Share on facebook
Share on linkedin
Share on twitter
Share on facebook
Share on linkedin
Share on twitter

How Emotional AI Can Benefit Businesses

Author | Min Sun, Chief AI Scientist, Appier

Emotion is one of the most distinguishable human qualities, one that sets us apart from machines. However, it is not out of the realm of possibilities for machines to read emotions and respond accordingly. Increasingly, machines are able to interpret human’s emotional states and adapt their behavior to give appropriate responses – something we call emotional AI, or artificial emotional intelligence (though in the computing field, it is known as affective computing).

Here we will explore what it is, how it works, and how it can benefit businesses.

 

Three Types of Emotional AI

Emotional AI is the next step in the evolution of artificial intelligence. By interpreting people’s emotions, AI can respond in a much more naturalistic manner, making the interaction much closer to typical human intercourse.  

There are three main types of emotional AI – natural language text analysis, voice analysis and facial expression analysis. The first two are already quite common, while the third probably attracts the most media attention. Other types of analysis also include mouse movement, eye-gaze, heart rate and electrocardiography, etc.

Natural language text analysis

It involves AI scanning written text like a review of a product or service, online articles or tweets, and then picking up on the sentiment of whether it is positive, negative or neutral.

Voice analysis 

This analyzes a user’s speech signals like their vocal pitch, intonation and tone as well as the words they use to determine their sentiment. For example, someone with a dry sense of humor might say the opposite of what they actually mean for comic effect, but using voice analysis you could pick up on the true meaning of what they are saying. This is especially useful in call centers – detect an angry tone of voice from a caller, and you can transfer them to a human operator rather than risk frustrating them further by making them deal with an automated system.

Facial expression analysis 

This is perhaps the most interesting one. Using a video camera to read someone’s facial expressions, AI can analyze their emotions, and from that you can infer their state of mind, their intentions, whether they are lying or being genuine, and so on. Some startups already use this in their job interviews to determine whether the interviewee is nervous, confident, or sincere about his answer, etc. It also has enormous potential for financial services companies, such as banks or fintech firms, when they are deciding whether to approve a loan for someone.

 

Analyzing Emotions for Retail: Online and In-Store

Emotional AI will be useful to all kinds of businesses. If your company needs to understand human emotion in order to make a decision, you will have a use for emotional AI, as it can help automate this analysis and hence these decisions. Nowhere is this truer than in the retail space.

Using cameras in stores, emotional AI can observe your customers’ facial expressions, how they walk, and other variables that will help determine their emotional state. For example, if someone is frowning a lot and walking at a very fast pace, you could deduce they are stressed and in a hurry. In which case, you could advise your salespeople not to approach them to tell them about your latest offers.

We can do the same thing online. Instead of seeing a shopper’s body language you can analyze their online behavior patterns. If they use their mouse cursor very aggressively, for instance, AI can infer they are stressed and/or in a hurry, and less open to offers. On the other hand, if they hover the cursor over the ‘Buy now’ button for a while, they might be indecisive, and so it could be a good time to send them a coupon for a discount or free shipping to help convert them into a paying customer.

 

Nailing an Emotion: Accuracy and Interpretation

So how accurate is emotional AI?

In the case of facial expression analysis, researchers have defined about 64 different facial expressions and micro expressions. AI can detect these with a pretty high degree of accuracy, and this will only improve as the technology develops – like other forms of AI, emotional AI improves as more data you feed it. So, accuracy isn’t the issue. 

Problems arise when you get onto the question of interpretation. Just because someone pulls a certain facial expression in response to a certain question, that doesn’t necessarily mean they are lying. It is a big leap to go from recognizing a facial movement usually associated with one kind of emotion or behavior, to assigning a certain motive to that expression. People’s emotions can be quite unique, and different people show emotions differently – there is a huge amount of variation in how people react to certain situations. You have to go person by person, and be wary of devising universal rules that you apply to everyone the same.

In demos, the facial expression recognition feature of emotional AI looks very impressive. That is because in those demos, researchers asked actors to pretend to be nervous, and AI picked up on that emotion. However, actors tend to over-emote – in the real world, people behave very differently. In real scenarios, I would say it is accurate 70 percent of the time, but use an actor and that rises to about 90 percent.

 

Coupons Yes, Job Denials No: Putting It to Good Use

You probably shouldn’t use a system with 70 percent accuracy to make “final” decisions like whether to hire someone or deny them a loan. That is because you are taking the final decision out of their hands, which is a lot of responsibility for a company to wield. However, pairing humans and AI to make an improved final decision can already be useful in certain scenarios.

For instance, emotional AI is very well suited to marketing decisions like whether to send someone a coupon. With this kind of decision, the final say is still in the customers’ hands, rather than the company’s. You as a marketer are just nudging the customer to complete the purchase.

Marketers should start preparing for emotional AI by focusing on data that reflects your customers’ emotions. For example, a customer call center can record all calls, and a website can store all user reviews for analysis. Use this data, and soon you will be able to leverage this technology to make more effective business decisions.

It is still relatively early days for deploying emotional AI in the real world, but it is a crucial step in the development of AI, and absolutely essential if we want to build an AI that interacts naturally with humans.

 

* This article was originally published on Campaign Asia

WE ARE HERE TO HELP

Let us know the marketing challenges that you’re facing, and how you want to improve your marketing strategy.

YOU MIGHT ALSO LIKE

How to Elevate Your Social and Search Advertising for App Install

With mobile use still growing globally, a smartphone application is a great way to drive engagement. But how do you convince customers to download it? One of the best ways is by leveraging predictive segmentation and keyword targeting powered by artificial intelligence (AI) on paid search and social media platforms. Mobile apps are more popular than ever, and they are not going away anytime soon. According to App Annie, annual mobile app downloads are expected to reach 258 billion in 2022, up from 205 billion in 2018. Due to the impact of COVID-19, consumer spending in apps hit a record high of US$27 billion in the second quarter of 2020. With app revenue following a similar trajectory, it is no wonder that global app install ad spend will more than double in the coming years, from US$57.8 billion in 2019 to US$118 billion by 2022, according to AppsFlyer.   The Key Drivers of Increasing App Install Ad Spend One of the biggest drivers behind this growth is competition. Brands are fighting to deliver personalized customer engagement and increase conversions, and a mobile app is one of the best channels to achieve that. The latest data from App Annie shows that

Advocating for Diversity and Inclusion, Day In and Day Out

Today, March 8, is International Women’s Day (IWD), when governments, employers and women themselves celebrate female success and the contributions that women have made to society. Advocating for women is critical, and as a growing technology company, we certainly shoulder some of the responsibilities alongside others in the industry. However, we can’t advocate for women or any other groups on just one day of the year. Organizations including ours need to look at diversity and inclusion on a daily basis, and make sure we are considering it in every area of the business, from hiring to team structure and recognizing achievements. It is proven that diverse and inclusive teams solve business problems faster, allowing things to get done more quickly, and also make for happier and more productive employees. So how can companies make sure they have a culture that welcomes everyone and gives them space to contribute and experiment? It certainly starts with hiring. At Appier, we focus on skill-based hiring, making sure we have the best people to do the job rather than look at race, gender or any other identifying factor. Culture fit is also incredibly important. Appier is a startup, so collaboration across functions is key

AIQUAホワイトペーパートップ画像

How Your Travel Business Can Use AI to Drive Meaningful Engagement

Your travel or tourism enterprise has probably benefited from the recent upsurge within the sector overall. In 2017 alone, the industry contributed over US$8.27 trillion to the global market. So how can you use this demand to win not just short-term gains, but also long-term value? It’s not easy when — along with major growth — the travel and tourism sector has seen other changes too. The spread of online travel agencies, the rise of artificial intelligence (AI), and customers’ demands for unique destination experiences and seamless self-service all have a role to play in how you attract and retain customers.     The Data Dilemma One challenge is dealing with the vast data generated in your operations, such as information about your customers’ travel destinations, preferences, budgets, and much more. Turning it into meaningful customer experiences can lead to higher conversions, stronger engagement and repeat business. But, even with all the data you have, there’s a whole element that may be missing from your efforts. Travelers are looking for an easy search process to find personalized and timely offers of just the right destination. For that, you need knowledge about their interactions elsewhere online, such as travel content consumed