text analytics market is garnering momentum and it will experience commendable growth in the next few years. The technology is being widely adopted across various industry verticals as businesses aim to improve decision making and customer satisfaction.

Also known as text mining, text analytics refers to the automated translation of large volumes of unstructured text data into quantitative form to understand trends, patterns, and insights. Combined with data visualization tools, these techniques enable businesses to make smarter business decisions and improve business performance.

With the increasing adoption of the technology, leading industry players are working on developing text analytics solutions to provide support local languages. For instance, in November 2020, Microsoft announced that it had added Hindi as the latest language under its Text Analytics platform, which is part of the Microsoft Azure Cognitive Services, to support businesses with improved customer sentiment analysis.

Hindi is the latest addition to the number of languages supported by Microsoft's Text Analytics API service. English as well as numerous European and Asian languages are currently supported by the platform.

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Based on the component, the text analytics industry is bifurcated into software and services. Text analytics software solutions include tools for customer relation management (CRM), customer intelligence, governance, risk and compliance, and fraud detection, among others.

The adoption of fraud detection solutions is slated to increase at a robust 20% CAGR over 2021-2027. The BFSI sector will likely be among the key adopters of these solutions due to the increasing number of fraud incidents in the industry.

Text analytics services mainly include professional services and managed services. It is estimated that the demand for managed services will grow at a notable 24% CAGR up to 2027. A large number of enterprises, especially small and medium size businesses are adopting managed services for conversational AI and NLP functionalities.

Today, text analytics tools find application in BFSI, healthcare, retail, government & defense, IT & telecom, media & advertisement, travel & hospitality, and other areas. Travel and hospitality application segment is set to capture more than 15% revenue share of text analytics market by 2027 due to the growing importance of sentiment analysis in the industry.

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Meanwhile, the demand for text analytics solutions in media and advertisement will rise at a healthy 23% CAGR in the coming years due to the widespread use of social media analytics in marketing and advertising.

Geographically, the market for text analytics in Latin America will record a notable CAGR of 18% through 2027. With increasing penetration of smartphones and availability of highspeed internet connectivity, consumer presence on social media platforms has increased significantly. Similar trends in the near future indicate a strong growth outlook for the regional market. The Middle East and Africa text analytics industry will expand at a steady 15% CAGR up to 2027.

Table of Contents

Chapter 1 Methodology & Scope

• 1.1 Scope & defintions
• 1.2 Methodology & forecast parameters
• 1.3 Region-wise COVID-19 impact anaysis
  • 1.3.1 North America
  • 1.3.2 Europe
  • 1.3.3 Asia Pacific
  • 1.3.4 Latin America
  • 1.3.5 Middle East & Africa
• 1.4 Data Sources
  • 1.4.1 Secondary
  • 1.4.2 Primary

Chapter 2 Executive Summary

• 2.1 Text analytics industry 360 degree synopsis, 2016 - 2027
• 2.2 Business trends
• 2.3 Regional trends
• 2.4 component trends
• 2.5 deployment model trends
• 2.6 Application trends

Chapter 3 Text analytics Industry Insights

• 3.1 Introduction
• 3.2 Industry segmentation
• 3.3 Impact of COVID-19 outbreak
  • 3.3.1 Global outlook
  • 3.3.2 By region
    • 3.3.2.1 North America
    • 3.3.2.2 Europe
    • 3.3.2.3 Asia Pacific
    • 3.3.2.4 Latin America
    • 3.3.2.5 Middle East & Africa
  • 3.3.3 Industry value chain
    • 3.3.3.1 Suppliers
    • 3.3.3.2 Text analytics technology providers
    • 3.3.3.3 Marketing & distribution channels
  • 3.3.4 Competitive landscape
    • 3.3.4.1 Strategy
    • 3.3.4.2 Distribution network
    • 3.3.4.3 Business growth
• 3.4 Evolution of text analytics
• 3.5 Text analytics industry architecture
• 3.6 Text analytics industry ecosystem analysis
  • 3.6.1 Text analytics providers
  • 3.6.2 Cloud service providers
  • 3.6.3 Third party service providers
  • 3.6.4 Marketing and distribution channel
  • 3.6.5 Vendor matrix
• 3.7 Technology & innovation landscape
  • 3.7.1 Network virtualization
  • 3.7.2 Big data analytics
  • 3.7.3 Artificial Intelligence
  • 3.7.4 Natural Language Processing (NLP)
• 3.8 Regulatory landscape
  • 3.8.1 North America
    • 3.8.1.1 Standards on Privacy and Web Analytics (Canada)
    • 3.8.1.2 Health Insurance Portability and Accountability Act (HIPAA) of 1996 (U
  • 3.8.2 Europe
    • 3.8.2.1 General Data Protection Regulation (GDPR)
    • 3.8.2.2 Exception of Text & Data Mining (TDM) in Copyright in Digital Single Market (DSM Draft Directive - EU)
  • 3.8.3 APAC
    • 3.8.3.1 Information Security Technology- Personal Information Security Specification GB/T 35273-2017 (China)
    • 3.8.3.2 Secure India National Digital Communications Policy 2018-Draft 66 (India)
  • 3.8.4 LATAM
    • 3.8.4.1 The General Data Protection Law (Brazil)
    • 3.8.4.2 Personal Data Protection Act 25 326 (PDPA - Argentina)
  • 3.8.5 MEA
    • 3.8.5.1 Federal Law No 2 of 2019 on the use of ICT in Healthcare (UAE)
    • 3.8.5.2 Privacy Protection Regulations (Data Security), 5777-2017 (Israel)
• 3.9 Industry impact forces
  • 3.9.1 Growth drivers
    • 3.9.1.1 Growing demand for social media analytics
    • 3.9.1.2 Increasing use of business intelligence tools
    • 3.9.1.3 Proliferation of cloud computing technology
    • 3.9.1.4 Growing demand for sentiment analysiss
  • 3.9.2 Industry pitfalls & challenges
    • 3.9.2.1 Lack of awareness
    • 3.9.2.2 High cost of software
• 3.10 Growth potential analysis
• 3.11 Porter's analysis
• 3.12 PESTEL analysis

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