What we have learned in six years of measuring attention
Jean-Paul Edwards
2 Novembre 2021

This article was originally published on The Drum.

By Jean-Paul Edwards, Chief Product Officer at OMD, with editorial input from Teemu Neiglick, Chief Executive at OMG Finland, and Markku Mäntymaa, Chief Executive and Founder at Viomba.

Attention is a hot topic now – this wasn’t the case six years ago when we started our attention journey in Finland with visual attention martech company Viomba.

After years of working with digital attribution models, it was clear that there was too narrow a view of marketing effectiveness. The click/conversion approach was far from being flawless and all manually set weighted models seemed to not tell the entire story.

It was our first attention measurement with real campaign data, real panelists and constantly connected eye tracking in 2015 when the lights were turned on. Visual attention levels were on average only 21%, so shockingly 79 % of impact was immediately lost. Almost 80% of the story so carefully crafted never had a chance to make an impact. Such a waste of marketing efforts and money.

Of course, attention has always been understood as being significant. If an ad isn’t seen, it can’t have an impact. It’s an investment with zero return. In digital media an enormous amount of money is invested into the opportunity to be seen. The more marketers start analyzing attention levels, the more competitive strategies they can create. Now marketers can use attention martech to instantly predict what proportion of impressions and creatives will be visually seen in live media, for how long and what is the cost for seen ads. Ultimately, this means brands can now actionably stand out from the attention clutter to create competitive advantage.

Over the years the amount of visual attention data gathered has expanded to over one million impressions on different ad formats and various site placements across several markets. From this data pool, we can define broad principles for brands seeking to maximize their digital marketing effectiveness through better attention performance.


Insight 1:  Different channels, different challenges to solve

Viomba data shows that social media channels deliver on average high initial attention levels due to the horizontally fixed news feed which don’t require our visual senses to work as hard to find objects. However, focused attention drops fast if the brand is not recognized, or the ad can’t create sufficient attention pull with other users.

Also, varying screen sizes create a different kind of visual environment to consistently measure. Instant attention levels on mobile are on average higher and there are less low attention placements. Whereas on a desktop, our eyes have much more freedom to maneuver around to detect visual objects. Even though desktop generally requires more eye work to fixate onto an ad, but once it happens, focused attention and memory impact is often more intensive and longer than on mobile. TV is a very different environment as ad messages are full screen, where our attention only competes with the surrounding environment.

Insight 2:  Media selection and ad format are powerful levers

There is huge variation in attention of almost 10x between the best and the worst media selection and almost 5x between the best and worst ad format on desktop, while the differences on mobile are lower. There is no correlation between media prices and high attention levels as formats are priced by size/length and opportunity to see, not based on what is visually more likely to be detected.

Long-term attention data across various markets clearly shows that there are media environments which require a fundamental re-think. In regularly used sites, visitors seem to have learned ways of browsing the sites to avoid the presented ads. On the buy side, a greater variety of sites and more dynamic formats should be considered to gain higher attention levels. On the sell side, ad formats and media specific placements should be shuffled around regularly to keep visitors’ visual senses more curious and alert.

Insight 3:  There are attention golden hours

As we are humans, not robots, our attention levels differ through the day. There are certain times when our visual attention responses are higher than average. One of these periods is early mornings.

Insight 4: Attention benchmarks differ across markets

When we compare attention data between markets, we can see significantly differing levels of attention for campaigns with similar parameters. There is a strong correlation between heavier advertising levels and lower attention levels. In markets with higher ad loads, attention metrics are to be optimized against local benchmarks and it is more critical to use tactics learned from continuous attention measurements to stand out.

Insight 5: Two distinct creative challenges

Creative assets face two related but distinct challenges – to capture and to keep our attention.

To capture initial attention can be mastered by any marketer as there are clear learnings to gain on how to improve creatives visual response levels. DCO-tools are great for testing different creative approaches and finding above benchmark attention levels for them.

The more difficult part is how the initial visual attention can be progressed to continued engagement (one day memory impact requires 3 seconds of focused attention) – this is already far more nuanced and specific to the brief. Since successfully attended display ads mostly generate less than 3 seconds fixated gaze time, impactful exposure frequency is required to reach efficient memory impact. Fortunately, with actionable and well-integrated attention, martech marketers can find out exactly how they can develop their own creatives and buying strategies to keep attention levels strong enough. Of course, if we are missing the initial attention, there won’t be even a chance for that.

Visual attention martech has come a long way in the last six years. Attention data has already been successfully enhanced with AI algorithms and the empirical data is constantly looped with AI to keep up with the ever-changing media. By partnering with Viomba, we’ve been able to integrate its industry leading attention martech into our local media buying systems and made it fit buyer’s existing work routines. Coming full circle, attention data is driving better attribution modelling and high attention ad inventories, as the new currency in digital marketing is evolving away from theoretical CPM towards real attention based aCPM (seen). Marketers are – and should already be – considering only ads really seen to define and read all existing KPI’s much more realistically.

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