As discussed in my previous blog (http://bit.ly/2p0Cmrz), 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 retail sector, use of text analytics software contributes to almost 30% of the total industry revenue according to Allied Market research. For example, Walmart, which is the biggest retailer company in the world, is collecting 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 have a look at recent applications of text analytics in diverse business verticals.
Voice of The Customer
Text analytics has been widely used to understand Voice of the customer better, by many leading players from various industries. The Voice of customer simply means to be aware of what customer’s perspective about a product, business or service. For Example, Netflix has been analysing huge amount of comments from social media and others online sources. This is to improve accuracy of streaming videos subtitle and caption. The challenge for Netflix is to improve quality of video or audio and customer’s preferences. Also to provide personalised experience on Netflix. Netflix is taking help of text analytics to know when demand is not satisfied. Netflix take corrective action right away.
Used to gauge customer satisfaction for their park experience but after having noticed some bias in their last surveys, they explain those differences with text analytics. In effect, Hispano–American family 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 surveys 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 who is known for not seeking feedback proactively from customers uses NPS survey to estimate what their customers are talking about newly launched Apple watch. NPS is a customer relationship metric. Many businesses use it to benchmark their customer satisfaction which evaluates how customers recommend a product to a close people. This tool is really helpful to have an idea of how many people are talking about the brand and is easy to deploy as it requires asking direct questions.
Text analytics is in use for regulatory compliance, patient profile and clinical trials in healthcare sector. An effective use of text analytics could also reflect in 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 of dollars! As per Market Allied Research, use of text analytics fosters healthcare research in turn contributing towards rapid advancements healthcare and pharmaceutical sector.
IBM and Linguamatics are big players in this market and are also making significant action at the industry level. IBM Watson explorer QA is helping MSKKC (Memorial Sloan–Keterring 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 Oncologist to choose between one treatment or another. Furthermore, Linguamatics has helped government and organizations to solve Ebola outbreak which is 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 service are early adopters of text analytics as this industry requires to analyse huge amount of evidence material in forms of emails, claims, financial statements or reports. Regulatory compliance and fraud detection are most common applications of text analytics in 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 estimate around $250 million of potential annual value.
In September 2014, advanced analytics division of Bank of England analysed social media content. The purpose of this initiative was to know if they could be facing huge withdrawal from Scottish financial institutions after the Scottish Independence Referendum. This was an interesting approach towards using text analytics.
Text analytics can be useful for Banking and Credit Institutions for Customer segmentation and Risk management. BNP Paribas fortis, an international bank based in Belgium. It’s been using Clarabridge tool to analyze engagement from social network. Social network such as Facebook, twitter, Instagram, Linkedin, Youtube and also from many others online channels. This information has helped bank to have a better social overview and improve their customer response cutting down their average handling time significantly.
Terrorism is becoming an adverse problem around the world. Recently, European countries such as Sweden, Germany and UK has been witness of unpredictable terrorist attacks. Since 2012, Europol, Europe’s central law enforcement agency has been using Attivio text analytics platform for consolidating criminal information system. Consolidating various data sources enabled them to have easy and fast access. Using entity recognition and correlation of various metadata, text and geographical information, Attivio tool could deal with complex queries such as finding people trained in Afghanistan over last 5 years, having specific driving license plate number and so on.
Here Comes The Cognitive Era
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 which normally require human Intelligence. AI capabilities such as deep learning, Robotics automation process or advanced cognitive analytics getting adopted in text analytics platforms last years. The best example as of now is IBM Watson explorer. This cognitive system is train to understand natural language and to evolve continuously. This tool is gaining huge popularity in Insurance sector. As it can ingest thousands of industry–related documents as books, articles, publication and so on. 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).
It is imperative for businesses to use text analytics and we will see 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 let you know more about it and schedule an exclusive demo of Gadfly. You can send requests for the same at email@example.com .