How Emotional AI Can Benefit Businesses
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
YOU MIGHT ALSO LIKE
Brands today chase various metrics to justify their investment in campaigns and advertising, and perhaps the most important one is the customer lifetime value (LTV). However, less than one third of advertisers worldwide think so. LTV is a measure of how much a shopper is worth to a brand throughout their relationship. Boosting LTV is vital for brands to drive conversion and build customer loyalty. For instance, users who discover and install an e-commerce app are much more valuable than those who install the app through ads. According to AppsFlyer, 10.9 percent of global organic app users made a purchase within three months with calculated LTV of US$20.63, compared with 8.9 percent of nonorganic users with LTV of US$7.10 only. One key strategy brands adopt to increase LTV is personalization. When customers find the most relevant content, products or a brand experience that is tailor-made for them, they are more likely to return and less likely to leave. This holds true at every stage of the customer lifecycle, from being made aware of your brand to engaging with your content, making the decision to purchase and eventually staying with you as a loyal customer. While there are many techniques to
It is a question facing every marketer – in today’s fast-moving world, how do you stay relevant? One of the most effective ways is to make sure you are acting on the latest data possible, in order to serve them up-to-date offers that meet their current needs. It is no good sending a coupon for a set of kitchen knives if your customer only showed a fleeting interest in them last week. With the help of real-time marketing automation, you can engage with your customers not only in a timely fashion, but also at scale. The Importance of Engaging Audiences in Real Time As with all data-driven marketing, the quality of your output depends on the quality of your data. If the data is inaccurate, it cannot help you market effectively. It needs to be representative of your customers’ current thoughts, actions and behaviors, in order to help you create marketing content that they find more relevant. Also, customers’ online behaviors and interests change rapidly, and there is no shortage of alternatives for them to choose from the internet, providing plenty of distraction. If they express an interest in a product or service, there is no guarantee that it will
Demand for e-learning has grown significantly over the past decade as people and companies increasingly look to upskill online. According to Business Wire, the global corporate e-learning market is expected to be worth US$49.87 billion by 2026. The US and Europe make up over 70 percent of this, while Asia Pacific is the fastest-growing region, with an anticipated growth rate of 20 percent a year. Some of the big players in the market include Udemy, SkillShare, and Coursera. Despite the positive prospect, what do e-learning providers need to do to play the long game in this thriving market? The answer lies in smart strategy and advanced technology like artificial intelligence (AI) and deep learning. The Rise of E-Learning This growth in the e-learning market is being driven by several factors: A glut of new e-learning technology, the need for talent enhancement and retention, and a desire for more convenient and cost-efficient ways to learn. The COVID-19 pandemic has only accelerated this growth by pushing people to spend more time online. However, competition in the e-learning industry is increasingly fierce, and there are still several challenges providers need to overcome. The mental hurdle is one. E-learning is still a relatively new