While a picture may be worth a thousand words – a thousand words may be worth thousands of dollars. Never thought how valuable all your company unstructured data would be? This heterogeneous knowledge can turn out to be quite useful for companies, however, there is still much to be learned.
All textual data included in e-mails, surveys, and social media logs are of great value for your company and can increase your revenue if you know how to get the best out of it. Most of the textual information a company has at its disposal is usually presented in form of chunks of data with no clear structure. Here it is where text analytics tools come to help extract meaning from this raw data and, consequently, get valuable business insights.
Text analytics is the process of deriving high quality information from text for a specific purpose. The market space here includes products that get points and context from a text that can lead, afterwards, to take intelligent actions. Such products are composed of diverse sets of helpful instruments that range from topic extraction tools, including POS-tagging components to sentiment analysis software. These high-tech innovations make it possible for data leaders to carry out different case studies that, not so long ago, involved a quite Herculean effort. To give an idea of the most representative text analytics case studies, we gathered the following four examples:
Top 4 Use Cases for Text Analytics
- Improving customer service by extracting sentiment and impressions from surveys, contact center logs and social media feedback to enhance both the customer experience and the expected gains.
- Predicting patient behavior in a medical environment to identify in which events and conditions they would be at risk, enabling medical professionals to react on time.
- Managing insurance claims to make it easier for insurance companies to settle and pay claims after an accident.
- Detecting fraud by identifying legal risks looking through all documents, activity, behavior patterns and interactions related to a suspect.
In other words, typical tasks concerning text analytics are those related to document search, topic extraction or sentiment identification. Although big companies may have enough resources to build a proper data analytics team responsible of those tasks, they prefer to resort directly to text analytics service providers. What Gartner here recommends is using a hybrid approach which includes both linguistic and machine learning statistical techniques to process and analyze textual data. Whereas linguistic technologies are often employed for an adequate preparation of the data and models through semantics and grammar, the ML techniques will be used to achieve effective results in less time.
In a recent Gartner report ‘Market Guide for Text Analytics’, Bitext has been recognized as a representative vendor for text analytics thanks to its Customer Feedback Analysis tools and NLP Building Packages available in more than 60 languages, including minority languages as Irish Gaelic. Bitext case studies are mainly based on the automotive and hospitality industries, but these technologies can be adapted to any sector and industry through personalized offers and platform-independent data.
As Jim Bergeson said, “data will talk to you if you are willing to listen”. Maybe it is time to trust on third-party vendors to reach your goals in half the time.