As the CTV market matures, we\u2019re going to see a lot of questions being asked about attention: how we define it, how we measure it, and, of course, how we leverage it for profit.<\/span><\/p>\n In this blog post, we talk about the science of attention, and how to make it work for you.<\/span><\/p>\n Case Study:<\/i><\/b> how OKO Digital was able to boost Loop TV\u2019s revenues by 500x \u2014 <\/span><\/i>on the first day.<\/span><\/i><\/a><\/p>\n Attention is a metric getting a lot of discussion these days, but it\u2019s also nothing new.\u00a0<\/span><\/p>\n As long as there has been advertising, audience attention was the goal, whether it was a guy yelling \u201chear ye hear ye\u201d in the town square, roadside signs for Burma-Shave, or frogs with a surprising amount of interest in Budweiser. Getting eyes and ears on content has always been the goal, and figuring out how to measure that has always been a hitch.<\/span><\/p>\n The <\/span>departure of third-party cookies<\/span><\/a> this year, however, has brought a new intensity to the quest to track audience attention in as direct a way as possible. Since tracking your audience as they travel around the Web has gotten more difficult (and problematic) than it was previously, finding a way to reliably measure attention has become the Holy Grail.\u00a0<\/span><\/p>\n Attention\u2019s value as a trackable metric is fairly obvious: if a publisher could promise that a particular bit of content commanded unusually-high levels of audience attention, where viewers were looking closely at the ads, advertisers would be willing to pay more for that than they would for slots where the audience is disengaged or disinterested.\u00a0<\/span><\/p>\n For example: the same audience, viewing the same episode of \u201cStranger Things,\u201d can have a radically-different attention level if they\u2019re watching at 6 pm versus a viewing session of 2 am. That sort of consideration has long factored into the pricing structure of linear TV, where \u201cprime time\u201d charges differently than other programming, but it can be a little harder to nail down when viewers themselves get to decide what they want to watch and when.<\/span><\/p>\n But, in the data-rich environment of CTV, which also affords more-refined targeting options than linear TV, cracking the attention code will be another way to help advertisers and publishers make informed decisions.<\/span><\/p>\n Before we can measure attention, however, we have to have an agreed-upon definition of what it actually is. Because CTV is a relatively-new market sector, compared to the more-seasoned areas of online advertising, the way attention is defined, both conceptually and as a KPI is a little bit \u201csquishy\u201d at the moment. But, to break it down:\u00a0<\/span><\/p>\n \u201cVIEWABILITY\u201d\u00a0<\/b><\/p>\n With web ads, \u201cviewability\u201d is defined by percentages.\u00a0<\/span><\/i><\/i><\/p>\n For how long a time? That\u2019s one of the factors that\u2019s currently varying from platform to platform. One TVision study on attention with LG calculated attention by whether or not the audience paid attention to an ad for two seconds or more. Some people say \u201cattention\u201d when they mean \u201cviewability,\u201d others might use it when they mean \u201cengagement.\u201d The IAB\u2019s definition of attention is, \u201ca consumer looking at or listening to an ad at the time they were exposed to it.\u201d<\/span><\/p>\n The industry, in short, has yet to settle on a singular definition of \u201cattention,\u201d but given the increased spend on CTV advertising in recent years, that standardization is definitely coming soon. But for the purposes of this article, let\u2019s consider it a measure of whether someone is actually looking at your ads, for however long that may be.<\/span><\/p>\n Modern technologies have started to unlock possibilities for measuring attention in a more-direct way than we\u2019ve ever thought possible before, but new technologies do bring with them new concerns.<\/span><\/p>\n In the TVision study we mentioned up-top, the way they measured whether a viewer\u2019s eyes actually locked in on a commercial was by setting a camera on top of the TV and pointing it at the audience. Advanced versions of this technology use AI-enabled algorithms to track viewers\u2019 eyes, to see when they\u2019re watching and when they aren\u2019t.\u00a0<\/span><\/p>\n That kind of approach works perfectly when you\u2019re performing an experiment under controlled conditions, but in the real world, this approach has experienced some pushback.<\/span><\/p>\n In 2022, the \u201cMoviePass\u201d program that promised unlimited movie-theater tickets tried to relaunch as a paid digital service, promising an ad experience that would <\/span>pause the commercials<\/span><\/a> if the user attempted to look away. It didn\u2019t go over well in the press, which generally considered it \u201cdystopian.\u201d And while none of the major streaming players have followed suit, it does seem likely that someone else will try again in the future.<\/span><\/p>\n Given that the reason third-party tracking cookies have gone away is due to privacy concerns from the public, pivoting to a camera in every living room that tracks people\u2019s eye movements will probably remain a tough sell to the viewing public for a while. However, one could make the case that since an AI algorithm isn\u2019t a \u201cperson,\u201d you aren\u2019t really being \u201cwatched\u201d in the traditional sense \u2014 and paired with a compelling enough incentive (reduced rates, fewer commercial breaks, premium content) it\u2019s possible that this technology could work in the future.<\/span><\/p>\n Another avenue to measure attention is by tracking audience engagement. Content is a driver of audience engagement, and some shows are appointment television with very invested audiences. \u201c<\/span>The Bachelor<\/span><\/a>,\u201d for example, saw four-figure spikes in engagement percentages for brands that advertised during the 2024 season premiere. Viewers who watched that premiere were 54% more likely to engage with advertising brands than the primetime broadcast average, based on stats like brand searches and website visits. If an audience is paying close attention to a show, it seems they\u2019re also more-likely to watch the commercials. Transforming that engagement-to-attention ratio into a predictable formula that brands can make decisions with hasn\u2019t materialized yet, but in our current environment, the data is definitely available.<\/span><\/p>\n Ultimately, however, the best way forward might be to combine some of these new methods with some of the methods of the past.<\/span><\/p>\n Back in the first Golden Age of Television, so-called \u201c<\/span>Nielsen Families<\/span><\/a>\u201d would agree to have their viewing habits recorded, forming a large enough sample population that Nielsen could extrapolate from there to showcase who in America was watching what. When you talk about, for example, the \u201cratings\u201d of the <\/span>Seinfeld<\/span><\/i> finale or the latest Super Bowl, the ratings in question are generally from Nielsen, even today. On the streaming side, however, viewership numbers are usually self-reported by the apps themselves, if they\u2019re reported at all.<\/span><\/p>\n In 2017, Nielsen began expanding their practice to include some streaming content on services like <\/span>Netflix, YouTube, and Disney+<\/span><\/a>. Though they\u2019ve had decades to perfect this approach, it\u2019s still an approach somewhat limited by the accuracy of the model, and CTV introduces a significantly-higher number of variables to track.<\/span><\/p>\nIs \u201cAttention\u201d The Big New Thing?<\/strong><\/h2>\n
What Makes Attention So Valuable?<\/strong><\/h2>\n
Defining \u201cAttention\u201d<\/strong><\/h2>\n
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![\"Measuring](\"https:\/\/stg-coolmedia-stage.kinsta.cloud\/wp-content\/uploads\/2024\/04\/Measuring-Attention-The-Future-of-TV-Advertising_additional_asset.png\")
<\/p>\nHow To Measure Attention<\/strong><\/h2>\n
The New With The Old<\/strong><\/h2>\n