Using ‘attentive seconds per 1,000 impressions’ to compare the impact of different media opportunities
It is notoriously difficult to compare the relative efficacy of different advertising inventory. The different currencies employed by the industry can mean that too often you end up comparing apples with oranges. We can use the attention funnel approach to compare the ability of each media to persuade people to look at advertising at all, and how good they are at holding people’s attention. This allows us to compare apples with apples.
VIEWABILITY AND VIEWING
As previously noted, in general there’s a big difference between what people could see and what they do, in fact, end up looking at. And there are big differences across different media, too.
In the first place, we can apply the MRC viewability standards (arbitrary as they are) consistently across all the media under review. We can see that not all TV ads are viewable. Yes, they appear on the screen, but sometimes there’s no one in the room to watch them – or those in the room have fallen asleep. TVision estimate that 74% of 30-second TV ads play out to someone in the room – meaning that 26% play out to empty rooms. It should be noted that in the UK, the TV ratings body BARB says that it takes into account when people are or aren’t in the room via its people meters, but it’s interesting to see the US data in the light of TVision’s insights. Again, just because an ad is viewable doesn’t mean that it will be viewed. Someone can be in the room while an ad is playing out, but it doesn’t mean that they are definitely looking. In fact, only 43% of 30-second TV ads get looked at. People may be in the room, but they may be checking out their phone, reading the paper, talking to loved ones, or getting the kids ready for school.
Almost all YouTube ads are viewable, and the vast majority of them get some attention.
Interestingly, when it comes to social media, many ads fail to meet the stringent MRC viewability standards, but do get some attention, leading to an interesting anomaly where viewing rates are higher than technical viewability rates.
Bringing up the rear of the chart, the desktop and mobile web data shows us the reverse: if ads are viewable to MRC standards, that is no guarantee that they will get viewed.
EYES-ON DWELL TIME
Next, we can look at how long people look at ads for. Here we can see that if people look at TV ads, they tend to look at them for a long time, relatively speaking: a 30-second TV ad will generate around 13.8 seconds of eyes-on dwell time, on average.
Within this average, some people watch the whole 30 seconds of the ad, others only glance at it for a couple of seconds, and there’s a wide distribution of viewing behaviour in between. But for simplicity’s sake, we use the mean average as a benchmark.
A 15-second YouTube ad will not get watched for the 15 seconds. On average, eyes-on dwell time with 15-second unskippable YouTube ads is 4.9 seconds. Eyes-on dwell time with social media ads is much lower, which is largely a result of the scroll velocity. If the ads are on screen, then they are extremely likely to be viewed. But they are frequently not on screen for very long, and so not available to be looked at for a long time.
Finally, there is the dwell time with desktop and mobile display, which is in line with the dwell time norms for social media.
ATTENTION CURVE
Mean averages can obscure as much as they reveal. To get a true picture of the reality of attention we should also look at the distribution of attention. Sure, if someone looks up at a TV ad, they will look at it for around 14 seconds on average – but how is that average constructed?
We have plotted the distribution of average aggregate dwell time with ads in different media. This chart shows the percentage of people who look for one second, two seconds, and so on – in total. They may not be watching from the start of the ad, and they may not be watching consecutive seconds. They may look at the screen, look away, and then look back.
Considered in this way, we see that the distribution of attention varies greatly. Most digital and social media formats have a fat head and a very long tail, suggesting that most people merely glance at ads, but, occasionally, if they find the ads useful or engaging, they can spend a very long time with them.
YouTube data suggests that, even though the ad is playing, people are not always watching. Just as the viewable percentage does not equal the actual viewing rate, so viewable time is not the same as eyes-on dwell time.
And from the TV data, we can see that far more people get to the end of a 30-second TV ad.
ATTENTIVE SECONDS PER 1,000 IMPRESSIONS
All these different views on viewing are interesting, but it would be helpful to have a single number we could use to compare attention between media. This would allow us to ask how many YouTube ads add up to the same amount of attention as a typical TV ad? How many mobile web ads would I have to buy to create the same amount of attention as an ad on Facebook?
We can create this number by combining the viewing percentage (how many people actually look at the ad) with the mean average eyes-on dwell time (the time they actually spend looking at the ad) and multiplying it by a thousand (as media are always traded in thousands). We call this the aggregate ‘attentive seconds per thousand impressions’ or ‘aPM’.
For instance, if you were to buy 1,000, 30-second TV ad impressions, we would predict that 43% or 430 of them would be viewed, but they would be viewed for around 14 seconds each, generating around 6,000 attentive seconds. 920 of your 1,000 YouTube impressions might get looked at, but for only 4.9 second on average, generating 4,500 attentive seconds. And so on.
By following the logic of the attention funnel consistently across media, we have been able to create a common currency of attention that works equally across different media.
This rough-and-ready calculation shows us that the average 30-second TV ad generates the same amount of attention as 1.5 YouTube ads, 4.5 Facebook in-feed ads, or 40 desktop display ads. Suddenly, we can start comparing apples with apples.