Attention: A Complex Phenomenon Whose Time Is Now

Matthias Rothensee’s background as a psychologist gives him a unique perspective on attention measurement. With the technology and ability to track improving, he believes attention is entering a golden age. He spoke with Realeyes about why attention is a nonlinear process, why companies new to attention should start small and test a campaign, and why deciding who pays for attention measurement isn’t so simple.  

Why is attention so important? 

Well, I'm a psychologist, and psychologists know that attention is a tremendously scarce resource, right? We, as humans, don't have so much attention at hand, and at the same time, it's the most important prerequisite for advertising success.  

So we know that attention is incredibly important. At the same time, there is this so-called "willing-and-able problem." That means that consumers are not oftentimes really willing to report accurately on their attention. Nor are they able to report on their attention because attention is such a fast process.  

It's nearly impossible to really say, I first look there, and then I look there. That's why we need a technical measurement of attention.   

What are some of the reasons why attention is having a big moment?  

Well, I think this has to do with the state of attention measurement today because attention measurement has advanced a lot throughout the last few years.  

You know, eye square started 25 years ago when we started with attention research. We did have helmets on top of people's heads, with cameras attached, looking into the world for them. This was a  cumbersome procedure and it was very expensive and complicated. And, nowadays, measuring your attention using algorithms and webcams has become much easier. And this, of course, gives a big push to the industry and the recognition of the fact that attention is so important as a predecessor of higher-order cognitive functioning is everywhere in the market, and that's why everybody is talking about attention.   

As a psychologist, it's important not to mix up the simplicity of measurement with an oversimplification of the measured object. We will always say that attention is a nonlinear process. That means that you need a lot of experience and benchmarks in order to really be able to interpret the attention data.  

Let me give you an example. For instance, we have media benchmarks where we find out that 2.5 seconds is a very important point in time because it distinguishes the shorter half from the length of ad context, and we see when we correlate the attention density with things like a recognition, for instance, that just below the 2.5 seconds, there's a much stronger relationship between attention and recognition rate than above the 2.5 seconds.

So, we see that there are a lot of nonlinearities, and that's why it's important to have an understanding that attention data is a complex phenomenon. And that's why we are thinking a lot about meaningful attention. What potential patterns are important from a psychological point of view, e.g., which ones which moments to shape higher order, consequences such as cognitive appraisals, emotions, preferences, and all these kinds of things that are important for brands as compared to moments where attention may be just very short or superficial.   

There's obviously attention measurement that looks at creative and measurement that looks at the environment in which they run. Is your point of view that those should be viewed separately or in tandem?  

We look at the phenomenon of attention from both angles. We have clients from the advertising industry who want to find out which is their best creative. But we also have clients from the media industry who want to know which platform is best for providing attention windows sufficient for specific executions. We know that platforms work fundamentally differently. 

Take, for instance, parallax scrolling environments such as newsfeeds. They were totally different than horizontal full-screen video environments. Or take forced exposure versus voluntary exposure. These are things that shape fundamentally different types of attention. You need to be aware of the specifics of the platform; all the platforms provide different canvases.   

We also know from our benchmark analysis that if you have a good creative, our benchmark analysis repeatedly says that between the bottom 25% of the creatives and the top 25%, there's an enormous gap to be filled.  

That's why we always say they play together. The media is providing the canvas, but the ad always needs to play on the canvas in the maximum efficient manner.  

Who should pay for attention? The answer may depend on whether we're talking about creative measurement or media measurement? 

From our experience, the spectrum of clients paying for attention data is very diverse. We have technology providers who want to prove that ad visibility measurement is a proxy for attention, and they pay us to make to produce correlation analyses that prove that there is a correlation between attention and ad visibility. But of course, two other big groups are the advertisers who want to find out which is their best creative among a group of ads that they want to test and also the media companies that pay us to find out which are the specifics of their platform.   

So we would say this whenever there's a company profiting from attention data, they should also be paying for it, just to make sure that it is their data that they have a proprietary take on it that they have their own analysis that can compare between the media platforms in a neutral manner. 

What is the advice you have for a company that is just starting out, or you know, and is unsure how to, you know, take their first step? So you know, if a company were to come to you and say we're hearing a lot about this. And how do we start?   

The first piece of advice would be to start small. Start with a small project, with just a few hypotheses, to see whether these proof in a first pilot project. The second one is what I already mentioned that attention data is a totally different type of data compared to survey data that is traditional for most of the brands. Attention data is not linear. You need some time to get used to analyzing it. And you need to run a series of a few projects before you are able to understand attention data.   

The third piece of advice I would give is to keep in mind what kind of target group you have. Because we know that specific different demographics behave differently about their attention patterns. We have, for instance, younger audiences that have particularly fast processing of visual information. Take TikTok, for instance, as a platform.  

It's tremendously fast as a platform from a behavioral point of view, and that's why we need to make sure that you understand your target group. There are also the preconditions that this has as a consequence for selecting or understanding your attention data; it all comes down to the age groups of the respondents, which are really determining how fast their attention patterns are.

Anything else you wanted to add?   

Well, I would say that there will be more attention data available in the next five years because just because measurement has become so simple, we will see more applications of eye tracking measurement basically in different industries.  

This also means that if you do your homework and have sufficient data warehousing and benchmarking databases that you can learn a lot more about creative best practices when it comes to designing creative in a way that really captures the attention of the consumers, and you use the short moments in time that consumers give you in order to bring about your message in the most effective way.