Attention Research has Entered its Fourth Age: The Age of Getting Stuff Done
Back in 2013, when we first started Lumen, we’d begin most meetings with potential clients with some hardcore humiliation. “What if I were to tell you that most people ignore most of your advertising?” I would begin.
We’d show some initial data about quite how few ads actually get noticed, and for quite how short a time. There would be gasps, moans, anger even. ‘Why do we bother?’, clients would say. ‘All that effort – for 1.7 seconds of actual attention!’
This was not just theatrics, but a necessary strategy. Any new science has to establish why it is worth the bother. It has to up-end established opinion, and point out scandalous paradoxes in the existing paradigm.
Early modern scientists did the same thing. They would keep Wunderkammers – cabinets of curiosities stuffed with strange objects and monstrous creatures. The aim was to destabilise people’s confidence that the way they currently saw the world was the only way to see the world.
But the problem with being mischievous and audacious is that you create a problem without necessarily providing a solution. Sure, people don’t look at ads as much as we think they do: now what? Mark Twain once said that everyone talks about the weather, but no-one does anything about it. The same could go for the paradoxes of smart-arsed attention researchers.
Our understanding of attention to advertising has evolved significantly over the last 10 years. But our deepening understanding is not merely down to the accretion of more data. Our mode of understanding has changed. It’s not just that we know more, but what we think is worth knowing has changed.
Like scientists before us, in attention research we have gone through four ‘ages of knowing’. At the time of those early new business meetings, we were in what you might call an age of wonders, where we provided strange new perspectives that challenged our existing assumptions.
A little later, we tried to make our data make sense to the lived experience of agencies and advertisers. We sought regular and predictable patterns – things that we could hold on to and depend on in the face of our previously corrosive skepticism.
Then came a period when, instead of trying to demonstrate consistent patterns in the data, we became more interested in inconsistencies and divergencies. These years were all about having a blind eye and ‘letting the facts speak for themselves’.
Then we arrived at where we are now in attention data – a mature period where what matters, we have found, is not so much what you collect but what you do with it. It is about how one symbolic system helps explain other symbolic systems to get stuff done.
It may or may not be an accident that Lumen has adopted this more pragmatic approach since we started working alongside Ezra Pierce, a scion of the same family as the 19th Century American philosopher CS Pierce, the founding father of pragmatism.
In partnering with Ezra’s company, Avocet, we have been able to link our attention predictions to their ad tech stack. This has allowed us to run thousands of experiments, showing how attention to a particular campaign leads to clicks, or sales, or (thanks to our chums at OnDevice Research) brand recall for that particular campaign. Rather than having one big theory based on one accepted truth, we have lots of little theories based on the interaction of disparate datasets.
This a profound change in what constitutes knowledge about attention to advertising. Up until now, we have been measuring things. Now we are using attention data to understand relationships. We have come to understand that what we are trying to explain is a process. Ads are as much events in time as they are objects in space.
We will never ‘solve’ attention, or advertising. The eye, like the heart, has its reasons that reason knows not. But we can learn more by acting in the world than by merely observing it.
Over the last 10 years, we have developed the tools – both technological and conceptual – to create a deeper understanding of how advertising works, and how it would work better. As an industry, our knowledge will grow, with more data, but also more metaphors and models, used to make sense of ourselves and our industry. But it will be learning through doing that will give us a new mode of understanding.