The Text Analytics Market projected to grow from $3.2 billion in 2016 To $8.8 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 17.2% during the forecast period, as per a report by MarketsandMarkets.
Why enhancing customer service and competitive intelligence presents an opportunity?
Text analytics assists companies in analyzing customer-centric data generated through customer interactions, feedbacks, and opinions, social media platforms, emails, contact center notes, survey responses, and blog postings. This analyzed customer data lets companies address customer issues related to their products and services, such as improving quality, bringing in needed innovation as per customer’s preferences, and finally managing their brand image and reputation. Companies believe that, with text analytics they carry a more tactical approach to gaining actionable insights from analyzed data. As a result, on implementing text analytics, companies gain the potential to improve customer experience through their Customer Relationship Management (CRM) and closely exercise marketing and branding activities efficiently and effectively. Text analytics, with multilingual support is also the key to removing the language barrier between a customer and company, which would help the company establish and expand globally. As per the analytics giants, such as IBM and SAS, the customer experience data would assist them in creating personalized offerings and campaigns to improve the sales and service further, and have a well-defined customer perception – both conscious and subconscious, for enhanced customer satisfaction, loyalty, and advocacy.
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Why achieving consistency in business semantics and interpretations is a challenge?
Focusing on business semantics and making notes about common rules and policies about the structure, flow, quality, and security of data must be the top priority for organizations. Inconsistent business semantics and failure to determine the authenticity of the data can lead to loss of data, creation of large volumes of unstructured data, incompatible databases, and wrong decision-making. To overcome inconsistency, business organizations have to set up nomenclatures and rules, which must be accepted and understood by all stakeholders within and outside the organization. Organizations need to create a corporate definition of all terms, so that the agreed definitions conform to the standards, while still maintaining distinct local definitions. Text analytics software must be capable enough to consider the chance of human error and prevent or minimize its occurrence in the first place. Hence, establishing a data structure, and achieving semantic disambiguates and consistent interpretation are the key challenges for text analytics.
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The major vendors providing text analytics software and services are SAP SE (Germany), IBM Corporation (US), SAS Institute, Inc. (US), OpenText Corporation (Canada), Clarabridge, Inc. (US), Megaputer Intelligence, Inc. (US), Luminoso Technologies, Inc. (US), MeaningCloud LLC (US), KNIME.com AG (Switzerland), and Infegy, Inc. (US).
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