So what is attention, really? According to the World Advertising Research Center (WARC), attention is defined as “concentrating awareness (even if fleeting) towards a reduced number of stimuli in our environments, while ignoring other stimuli.”
Today, solutions exist that try to measure attention by way of proxy indicators.
Signals like clicks, scrolls and mouse hovers are valuable, though represent an incomplete picture of attention. Interaction data are reliant upon overt interactions, valid for interactive experiences, not so for non-interactive ones.
Furthermore, interaction and click-based metrics create an incentive to produce clickbait versus a high-quality user experience. Heavy clicking and scrolling might suggest to you an attention outcome.
However, it might actually mean you’ve delivered a highly interactive experience that commands poor-quality attention, confusion, and dissatisfaction.
Consider the analogy of a customer service phone tree, which forces frustrated users through multiple paths over a long duration, resulting in poor outcomes. Is this quality attention to celebrate? Of course not.
That is why digital proxies are best suited for detecting the possibility of human presence through interaction. Interaction metrics are relevant to digital experiences whose goals are predicated on heavy interaction.
The danger is these metrics penalize highly engaging experiences that require leaning back and applying focus, such as with video.
What about metrics like screen real estate, video completion rates or audibility? You’re likely to achieve better attention outcomes if your ad is viewable on screen versus not, or if your video plays until the end, or if the sound is on. But the potential to see and hear is not attention.
What if the ad is partly viewable on a website page for six seconds, though your attention is focused on another page, in another tab, inside another browser, or on another device — for that same six seconds?
What if the ad is audible but the volume is turned down while you stream a podcast from your mobile phone via headphones full blast? What if the video ad completes, but you put your device down to go to the bathroom two seconds in?
Because of their prevalence, it’s worth calling out self-reported metrics that stem from surveys and focus groups. These techniques are frequently misused and crude proxies at best.
They rarely produce actionable or reliable attention measurements against individual media consumption sessions — particularly ones found in brand lift studies with weak or no correlations to real-world outcomes like sales.
The propensity for brand bias and precise measurement make these tools questionable for measuring or managing to attention.
Marketers should consider the growing discipline — and body of evidence — around real human attention measurement.
Techniques like brain scanning (i.e., EEGs) or other body measurements have long been used in academic settings, as well as in high-stakes advertising situations for testing content. They can yield powerful learning and insights.
At the same time, these in-lab techniques present challenges because they don’t scale, they are expensive and they create unnatural brand experiences.
These techniques are especially challenging during a pandemic where you can’t have in-person access to people.
They may be good for optimizing your Super Bowl advertising creative debut, but the rest of the marketing industry still needs a solution for the exploding volume of everyday digital creatives and targeted campaigns.
Facial coding and emotion AI is proving to scale in a cost-effective manner in the case of viewing video and using apps. It is natural and observational, and proving reliable and predictive of attention and other real-world outcomes.
Facial coding can be carried through computer vision technology and machine learning draw human response measurements. It relies on opt-in viewers to enable their device cameras to anonymously detect their facial cues and capture response second by second, frame by frame.
Emotion AI is applicable both in measuring the attention-earning potential of creative in forced-exposure testing situations, as well as in live in-context situations like movie viewing, app usage, gaming and website browsing.
The precision and granularity of emotion AI is powerful for developing marketing models that predict real-world outcomes (like video completion rates and social sharing), as well as attentiveness.
While attention is what enables a media or brand experience to enter a person’s consciousness, it is emotion that controls attention and creates memorability. This intelligence enables prescription to measure and optimize creative, media and audience for attention outcomes.
Attention management in advertising is about moving away from potentials and probabilities, and instead moving closer toward real and definitive outcomes.
That starts with marketing and advertising leaders prioritizing attention as a key outcome and aligning their brand goals, strategies and organization around key attention measures.
Attention management is becoming a foundation of advertising. Marketing and advertising leaders who get ahead of it will experience performance gains in the near term, and significant competitive advantage over the long term.
To embrace attention in advertising is to advertise responsibly.
Max Kalehoff is the Vice President of Marketing and Growth at Realeyes, the world’s leading attention measurement and emotion AI company. With 15+ years of experience in brand building, customer development and revenue generation for high-growth businesses, he leads high-performing marketing teams to success by being open-minded, data-driven and agile.Reblogged 5 months ago from www.clickz.com