Don’t be afr(AI)d to catch feels
As organisations and brands find fresh, cutting-edge ways to use AI every day, its potential in the realm of marketing and advertising is practically limitless.
So what is the key application of AI to produce the best possible campaigns and strategies in 2022?
Emotions.
What exactly is Emotion AI?
Affective computing, commonly referred to as emotion AI, is essentially about detecting emotions through the use of artificial intelligence. Emotion AI is a subset of artificial intelligence (the broad term for machines replicating the way humans think) that has the capacity to assess, comprehend, imitate, and even respond to human emotions.
It’s no secret that our emotions drive our behaviours.
Companies that have previously been using focus groups and surveys to understand how people feel can now, with the aid of emotional AI technologies, capture the emotional reactions in real-time. Decoding facial expressions, analysing voice patterns, monitoring eye movements, and measuring neurological immersion levels; these are just a few examples of present-day deployments of the technology. Accurately deciphering emotions conveyed by facial expressions, through machine-learning algorithms dates back to the work of Silvan Tomkins, a pioneer psychologist who was one of the first people to research the idea of affect as a set of bodily processes.
By adapting to visceral reactions, and exploring the interplay between artificial intelligence, human emotions and advertising and marketing practices, the ultimate outcome for brands could potentially be a much better understanding of their customers — and even their employees.
The Emotion Economy
In today’s world of overstimulated consumers, it is particularly important for brands to identify and measure their emotions to understand the potential customers’ emotional drives and motivators.
It’s important to cluster the applications of Emotion AI under a broader umbrella of ‘Emotion Economy’. Futurist Richard Yonck first coined the term, describing the emotion economy as an ecosystem of emotionally intelligent devices and software iterations that will completely change the way we interact with machines. And, of course, the very definition of emotional marketing or emotional branding refers to a brand’s strategy that stimulates consumers’ affective state, appealing to their feelings with the aim of increasing consumer loyalty and maximising emotional connection with the brand.
In terms of ways of measuring engagement — with Emotion AI, the possibilities are virtually endless. From measuring changes in oxytocin levels, the brain’s “neural signature of emotional resonance” — to tracking the eye movements, or observing facial expressions such as a brow raise or upper lip raise, or even combining an analysis of both face and speech to provide a more well-rounded insight into the human expression of emotion. But Emotion AI technologies can go even further, helping companies connect with users at the deepest level possible, allowing brands to deliver authentic marketing, tailored to modern audiences across the globe.
Neuromarketing and Emotional AI.
A Competitive Advantage for Brands.
When we think about technology and AI — on the surface, it seems to be all about automation, productivity, efficiency. But how can brands leverage the technology and navigate this space in a way that fosters human connection and transforms our relationship with technology, and by extension, with one another?
From customer feedback to hyper-personalisation
As platforms increase in data, scale, and sophistication, exploring AI solutions for automated affect recognition is the natural progression for brands to gain legitimacy and contribute to social change.
We interviewed Andrew McStay, author of ‘Emotional AI: The Rise of Empathic Media’ and the leader of Emotional AI Lab — an international research group examining the social and cultural impact of artificial intelligence technologies in relation to data about human emotion, who said: “From the point of view of the digital advertising or digital marketing industry, given an intense interest in neuromarketing — well, it’s less about the kind of the communication in terms of going through traditional above the line media, but rather about creating deeply personalised ads that have got nice deep resonance.”
McStay went on to say:
“It’s fair to say that facial coding and the use of computer vision to detect a few basic human emotions hasn’t got much farther to go, I think. It’s a very discredited area. But what if we combine it with different kinds of modalities – the use of physical sensors such as wearables, which can pick up your stress levels, blood oxygen levels, heart rate, and so many more intimate measurements? Basically, it’s about triangulation as a way of dissipating the issues of accuracy and bias.”.
Combining behavioural and sensory data can enable brands to hyper-personalise physical and digital experiences — both online and offline. By deploying Emotion AI, brands can tap into the subconscious behaviours of the consumers that drive 95% of purchase decisions. And being able to tap into the audience’s visceral subconscious response through the use of Emotion AI technologies, it’s possible to capture that data at scale. With the power to create more personalised user experiences, with Emotion AI as the key to instinctive, unfiltered feedback, the technology could help brands achieve real-time empathetic marketing.
Using Emotion AI technologies to infer consumer feedback can be an invaluable tool, helping gain a deeper understanding of both existing and potential customers, as well as to stay competitive in a quickly evolving digitally altered market – and achieving this through real-time insights is critical to gain actionable data quickly. In doing so, running ad campaigns, delivering products or experiences and sharing messaging that resonates with customers deeply could help create an adaptive, customised experience — not for everyone, but for every one. Delivering relevant, accurate and authentic content by benefiting from Emotional insights to augment the customer experience across all ages could be a major differentiator for businesses in the next few years.
How is Emotion AI deployed at the moment?
The current use cases of Emotion AI in the advertising industry range from a better engagement and understanding of consumers, through media analytics and market research space measuring consumers’ emotional responses to advertising.
A fascinating example of what is possible is Affectiva, an emotion AI company based in Boston, specialising in automotive AI and advertising research. They use emotion AI technology to capture macro and micro facial reactions to advertisements.
“Our technology captures these visceral, subconscious reactions, which we have found correlates very strongly with actual consumer behaviour, like sharing the ad or actually buying the product”, says Rana el Kaliouby, Ph.D., a pioneer in Emotion AI, co-Founder and CEO of Affectiva. Using their “Multimodal emotion AI”, which analyses facial expression, speech, and body language, Affectiva can gain a complete insight into the individual’s mood, which can be later tied to actual consumer behaviour and advertising KPIs. This allows for the ability to track the emotional journey of a piece of content, which correlates very highly with potential virality but also purchase intent, purchase behaviour, brand perception and brand loyalty.
But how emotional should you be getting over Emotion AI?
Not unexpectedly, there are negative implications to the use cases of this technology that also need to be considered, including the concerns over privacy and the question of bias, with main concerns regarding the cultural variability of human emotion and the ways in which flawed AI systems perpetuate biases through their (mis)interpretation of emotions.
“Given that facial expressions are culturally variable, using them to train machine-learning systems would inevitably mix together all sorts of different contexts, signals, and expectations”, argues Kate Crawford — a researcher on the social and political implications of artificial intelligence.
According to the 2022 Edelman Trust Barometer — we find a world “ensnared in a vicious cycle of distrust, fuelled by a growing lack of faith in media and government”. A trust crisis in terms of how organisations are building and deploying AI at scale calls for an examination of the ways in which these technologies are developed — and whether those are just, fair and accountable. And all of these questions come into play as we think about humanising technology. Context, conditioning and culture are often ignored — recognising and acknowledging the social and political implications of Emotion AI could be a crucial step for brands towards paving the way for more ethical marketing practices guided by empathy, authenticity and societal accountability to ensure fairness and a more empathic mode of consumer research.
Demonstrating awareness of damaging stereotypes for how marginalised consumers are represented (either under- or mis-represented) can help brands become more inclusive in a present-day brandscape and contribute to social change.
What’s next?
Authenticity is about being relevant, being intentional, and showing that you care and value your audience — and doing that successfully can be achieved through knowing your audience and knowing which emotions would resonate most.
As for the future of Emotional AI and advertising, McStay adds: ” In terms of the in-house use of Emotion AI technologies such as facial coding – there is a range of different measures that you could use to respond to content and for the most part – that’s largely unproblematic and in terms of the use of technologies for their creative potential, I think there’s real scope. However, when you start looking at OOH – I think that’s where things get a little bit more tricky. Having cameras in public spaces that identify people and their emotions in real-time – you really have to consider who’s being exposed to that advertising. So it’s about approaching people in a way that is respectful and legitimate, never mind legal. And that demands a high level of creative work as well. So thinking about public-private balance, that’s where the law is currently really struggling at the moment. I think that is definitely changing and will impact how things will be done in the future.”
Looking ahead, the overall takeaway is that it is possible to integrate the technology in a transparent, meaningful and ‘human-centric’ way. However, AI deployments for surveillance and security spark the questions of ethics and in a broader sense, provoke a reflection upon our interactions with our devices, meaning that the rapid development of AI technologies needs to be supported by the necessary regulatory frameworks.