How Text Analytics Applications Bridge Gap Between Business-Customer Communication

Researchers and scientists all across the world are working towards bridging the gap between humans and machines. In the move to do so, text analytics plays a vital role in easing out the communication between human and mechanized systems.

It is an automated process to analyze written text and extract useful information from it. The process is often executed using software designed to go through lengthy texts and gather insights that may further help in marketing, branding or other significant purposes.

A number of companies across different industries use text analytics to analyze articles, tweets, social media posts, reviews, comments, and other types of writing. It is done to seek meaning and gather intelligence with the help of algorithms and ML tools.

Additionally, the concept of NLP and text mining is vaguely familiar to text analytics, they are often used together to carry out business processes.

Application of Text Analytics Merged with Text Mining and NLP 

From security to academia, technology is being implemented for various business and research purposes to drive better decision-making and predict futuristic behaviours. Below are five significant use cases of text analytics:

Monitoring Social Media Behaviour

Any communication across social media can be the greatest resource to understanding customers' opinions and feelings about businesses, products, and services. The tool is used to analyze written and shared content of people on an online medium. However, the writing style on social media can be quite specific and different from other types of writing. In some cases, sentences can be very short, words can be abbreviated, and emojis are often used to express feelings and opinions. Therefore, efficient NLP and analysis software is deployed to understand millennials' way of communicating.

Assessment of Creditworthiness 

Banks in developing countries can leverage NLP and text analytics to assess the creditworthiness of clients who may or may not have credit history. If any client doesn't use credit, his internet browsing and engagement in other activities can leave significant digital footprints behind for analysis. NLP algorithms and text analytics can analyze geolocation data, social media activity, browsing behavior to gather insights into their habits, networks, and strength of their relationships.

Also by the analysis of their numerous client-related variables, the tool generates a credit score which can predict customer's further activity. However, the access to customers' information is only granted on consent. Additionally, the data can never be transferred to third party.

Deployment of Chatbots

These are the most common application of text analytics and NLP. The chatbot is actively used in business as they help meet customers' request for personalization. This is done by collecting user-relevant data and in return providing extremely personalized experiences eliminating the stress of human-to-human communication. Additionally, in sales, chatbots are being used increasingly as they can target prospects, strike a conversation, schedule appointments and do much more for betterment.

Speeding Up Hiring and Recruitment Process

Using text analytics and NLP, HR professionals can pace up their candidate search while discovering relevant resumes. The tool can also craft bias-proof and gender-neutral job description. It can search for relevant synonyms to help recruiters identify candidates that meet the job requirement.

Increasing Potential for Advertising

Through digital media analysis, text analytics and NLP tools enable advertisers to identify new audiences who might be interested in their products. A simple keyword matching routine helps software widen the range of channels for ad placement. It helps companies spend their ad budgets more effectively while understanding their target audiences.