The global Textual Mining Market is forecast to reach USD 16.85 Billion by 2027, according to a new report by Reports and Data. Textual mining tools are increasingly used by most of the organizations to help their business by useful insights from different sources, such as client interaction, blogs, emails, tweets, product reviews, and center logs.
The major objective of the mining of these textuals is to gather several forms of information that includes structured and unstructured data and can be utilized further for fueling the decision of the business. Some of the benefits of this mining include improved processing speed, cost reduction, better consistency, and integration of big data. The rise in demand for Big Data and machine learning is augmenting the growth of the product.
Textual mining is gaining traction due to the rise in the adoption of social media platforms and a growing inclination of cloud technology for data storage. The organization is transitioning to adopt cloud technology. They are being driven by their ability to prevent fraud, plan efficient marketing campaigns, and manage risk.
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The partnership between IBM and the Fortune 500 company in March 2017 demonstrates the application of text analytics solutions in detecting fake insurance claims and improving customer experience. By leveraging text mining techniques, such as natural language processing and pattern recognition, the companies were able to analyze various documents and identify patterns that indicated fraudulent claims.
The use of text mining in the insurance industry offers several benefits. Firstly, it helps reduce the financial burden on insurance firms by detecting misrepresented and fake claims, thus minimizing fraudulent activities and the associated costs. By identifying similarities in claim patterns and verifying their authenticity, insurance companies can prevent payouts on fraudulent claims, leading to significant cost savings.
Additionally, text mining enables faster claim settlement processes, as it can quickly analyze large volumes of textual data, such as policy documents, claim forms, customer communications, and other relevant sources. By automating the analysis of these documents, insurers can accelerate the claim processing time, resulting in improved customer experience and satisfaction.
The collaboration between IBM and the Fortune 500 company exemplifies the growing awareness and adoption of text mining solutions within the insurance industry. As more companies realize the benefits of text mining, such as fraud detection and improved operational efficiency, the market for text mining technologies is expected to grow.
However, it is worth noting that the cost involved in the text mining process remains a challenge for some companies. Implementing text mining technologies and tools requires investment in software, infrastructure, and skilled personnel. The upfront costs and ongoing maintenance expenses can be a barrier to entry for certain organizations, limiting the adoption of text mining solutions.
some leading companies operating in the global Keyword market:
SAS Institute, Inc., SAP SE, Clarabridge, Inc., Lexalytics, Inc., IBM Corporation, Luminoso Technologies Inc., Averbis, Meaningcloud LLC, Opentextual Corporation, and Bitextual Innovations S.L., among others.
The retail sector is poised to experience significant growth with a high compound annual growth rate (CAGR) due to the increasing demand for maximizing sales and customer intelligence. In order to ensure profitability and cater to the diverse product preferences of customers, the retail sector relies heavily on maintaining regular customer demand. As a result, the industry is expected to witness a surge in the adoption of customer intelligence platforms provided by leading service providers such as IBM, SAP, and SAS. This adoption of customer intelligence platforms is anticipated to drive online efficiency and sales, enabling retailers to gain better insights into customer behavior and preferences, and develop more targeted marketing strategies. By leveraging customer intelligence platforms, retailers can improve customer engagement, enhance brand loyalty, and ultimately achieve greater success in a highly competitive market.
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The global market is further segmented into type and application:
Component Outlook (Revenue, USD Billion; 2017-2027)
- Managed services
- Professional services
Application Outlook (Revenue, USD Billion; 2017-2027)
- Marketing Management
- Document Management
- Customer Experience Management
- Governance, Risk, and Compliance Management
- Workforce Management
Deployment Mode Outlook (Revenue, USD Billion; 2017-2027)
Organization Size Outlook (Revenue, USD Billion; 2017-2027)
- Large Enterprises
- Small and Medium Enterprises
Industry Vertical Outlook (Revenue, USD Billion; 2017-2027)
- Retail and Ecommerce
- Healthcare and Life Sciences
- Banking, Financial Services, and Insurance
- Government and Defense
- Telecommunications and Information Technology
- Media and Entertainment
- Energy and Utilities
- Travel and Hospitality
Regional analysis provides insights into key trends and demands in each major country that can affect market growth in the region.
- North America (U.S., Canada, Mexico)
- Europe (Germany, U.K., Italy, France, BENELUX, Rest of Europe)
- Asia Pacific (China, India, Japan, South Korea, Rest of APAC)
- Latin America (Brazil, Rest of LATAM)
- Middle East & Africa (Saudi Arabia, U.A.E., South Africa, Rest of MEA)
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