Attention seeker: Getting results from digital’s most dynamic metric

Attention seeker: Getting results from digital’s most dynamic metric

Attention data can be used to cross-pollinate a variety of digital ad opportunities and improve creative execution, but many brands haven’t been deploying attention data the right way.

With channel diversity on the rise, metrics like attention time are offering new ways to cut through the noise in multiple environments. Technologies including eye-tracking and neuroscientific insights have the potential to kickstart a mature, world-leading market that can interpret exactly how to maximise audience attention in real time.

Yet, like all emerging innovations, attention tech has yet to live up to its hype. Following the classic Gartner hype cycle, we can see that the attention economy — now past the stage of “inflated expectation” — risks falling into the dreaded “trough of disillusionment”.

The problem is many advertisers haven’t been deploying attention data in the way that they should, stalling its evolution into a game-changing tool. Here’s how we can move beyond the impasse to reach the promised “plateau of productivity”.

Take a more incisive approach

There are two major stumbling blocks facing attention tech right now.

First, advertisers aren’t using it enough to generate actionable data. Typically, agencies are limiting the role of advanced attention metrics to small, limited-spend campaigns. This toe-dipping instinct goes against the grain of what attention — in its fullest capacity — is designed for.

The more insights we build around sophisticated attention tech, the better we’ll understand how it can be optimised across different digital channels and in relation to specific campaign elements, eg. high-impact creative formats.

As well as measuring the wrong things, we’re frequently measuring them in the wrong way too. Since attention tech is far more progressive than click-through rate (CTR) as a measure of ad engagement, relying on post-campaign analysis (as CTR is calculated) makes no sense.

Rather, brands must learn to use attention during a live campaign, drawing from in-flight learnings to optimise whatever is getting the most attention.

Be smarter with cross-platform spend

GumGum partner Playground XYZ recently launched its Optimal Attention framework, enabling brands to define the minimum attention time (the length of time, in seconds, that an ad is directly looked at) needed to drive tangible campaign outcomes.

Heineken-owned lager brand Cruzcampo used the metric to identify the attention time required for brand lift across each of its key creative assets in a digital video campaign on YouTube and Meta platforms.

Using real-time attention rates, Heineken recognised how user-generated and influencer content outperformed its business-as-usual assets. Actual media delivery on YouTube also exceeded the optimal attention threshold by 355%, meaning there is potential for Heineken to use different, lower-cost formats while continuing to increase reach by fulfilling attention targets.

Applied with this level of precision, we can see that differences in channel (eg. YouTube versus Meta or mobile versus connected TV) significantly impact how an attentive second converts into brand lift. Understanding this subtlety makes attention data a powerful tool to help shape platform strategy and the allocation of adspend.

Explore cross-pollination advantage

Channel strategy is just one example of how attention data can be used to cross-pollinate a broad spectrum of digital advertising opportunities. Take contextual targeting, a cookieless technology that’s surging in 2024 ahead of Google Chrome’s third-party cookie clampdown. The application of this scalable, sustainable solution can be enhanced significantly when used in tandem with real-time attention data.

For example, in combining attention and contextual insights in its “50% Off Value” campaign last year, pizza chain Domino’s was able to identify the contextual categories where attention levels for its creative assets were highest.

Ad delivery was then optimised towards the high-performing categories and away from the underperformers, driving an overall return on advertising investment of between 135% and 398% for each creative element.

In addition, the campaign generated an additional 3,000 hours of attention on just one creative element alone — all by using attention to drive greater contextual efficiency.

Enhance creative engagement

We already know that high-impact creatives have a striking effect on brand recall. But this pivotal relationship with attention works both ways. One of the biggest advantages of rich attention data is that it can be used to continuously improve a brand’s creative execution.

Instead of taking a “set and forget” mentality to creatives, advertisers can use thousands of digital data points to recognise what features of a live campaign are performing the strongest. For example, which elements of an in-game ad capture the audience’s gaze the longest? Does a brand logo appear early enough in a Meta animation to surpass optimal attention targets?

Attention data can also tell which ad formats work best in parallel with specific creatives. These learnings then create a continuous feedback loop for any digital campaign. They extend the shelf life of individual creative assets while also forming the basis of deeper relationships with brand audiences.

The hype cycle shows us that attention tech has the ability to solve real-world problems, with revolutionary results (aka the productivity plateau). But, to do this, we need to be making more of its innovative capacity to mature the industry.

This challenge also comes down to better guidance from tech vendors. With a stronger education effort, we stand a better chance of nurturing attention to the point of universal adoption, sparking a new era of meaningful audience interactions.

Peter Wallace squarePeter Wallace is general manager, EMEA, at GumGum

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