Emotion recognition technology is transforming customer service

“Good morning.”

“Good morning!”

Both greetings, though distinct only in punctuation, can establish different moods of the writer and the kind of tone conveyed to readers. Imagine the impact of a paragraph, tone can vary widely based on word choice, punctuation, and structure.

Depending on the context, one may be deemed inappropriate while the other perfectly conveys a desired tone and professionalism.

Making the desired impression with written communication is becoming increasingly important, as the business world increasingly shuns face to face catchups and phone calls for instant messaging tools and email. 

Grammarly recently launched a tone detector to help users write to understand how the content of their email will come across to others, and provide suggestions to alter messages appropriate to the subject of an email.

The extension tool determines tone using machine learning models that look for a combination of signals, such as word choice, punctuation usage, capitalization choices, negations, and amplification words like “very” or “extremely.”

But while a deft approach to tone is important between people, it’s also becoming central to the development of the conversations we are having with machines, as businesses turn to automated customer support.

A rise in customer support powered by AI

Besides crafting professional-sounding emails, emotion detection technology can be found in other communication sectors where quality customer services are the tenet of customer acquisition and retention. According to Statista, 67 percent of consumers worldwide used a chatbot for customer support in the last year.

Last month, OCBC officially announced the rollout of a new mobile-app based assistant in Singapore with the support of Clinc, a US conversational AI startup. 

The new Banking Assistant had handled 20,000 voice requests since its soft launch and the press release continues with the statement: “The customer speaks to the Assistant as if conversing with a human assistant– and the requested task gets done.”

In the same vein, Bank of America’s Erica had served more than 50 million clients in the first year after its introduction, from handling daily banking tasks to highly complex activities.

In an average month, Erica engages with more than 500 thousand new users providing a one-of-a-kind customer experience.

The mechanics behind AI in customer service

AI-generated customer support systems may not be as emotionally attuned or empathetic as people, but they are advancing to determine and accurately assess the emotions of customers based on their tone and language.

One of the ways is training chatbots to understand human language through Natural Language Processing (NLP). Chatbots will be fed with datasets of customer requests and responses to the queries. Based on this, chatbots can form adequate response during live chats with customers and assist them as efficiently as an experienced customer service representative.

A report by Juniper Research stated; “By 2023, banks around the world would save up an operational cost of US$7.3 billion with the use of chatbots.”

Besides its cost efficiency appeal, another advantage of chatbots is the availability of its services at all times. This comes in handy when an influx of customers may require instant support at the same time. 

A rising technology in customer service is sentiment analysis, whereby machine learning is trained to recognize the embedded emotions in language and determine the mood customers are in.

With this knowledge available, AI can offer customer service representatives the much-needed support by suggesting an appropriate course of action. Without a doubt, customer-facing employees are more ready to deal with challenging situations and can resolve customer requests in a shorter time span, increasing efficiency and leveraging business performance.

In the long run, sentiment analysis could provide imperative insights of the kind of services expected by customers and employ the ‘right’ moves to ensure satisfaction in services.

Forerunners are already integrating the findings of sentiment analysis into formulating distress plans, pinpointing the root of common customer issues and the best solutions for them, written in manners that appeal to the masses and gain the confidence of clients.

Basically, emotion recognition technology is a powerful tool and changing the face of customer service through its ability to identify human emotion as well as the ways to handle it.

However, while automated customer support is increasingly lifting the burden from companies, and providing customer 24-7 support, there is evidence to suggest that in some cases where the stakes are high, humans will choose a human operative— at least for now. 

In its UK Investors Survey, GlobalData found that just 5 percent of millennials chose chatbots to discuss the buying and selling of financial investments. For the remainder, face-to-face communication still reigned supreme in this scenario, with 95 percent preferring to speak to a human financial advisor.