Text analytics has evolved over the years to get more significance, thanks to the exponential growth of text data. Industries such as Healthcare, BFSI (Banking, Financial Services, and Insurance), and Governments are using text analytics extensively to reinvent their processes. Text analytics will gain more and more ground in these sectors by 2020.

In the retail industry, the use of text analytics software contributes to almost 30% of the total industry revenue, according to Allied Market research. For example, Walmart, the biggest retail company in the world, collects 2.5 petabytes of data every hour from their customer interaction. Insights from this huge data volume helped Walmart deal with complex business questions in just few minutes. Sounds exciting? It is. Let’s look at recent text analytics applications in diverse business verticals.

Voice of The Customer

Text analytics has been widely used by many leading players from various industries to understand the Voice of the customer better. The Voice of the customer simply means to be aware of the customer’s perspective about a product, business, or service. For Example, Netflix has been analyzing huge amounts of comments from social media and other online sources. This is to improve the accuracy of streaming video subtitles and captions. The challenge for Netflix is to improve the quality of video or audio and customer preferences. Also, to provide a personalized experience on Netflix. Netflix is taking the help of text analytics to know when demand is not satisfied. Netflix takes corrective action right away.


They used to gauge customer satisfaction for their park experience, but after noticing some bias in their last surveys, they explained those differences with text analytics. In effect, American families expected to be the most satisfied by the park experience, but some acquiescence responses were contrary as the survey only included highly satisfied or dissatisfied responses. Using OdinText software, Disney added more context to their initial survey finding and discovered that Hispanic-American and non-Hispanic park visitors were similar in satisfaction ratings and satisfied with many of the same aspects of their Disneyland experience.


Text analytics can even be simpler. For example, Apple, known for not seeking feedback proactively from customers, uses an NPS survey to estimate what customers are talking about the newly launched Apple watch. NPS is a customer relationship metric. Many businesses use it to benchmark customer satisfaction, which evaluates how customers recommend a product to close people. This tool is helpful to know how many people are talking about the brand and is easy to deploy as it requires asking direct questions.

Healthcare Sector

Text analytics is used for regulatory compliance, patient profiles, and clinical trials in the healthcare sector. An effective use of text analytics could also reflect benefits at a higher level. As per McKinsey’s 2016 analytics study, this added value has been estimated for US Healthcare to be around $300 billion! As per Market Allied Research, text analytics fosters healthcare research, contributing to rapid advancements in the healthcare and pharmaceutical sectors.

IBM and Linguamatics are big players in this market and take significant action at the industry level. IBM Watson Explorer QA is helping MSKKC (Memorial Sloan–Kettering Cancer Center), a cancer treatment center in New–York City, to interpret large volumes of data such as medical literature, patient treatments, and clinical trials. Insights from these documents are in use to assist Oncologists in choosing between one treatment or another. Furthermore, linguistics has helped governments and organizations solve the Ebola outbreak, one of the deadliest in History. Thanks to their powerful NLP, they have given insights from patients’ documents to see which molecules could lead to the viral infection.

BFSI (Banking, Financial Services and Insurance)

Financial services are early adopters of text analytics as this industry requires the analysis of huge amounts of evidence in emails, claims, financial statements, or reports. Regulatory compliance and fraud detection are the most common text analytics applications in the financial sector. According to McKinsey Global Institute, the value of effective use of text analytics to detect frauds and improve operations for Europe’s Public Sector administration is estimated at around $250 million of potential annual value.

In September 2014, the advanced analytics division of the Bank of England analyzed social media content. This initiative aimed to know if they could be facing huge withdrawal from Scottish financial institutions after the Scottish Independence Referendum. This was an interesting approach to using text analytics.

Text analytics can be useful for Banking and Credit Institutions for Customer segmentation and Risk management. BNP Paribas Fortis is an international bank based in Belgium. It’s been using the Clarabridge tool to analyze engagement from social networks. Social networks include Facebook, twitter, Instagram, Linkedin, YouTube, and many other online channels. This information has helped banks have a better social overview and improve their customer response, significantly reducing their average handling time.


Terrorism is becoming an adverse problem around the world. Recently, European countries such as Sweden, Germany, and the UK have witnessed unpredictable terrorist attacks. Since 2012, Europol, Europe’s central law enforcement agency, has been using the Attivio text analytics platform for consolidating criminal information systems. Consolidating various data sources enabled them to have easy and fast access. Using entity recognition and correlation of various metadata, text, and geographical information, the Attivio tool could deal with complex queries such as finding people trained in Afghanistan over the last 5 years, having specific driving license plate numbers, and so on.

Here Comes the Cognitive Era

The next quest for big data text analytics is to enable platforms to be cognitive and self–educated. In other words, the next-generation text analytics platform should be able to do tasks that normally require human Intelligence. In recent years, AI capabilities such as deep learning, Robotics automation processes, or advanced cognitive analytics have been adopted in text analytics platforms.

The best example as of now is IBM Watson Explorer. This cognitive system is trained to understand natural language and to evolve continuously. This tool is gaining huge popularity in the Insurance sector. It can ingest thousands of industries–related documents such as books, articles, publications, etc. Apart from that, Machine intelligence could be used for different use cases. To get insights from future scenarios, to engage with customers, or to automate deep domain–specific tasks. (Like analyzing Radiology images to identify malignant tumors).

Businesses must use text analytics, and we will see a huge adoption of text analytics. At Ellicium, we want to empower businesses with text analytics, and our unstructured data analytics platform “Gadfly” does the same. I would be glad to inform you more about it and schedule an exclusive demo of Gadfly.

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