February 26, 2020
Taking to the stage at Spark North, Lumen MD, Mike Follett, shared some eye-tracking work that has highlighted the impact of attention for advertisers.
Lumen's planner friendly metric of ‘Attention per 000s’ gives you a single number that demonstrates print environments deliver more attention than mobile and desktop. This metrics is based on the following calculation:1000 ads x % chance of being seen x seconds of attention. Attention per 000’s is based on Lumens huge eye tracking database which is made of hundreds of thousands of impressions across print, desktop and mobile and measures eyes on adverts in a variety of environments.
Media buyers can use this data to assess the relative impact of placing ads in different media.
If you break this calculation down, it demonstrates that whilst % viewed is similar in print and on mobile, the amount of time spent on the page of printed content is longer than on mobile, which gives you more time to actually see an advert.
Although not able to break this number down into magazines specifically, Mike demonstrated that the % of attention per 000s viewable (as opposed to viewed above) is very high for printed magazine magazines, second only to TV. He also revealed that quality publishing environments online have higher attention per 000s impression than the average for other online display inventory.
Through individual client studies Lumen data has also been able to show a strong correlation between attention to advertising and subsequent ad recall, in both print, and, to a lesser extent, online.
They also have case study evidence that demonstrates a strong correlation between attention and sales. This has been proven via their 250-strong panel of respondents who download software to their home computers which records the ads they could see, the ads they actually see and builds a predictive model of attention which can be used in attribution models to explain sales
When applied to online attribution modelling, there is a strong correlation between the ads that we predict people are likely to look at and subsequent clicks and sales.
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