According to IBM, nearly half of businesses are now using NLP-based AI applications, and about 52% of these are using or considering using NLP-based AI analytics to enhance customer service
Almost half of businesses today are now using applications powered by natural language processing (NLP), and one-in-four businesses plan to begin using NLP technology over the next 12 months. Customer service is the top NLP use case with 52 percent of global IT professionals reporting that their company is using or considering using NLP solutions to improve customer experience.It was the use AI case IT professionals were most likely to report that the COVID-19 pandemic has increased their focus on.
It was Benjamin Disraeli, former Prime Minister of the United Kingdom, who once said: “There are three types of lies – lies, damn lies, and statistics”. If Disraeli were living amidst us today, he would have to eat a humble pie. Researchers, corporate leaders, politicians, administrators and almost everyone in the public space are now realizing the fundamental truth of our times — big data does not lie.
Big data, however, is chunks of small data. Any single piece of information can be viewed as a data and when combined with similar pieces of information, it becomes data sets. However, it would have never occurred to us before that data is everywhere – what people say and talk about us is also data. As there is proliferation of various media channels, huge chunks of conversations exist in the public space on almost every single endeavor of human life.
Listening has always played an important role in business. Understanding what people are talking about a brand and incorporating the feedback in real time has helped companies improve their business outcomes and boost productivity. However, with millions and trillions of conversations happening on a daily basis in a plethora of social media channels, keeping track of what people are saying about a brand is well-nigh impossible, let alone garner insights from them.
AI-powered sentiment analysis is enabling corporate decision makers to get real-time insights into the conversations. Whether they are positive, negative or neutral, the analysis leads to a highly precise classification of the trends, paving the path for better-informed decision making. Combining neuro-linguistic programming and machine learning, the trillions of conversations can be analysed within no time bringing deep insights to companies willing to learn.
Customer-sentiment analysis using AI enables decision-makers in the corporate world to monitor the sentiments and moods of the customers. With sentiment analysis, unstructured and seemingly unrelated data suddenly light up to provide actionable insights – empowering decision makers with superior accuracy.
In the contemporary, consumer-centric business ecosystem, growth and productivity have a direct correlation with the customer experience. If you look around, you will invariably find great customer experience at the core of most of the brands that are growing at impressive CAGR. Understanding customer sentiments, and creating a customer experience that takes into account customer challenges, concerns, and expectations, goes a long way in driving growth.
So, how does AI drive sentiment analysis? Advances in neuro-linguistic programming have made it possible to take public statements about a brand, essentially daily ongoing conversations that happen through a variety of media channels and classify them based on their tone and effect. The simplest classification that can be done is positive, negative and neutral. By themselves, each one of these individual conversations are meaningless but when analyzed using machine learning tools, the unstructured data suddenly starts revealing actionable insights. A brand cannot ignore these insights, if they want to remain in the market and if they have “customer satisfaction” as their main motto.
From a business perspective, sentiment analysis is a cutting-edge tool that empowers brands to eliminate the fogginess around customer interactions. By deploying AI-driven analytical tools that decode consumer sentiment, it is possible to identify the pain points as well as the inadequacy of response provided by customer service. By taking corrective measures based on such insights, brands can transform their product quality, enhance the customer experience, and do away with processes or actions that disappoint the audience. Eventually, everything in the modern business environment boils down to quickly, and accurately meeting the customer expectations. When that happens, revenue generation improves proportionately, and that’s exactly what AI sentiment analysis can do for your business.
This article has been authored by Bhaskar Mishra, Head of Product, Mihup