The Amplified Intelligence CEO on attention as a future-proofed currency

Professor Karen Nelson-Field, founder and CEO of audience measurement company and global attention economy leaders Amplified Intelligence, is the guest speaker at our upcoming Melbourne event, A Place At The Table. She recently delivered her expert take on attention as a future-proofed currency, titled New World: New Metrics, at the WFA Global Marketer Conference in Athens. Here’s what she had to say.

 

We are in a currency crisis

If you haven’t heard of attention, Nelson-Field says, you’ve been living under a rock for the past year. As an industry, we’ve never been more aware of how hard it is to get people’s attention and how difficult it is to drive trust and creativity and be unmissable.

But how can attention be a future-proofed currency? What will it take for us to change the audience measurement currency and think about what’s next? The answer, Nelson-Field says, is probably not what you think.

Attention as a future-proofed currency: the background

Why is attention important? Because, Nelson-Field says, if your ad isn’t seen, you can’t gain trust, and you can’t sell your brand. And if you can’t gain trust and sell your brand, your brand won’t grow, and you won’t gain mental availability.

Why are new metrics becoming more and more important for marketers? Because getting your ad seen, even by a few, even for a fleeting second, is getting harder and harder. Marketers are acutely aware of how difficult it is for an ad to be seen by a person, and that’s reflected in the human data Amplified Intelligence collects at scale through AI and facial recognition.

Nelson-Field says, our current currency is letting us down, and marketers are starting to understand that. It was built at a time when the internet was unregulated. The MRC Standards were brought in and they, at least, did an amazing job of ensuring an ad had some type of verified OTS. But our current currency is failing because what humans are good at is avoiding. We’re experts at avoiding. And a lot of ad avoidance happens because of the functionality of the platforms that advertisers buy into. We know, Nelson-Field says, that one of the biggest causes of distraction is the function of the platform itself, and it’s a real issue. Data shows that around 75% of the online ads brands pay for, measured against the currency they trust, don’t deliver the value they believe they do. On average, 30% of ads that advertisers are paying for are not MRC compliant, while 44% are viewable with less than zero seconds of attention. Nine per cent are viewable with no attention, and only 17% are viewable with two seconds of attention. That means that around 75% of online ads that brands pay for deliver them no human attention. And that, Nelson-Field says, is pretty scary.

If the gap were consistent and/or transparent across formats and platforms, it would be okay, she argues, because it could be adjusted and have an index applied, and all would be well. But the problem we face is that the gap is different. The currency is not generalisable across different boundary conditions. One of the basic premises of a currency is that it needs to have those conditions brought in so that currency changes can be understood. If a currency is not flexible or adaptive to those different boundary conditions, the result is that brands don’t know whether their ad was seen by 7% or 25% because the gap is different by platform and format. That’s a scary thing, says Nelson-Field, and that’s why our industry is in crisis in terms of measurement.

Creativity vs platforms

Each platform has its own attention elasticity, and we’re starting to understand what this means. The creative of an ad is tempered by the performance of the platform it’s on, and Nelson-Field says there’s nothing advertisers or agencies can do about that. Because the inherent nature of a platform’s functionality is the thing that tempers an ad’s performance, creative has little chance to shine beyond this elasticity. We’re seeing it more and more often, she says. Some in the industry say that creative drives attention. Nelson-Field unequivocally says it doesn’t. Creative plays within the boundaries of the platform that say, ‘here’s how much attention we’ll give you’, she says. We can’t forget that humans have learned ad avoidance behaviour.

The true cost of inequitable impressions

The problem Amplified Intelligence solves for as a business, and the subject of much of Nelson-Field’s research as an academic is the flow-on effects of inequitable impressions. Inequitable impressions impact more than most think, she says. And it’s not just the obvious: any measurement system, model, methodology or concept that relies on inequitable impressions will fail. There are so many flow-on effects from inequitable impressions—for example, an ad’s ability to drive mental availability. Amplified Intelligence has just looked at this across five countries and found that not only is the ability to gain mental availability tempered by the platform’s performance, but even worse, poor performing platforms drive more misattribution to a brand’s competitors.

So how do you be unmissable when a platform allows you to be missable? Anything that advertisers rely on equitable impressions for, like share of voice, mental availability, MMMs, and, of course, reach planning, is built on shaky ground. The whole ecosystem, Nelson-Field says, is in a currency crisis.

Where to from here?

So, Nelson-Field asks, how do we move forward as a currency? First, we need to stop measuring inward and start measuring outward. Device metadata is the problem, she says – it tells you little about human behaviour. We’re always considering device data as our go-to currency, and we’re failing to look outward at how humans behave. That was the easy default in 2014, and Nelson-Field pays tribute to those who built the technologies, but now she’s busting myths about scroll speed, ad length and time on screen.

• Scroll speed is said to be a predictor of attention – but, Nelson-Field says, it’s not. It’ll tell you if someone is looking at the screen, but it won’t tell you if someone is looking at the ad. People are constantly switching their attention as the ad is appearing, not looking at it sustainedly.
• Ad length is said to be a predictor of attention – it’s not. Just because an ad is long doesn’t mean it’s bestowing more attention. According to Nelson-Field, it’s actually driving more wastage.
• Time on screen is said to be a predictor of attention – it’s not. It’s actually a predictor of non-attention and distraction. Through its research, Amplified Intelligence can see where people are looking while they’re on these platforms. People might hold the phone and thumb the screen so it doesn’t go dark, but their attention is elsewhere, and they’re not looking at the screen. So, brands are spending money on people who are looking anywhere but at the ad.

Metadata vs human data

A lot of the aggregated products we see in the attention space are based on this metadata, Nelson-Field says, so that will fail us, as a currency, in the future. When you look below the metadata, there are more nuances in human behaviour and individual attention data. Human data tells a human story. But it’s also predictive. Amplified Intelligence sees what they call the shape of attention in its work. If you understand how a human views something, Nelson-Field says, it has stronger relationships with outcomes than metadata alone. She predicts that clusters of human attention will be the backbone of a new attention currency.

The importance of robust data

Nelson-Field points out that a functional attention currency needs good data, not what she calls ‘Lego bricks’. She refers to an infographic currently doing the rounds that was built out by a graphic designer showing how supposedly easy it is to build product. If you’ve got data, and it’s sorted in range, and it’s nicely presented, then, therefore, it’s actionable, according to the infographic. By contrast, Nelson-Field advocates taking a less hurried approach. Let’s not rush to build product just because there’s a land grab in attention right now, she says. Because if the bricks are dodgy, the tower will fall, and it’s the same with attention product. If the data is not robust, brands risk infected systems and meaningless outcomes because they’re ingesting that data into their systems, reach, frequency planning, and everything else. She cautions advertisers to be super careful and fussy about the data, not just about the data they collect but also the knowledge around it. As a trained researcher, she’s not convinced of an outcome unless she understands all the things around it. A single instance of wisdom just doesn’t cut it.

What is good data?

To answer this question, Nelson-Field starts with Marcus Vitruvius, a Roman architect and engineer from the 1^st century BC. He built guiding rules on best practice architecture and engineering, and his whole philosophy is the guiding rules that lead to the endurance of structures. He said to abide by these rules will prevent structures from falling to decay. And Nelson-Field believes that moving forward, an attention currency should have guiding rules too. Here are her rules for good attention data.

• It needs to be human – that’s a no brainer. Let’s not rely on metadata, she says. Let’s look out.
• It needs to be privacy safe. We need to be super careful about the scale of our ability to spy. There are some spyware products out there now, and they have Nelson-Field worried.
• It has to be natural. People can be trained to pay attention to do some sort of viewing system, but it might not be in a natural environment. So, it has to be completely natural.
• It has to be accurate. Systems can easily be sent off on the wrong path. Was the ad looked at, or was it not? Is it consistent? Nelson-Field’s background is that generalisability is the key to laws, so she looks for that in her own business. It won’t be until Amplified Intelligence has seen the same findings across five or six countries, or different boundary conditions, that it will be released.

Nelson-Field says, “as we build an attention-based audience measurement category, guiding rules around data quality must be followed … only then will an attention economy endure.”

What will it take to make an attention currency functional?

People use the word currency, but what they really mean is measures. Attention is not a currency yet – at the moment, it’s just metrics. There’s a big difference between the two. Amplified Intelligence is still a measurements business, Nelson-Field says. It’s not a currency business yet.

A currency takes three things:

1. Value. Why is it important, and what problem does it solve? As a business, Amplified Intelligence has only productised in the last 18 months. In the years before that, it was Nelson-Field’s job to make sure attention was a) different to viewability (it sure is, she says) and b) uncover whether it is valuable to brands (it sure is).
2. Stability. Is it stable across boundary conditions? If you go to collect in one country, will the conditions be much the same as in another country, or does the model fail? How stable is attention, and how adaptable is it to when media changes? As soon as Facebook, Google or any other platform changes a format even the tiniest bit – an aspect ratio, for example – how reliable are the models? Will they fail?
3. Unity. The collective agreement from ‘the who’ on data standards, privacy, usage, accreditation, and compliance.

These three things must happen together, Nelson-Field says, because it’s a dysfunction if there are two but not three, and we’ve seen that through the history of time. We need all three to make sure the currency is future-proof.

To wrap up, Nelson-Field outlined her Attention Economics 101.

• If we don’t look outward, no VALUE will come from change
• If we don’t use quality data, no STABILITY will be established
• If we don’t bring key stakeholders in, no UNITY will be gained

So, how can attention be a future-proofed currency? According to Karen Nelson-Field, “the only way attention can become a functional currency is when individual-level attention data is used to build models that account for different boundary conditions and are continuously refreshed as conditions change.”