Communication which started with humans interacting verbally with words has now progressed to advanced human-bot interactions. These highly programmed bots have the capabilities to give accurate responses to complex queries. However, fruitful conversations with customers – can never be limited to mere answers to their questions. It has to be empathetic, where the bot has to understand the user’s concerns and respond accordingly.
These aspects of understanding and reciprocating emotions were considered a far-fetched goal for AI to achieve. But today, as impossible as it may seem, AI has started to understand and analyse emotions. And not just that, bots are now capable of responding and reciprocating with empathy, when they find stress markers.
This amazing turn in technology is known as Artificial Emotional Intelligence (AEI). It also goes by names like Affective Computing and Human-centric AI.
Artificial Emotional Intelligence (AEI) can be described as a development and study of devices which can interpret gestures, tone-of-voice, facial expressions and factors similar to these – to determine a person’s emotional state – and respond to it. It is a mixture of computer science, cognitive sciences, and psychology. Some researchers working on this field expect the global affective computing market to reach $174 billion by 2025.
For businesses to understand the needs of their customers better, they have to ask the right questions. One of which would be ‘How would the integration of AEI help them – as service providers – function better?’ The answer here could be
Technically analyzing customer feedback Understanding the Customer:
When a customer invests in a company, money is not the only thing at stake. More importantly, it’s the customer’s trust that’s at stake. AEI should be ingrained in the fabric of customer interaction that happens at any end of the company. Instead of merely analyzing the words spoken by the customer, the system interacting with the customer needs to take into considerations factors like the customer’s tone, sentiments and facial expressions. The usage of these features would lead to a better and empathetic engagement. Thus, improved Customer Experience.
Let’s pinpoint some areas where AEI can really make a difference.
Presentations:
Neuro adaptive systems working on AEI can detect neurophysiological processes and respond automatically. They can give real-time feedback on whether the clients are engaged or bored in a presentation, by recognizing the facial expressions. This can help presenters have nuances in their delivery in correspondence to the real-time feedback.
VCA (Virtual Customer Assistants):
When a customer has to complain or has a query regarding some product, customer service executives (popularly VCA’s these days) are the ones that respond on behalf of the company. Here, instead of just responding logically to a customer’s questions, AEI can help VCA’s to make the customer feel comfortable and in trustworthy hands. This will surely boost the CEM (Customer Experience Management) factors and thus lead to customer retention in the company.
Negotiations:
While in a negotiation, to understand if you’re going in the right direction, it is important that both verbal communication and the emotional state of the client, including disagreement, fear and discomfort, are taken into consideration. AEI can come in handy here. By analyzing factors like facial expression, heartbeat rate and stress markers, and comparing them to the fed data statistics of particular emotions, the emotional state can be calculated to approximately 94% accuracy.
In the Office:
Ethnographic techniques work by monitoring sleep patterns, heartbeat rates, and thermal imaging. There are cameras installed at the office desks that continuously monitor and capture the thermal images of the employee. By analyzing the paranasal region of the face, it can detect stress and thus suggest the employees to take a break or play their favourite song. A happy employee is always a company’s strength.
Conclusion:
While we intend to make AI more human-like, we must also remember that the society we live in today, is not what we aim to build tomorrow as well. Instead of aiming these AEI algorithms to be more human-like, the end goal should be to make them better than humans, free of – our deep-rooted biases, inbuilt fears of taking risks, and a habit of procrastination. AEI would help in not just understanding and responding, but in reading between the lines and thus, ultimately delivering the apt products to the client, which in turn will enhance their Customer’s Experience.
