From Fake News to News Feed: The Dark Side of Targeting
Where to start, if we’re wiping the slate clean and taking an altogether different approach?
Note: this article was published before the Facebook/Cambridge Analytica story came to widespread attention.
From the connected home, to Alexa Video recording your every move, to Google and Facebook’s tracking and filter bubbles, one thing’s clear – technology and surveillance are becoming one and the same thing. And films like the Circle or Minority Report look less science fiction, more science fact by the day.
Not that everyone is happy about all this.
Lately, Facebook has been a particular focus of anxiety. Not least since its role in spreading fake news emerged. Whatever the protestations of its founder, it’s hard to deny the social network has played a part in leading us to a more extreme political landscape, and a more uncertain future across the globe.
While delivering targeted news might at first have seemed the height of logic, the direct (if unexpected) result was the explosion of deliberately false, or misleading content. ‘News’ that played to peoples’ darkest urges, fears and prejudices.
As a piece in the London Review of Books puts it:
We can’t prove just how dangerous these ‘filter bubbles’ are to our societies, but it seems clear that they are having a severe impact on our increasingly fragmented polity. Our conception of ‘we’ is becoming narrower.
So, despite Facebook’s stated mission, to connect and bring the world closer together, through targeting and segmentation, it may be having the exact opposite effect.
From the Feed, to the Banner
If that is the macro effect of targeting on the social feed, what about the banner?
Many have yet to make the connection, but what has become the standard way of buying and selling ads – programmatic technology – works much the same as Google and Facebook’s filter bubbles. In other words, based on consumers’ aggregate browsing history, purchases or assumed interests, it puts people into segments, or ever more targeted smaller groups.
In many cases, we are trying to convince people to seek out products they already have, or already plan to buy anyway. Added to this, our love of segmentation, combined with over-zealous retargeting may have also played a big part in the growth of ad blocking.
But the benefit of data, machine learning and programmatic combined could be so much more. At the risk of stating the obvious, it could be telling us about new, different products, instead of those we’ve just looked at. Things we will want, but don’t even know that yet.
Back to the Future
Why don’t we see more of this type of thinking in ad tech? Quite clearly, scaling campaigns well is a far greater technical challenge than retargeting, or relying on stale, 3rd party data. Even if it could be more effective, not to mention more interesting for the consumer.
Of course, while many ad tech firms proudly announce the data they use is anonymised and safe, at least one piece of research claims that combining just two of those data points lets you personally identify half of them.
Combine our new Facebook filter angst with the upcoming GDPR, and a growing consumer backlash against all types of targeting doesn’t seem all too far-fetched.
What if we were to state the case for using advertising technology in a completely different way. If we said “What has firmly established itself as the canonical approach – based on third party, historical data segments or unrestricted retargeting, has had its day.” In the words of Hearts & Sciences UK CEO Frances Ralston-Good, “The advantage is gone if everyone has the advantage.”
On the one hand, is a slavish over-reliance on retargeting, on the other, a shaky belief in assumed demographic data. But smarter methods in the programmatic space are hiding in plain view.
But where to start, if we’re wiping the slate clean and taking an altogether different approach?
Well, a clue to a better way of building campaigns might not come from what we’ve done so far in digital. Rather counter-intuitively, maybe it comes from the world that preceded it.
Remember the feeling of serendipity that comes with ‘old’ media? If you still listen to radio, then surely you do. But it’s also there when you buy a magazine, and inside find an article on your favourite artist. It’s also there in the new idea or perspective you picked up from that newspaper article you happened to read on page seven. Or even on a visit to the music shop where what’s on the turntable ends up in your personal collection.
What if, instead of a constant process of filtering and narrowing our target audience at great expense, you used technology to build those groups up? What if you could use data to recreate one of the key benefits of old media we now lack – discovery?
Of course, we’re not talking about simply reaching people at random. What we’re proposing is just a different way of approaching and measuring intent.
From the feed to the banner, based on all the evidence so far, it’s time for a fresh approach. One that doesn’t always end in division, however unintended that is.
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