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Machine learning needs people knowledge

05 Mar 2019  |  Alex Steer 
Machine learning needs people knowledge

We've been spending billions of dollars of marketing spend on ‘good-enough’ proxies for people and audiences, writes Wavemaker's Alex Steer. What we've built is a house of cards.

If you were to write out a list of the data-related topics that marketers talk about, and arrange them in order of sexiness, it’s a fair bet that you would find ‘artificial intelligence’ at one end of that list, and ‘credible standards for audience measurement’ at the other.

This should come as no surprise. After all, we’ve lived through the best part of a decade of incredible advances in cloud computing which have given new life and (let’s face it) big new injections of cash into artificial intelligence and machine learning research.

We’ve seen the rise of tech and media giants whose entire business models are built on predictive analytics, not to mention significant B2B marketing efforts directed at getting brands excited about the possibilities of ‘AI’ (a phrase whose definition ranges from the enormously specific to the borderline mystical).

Nor is this purely hype, the way so many other supposed marketing innovations are. (I’m looking at you, blockchain.) Artificial intelligence does, after all, offer the prospect of being able to make better decisions faster, and to update those decisions as the evidence changes.

For an industry like ours, whose practitioners have gone from being parched for lack of data to drowning in the stuff, in little more than a decade, the promise of an approach that can help make sense and create value from all that data is hugely appealing.

The kinds of questions that AI, machine learning and cloud computing can answer read like the back of a self-help book for marketers. How do I understand my customers better? How do I make better predictions in close to real time? How do I create better customer experiences? How do I see how every marketing touchpoint contributes to penetration, customer acquisition, lifetime value, and so on? These are genuine and legitimate needs, and the application of data science (the well-branded union of statistics and software engineering) can offer meaningfully better answers.

Back in 2012, Harvard Business Review famously declared data science ‘the sexiest job of the 21st century’. It’s a fair bet that they were thinking about the AI end of the spectrum.

Meanwhile, down at the other end of my list, away from the excitement, are those of us diligently working out whether the individuals who see all these new AI-powered ‘brand experiences’ are the right people, or indeed people at all. In a world of elegantly-architected walled gardens, some of us are checking the bricks.

Now, I’m a bit of a nerd, as the whole ‘let’s rank data topics by sexiness’ thing in the first paragraph has probably made clear, so I don’t think being on the boring-but-important end of the spectrum is anything to be ashamed of. But I think diligence can be its own worst enemy when it forgets to win hearts and minds – so now is the time to start shouting, loudly, about the importance of people measurement.

Why now? Because for the last ten years or so, we’ve been building a house of cards – building businesses and deploying billions of dollars of marketing spend on ‘good-enough’ proxies for people and audiences. From clicks, to cookies, to ‘roll-your-own’ customer IDs, to ‘trust-the-platform’ walled-garden reach and frequency estimates, we’ve created a digital economy that rewards audience scale and granularity, without insisting on independent validation of that information.

As if that weren’t bad enough, we’ve taken a similar ‘good-enough’ approach to behavioural metrics such as ‘impressions’, ‘views’ or ‘engagement’, with the result that media planners are now routinely forced to compare apples to oranges, which is bananas.

So what happens when you pay for ‘people data’ without insisting the people are real? After a decade of fake news, disinformation, electoral interference, data breaches, echo chambers and unchecked hate speech, look me in the eye and tell me you don’t know.

But the real answer to the question, ‘why now?’, is that if those of us who control media budgets don’t insist on a higher standard now, we’re at the start of a catastrophe, not the end of one. We are only beginning to explore the ability of artificial intelligence and machine learning to classify, predict, decide and act, based on information about people.

What do you think happens if we allow algorithms to make decisions based on fake people? The consequence for advertisers is a massive escalation of fraud. The consequences for people and for society as a whole are much worse.

Optimising towards unverified engagement metrics such as clicks has already led to a noisy digital ad ecosystem which has prompted rampant ad-blocking. That will look like a fairly small problem compared to the reputational damage to our industry if we are seen as the major source of funding for platforms that enable fake news and disinformation.

There’s a wonderful scene in the film The Big Short where one of the lead characters, a hedge fund manager who’s been betting against dodgy loans, realises that for every dollar invested directly in those loans, there are thousands of dollars invested in exotic financial derivatives built on top of the value of those loans.

Without a commitment to verified people data, many businesses will find themselves in a similar situation, making huge investment decisions based on machine learning algorithms which are trained on dodgy data about the behaviours of people who cannot be verified as real. The results will be like playing Jenga. With a hammer.

There is good news, and it’s simpler than you might think. Most of us think that these problems are inherent to the ‘black box’ nature of AI and machine learning. In fact, the vast majority of them are data problems, not algorithm problems. Marketers can take two actions that, if applied at scale, will drive the cleanup of the ecosystem.

The first is to work directly with responsible businesses in a transparent media supply chain. The second is only to plan, measure and pay for media based on independently verified people data, such as that provided by UKOM. Do not accept machine learning, however sophisticated, in the absence of people knowledge.

Digital audience measurement may never be sexy. But now, more than ever, it’s important – to brands, to individuals, to societies. And that matters more.


Alex Steer is chief product officer, Wavemaker

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NigelJacklin, MD, Think.me.UK on 7 Mar 2019
“I agree with the concerns you raise about the integrity of data and the processes used to analyse and act on that data. Post GDPR I would add a third 'data integrity' concern...purpose (from a consumer/individual viewpoint)...what data do you hold/collect/create for me...and what purposes are you using it for. Notwithstanding the concerns that Cambridge Analytica might (not) have swung any elections/referendums, people out in the real world do seem to be mildly concerned about the ways in which they get odd messages/offers etc from the brands they interact with. We are working on our own Data Integrity project...looking at it from a consumer (i.e. personal) point of view.”
OwenHanks, CEO & Co Founder, Measure Protocol on 6 Mar 2019
“Nicely written Alex- couldn't agree more -we are in an age where bad decisions especially around marketing and public policy are actually hurting society, in fact we like to refer to human generated data as the first and last mile that helps to correct poor AI if its starting down the wrong path. I have one exception to your POV in that I believe that some elements of blockchain technology have the ability to force function the industry away from blackboxes. Thanks for sharing Owen”

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11 Dec 2019 

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