Attention economy is arguably the ad industry buzzword for 2022. Publishers and brands have realized that they need to move beyond traditional viewability metrics to focus on how a user is interacting with content in the moment.

In simple terms, there’s a measurement gap between what is viewable and what is viewed. But how simple is it to leverage attention through programmatic data?

Here, we take a look at four common challenges to extracting programmatic insights.

1. Analyzing metrics that matter

Let’s imagine a line graph, with a steadily increasing curve representing the volume of online content over time, while a flat line represents the time a user has to consume it. The gap between those lines describes the attention deficit.

A few years ago, viewability metrics (e.g. video completion rate, dwell time) became the accepted way of measuring the performance of online content. However, as Playground xyz’s CEO Rob Hall describes, as the attention deficit widens, standard viewability metrics are fast becoming “preconditions for [online content]. But really, they’re more hygiene factors.”

And while hygiene factors are important, publishers are now looking to dig deeper into engagement by using attention metrics, such as attention time or active time in view.

Attention metrics can be measured in different ways. These include as scroll rate, scroll depth, touch rate and cursor position. All of these can show how the user is interacting with the page when the content is in view. Crucially, they also reveal whether they are interacting with the content itself.

Taking this one step further, publishers are also beginning to use advanced image-streaming technology that detects when a user is zooming in on content, or viewing it in full-screen mode.

Just like viewability data, attention data can have a significant steer on programmatic advertising. By making the leap to include attention metrics in the mix, publishers can serve more relevant content programmatically, optimizing the experience for users.

2. Getting close enough to real-time data

According to Lumen Research, the key to analyzing engagement is to create a predictive model of attention to content that can be used to inform programmatic bidding in real time. ​​By collecting huge volumes of impression data on visual attention, they have created a powerful model that can project how much attention an ad or a piece of content is likely to receive.

The reality is that many publishers are still reliant on data platforms that refresh data sources once a day at best. But by switching to more advanced tools, they can get access to much fresher data than ever before. For instance, they might use software that refreshes the data every few hours. Once they have real-time attention data, they can then look to build more advanced data models based on predictive analytics.

Armed with this vast (and ever-growing) pool of fresh data, publishers can extract more relevant insights for more effective programmatic advertising based on user attention. However, analyzing data at scale can be laborious and error prone without the right tools in place.

3. Extracting programmatic insights at scale

Some larger publishers handle petabytes of impression data each day. That’s a heck of a lot of data from which to extract useful insights. Until now, publishers have faced the daunting task of building out their own strategic data asset. But this can take months, if not quarters, before their business teams can get to the data they need.

Thankfully, there are heavy-duty data unification tools on the market that can handle growing volumes of data with ease, providing business teams with the bespoke reports they need, while freeing up engineering teams for other business-critical projects.

But, of course, it’s not just about scale. It’s also about versatility.

4. Blending different reports for a holistic view

Having access to large data sets is no use if they remain in silos. For instance, while drilling down into programmatic data is important, so too is having a holistic, blended view of all ad spend. Likewise, inventory yield reports should provide up-to-date reporting across all digital revenue channels including display, mobile, CTV, social, DOOH, etc.

However, for many publishers, generating these high-level reports is still a challenge. It takes too much time and effort to manually aggregate the disparate datasets. And then it takes even more time to build custom reports that are accurate and speedy enough for business teams to act upon.

Get the tech to do the heavy lifting

The speed at which today’s media teams are expected to build, measure and optimize programmatic campaigns to remain competitive is daunting. But it is not impossible.

Whether choosing the right metrics, accessing the data in real time, or bringing together disparate data sets at scale, implementing automation tools to do the heavy lifting will give publishers the confidence – and the time – to focus on user attention. Only then will they start to drive more meaningful ROI for their brand advertisers.

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About the author

Navid Nassiri joined Switchboard as Head of Marketing in 2021. Switchboard’s data engineering automation platform aggregates disparate data at scale, reliably and in real-time, to make better business decisions. In his role at Switchboard, Navid is focused on driving growth and brand awareness through innovative marketing strategies. Navid is a seasoned entrepreneur and executive, including leadership roles at PwC and NBCUniversal.