Predicting the future: The sweet spot between idiot and expert

Simon Bird argues that instead of listening to expert predictions about the future, adland might be better placed to stick with those who never profess to be experts in one particular area.

We’ve all read various articles and heard chatter about the movement of agency services going ‘in house’, moving into a consulting firm, or becoming automated.

Some of these changes are obviously justified, but there’s some evidence that companies that move agency functions out of agencies may well end up producing worse work rather than better work.

It suggests there’s a sweet spot somewhere between idiot and global expert, where open mindedness and curiosity is maximised.

You may be familiar with the Gartner Hype Cycle, if only for the humorously cynical and philosophical names of the various stages, such as “the peak of inflated expectations”, “the trough of disillusionment”, and “the slope of enlightenment”, names that sound more like chapters in a new Tony Robbins book than those of a technology classification chart.

The Hype Cycle is now over twenty years old. Given this time frame largely covers the entire rise of digital marketing, taking a little wander down innovation history lane is rather informative. It turns out not so many technologies have progressed smoothly along the adoption journey.

Aside from some now outdated language, the first Hype Cycle from 1995 actually looks like a pretty good prediction of tech adoption. Emergent computation is, apparently, a forefather to neural network-based machine learning, so whilst the terminology might seem unfamiliar, it’s still very relevant in 2018 in areas such as machine learning.

But looking a little more closely at the many Hype Cycles since 1995, it’s clear over the years that there have been more than a few technologies that turned out to be far more hype than help and ended up slipping right off the cycle – truth verification (2004), 3D TV (2010), social TV (from 2011), volumetric and holographic displays (2012), to name but a few.

As Michael Mullany says in his article about this subject from December 2016, the tech industry (like most industries) is not very good at making predictions and also not good at looking backwards after the fact to see what it got right and what it didn’t.

This is no slight against the tech industry, almost all industries fall into this type of thinking. Humans are rather lazy thinkers, so we tend to remember the easy to recall successful predictions and conveniently, mostly subconsciously, we forget the hard to recall failures. This, of course, creates a terribly inaccurate feedback loop.

Mullany also goes on to mention a couple of other reasons why there are so few technologies that flow nicely through the technology adoption cycle; many technologies are simply flashes in the innovation pan (although truth verification sounds like it may have caught on if it was launched now).

Obviously the world of marketing and advertising is becoming increasingly more technology-based. If all the experts at Gartner keep making considerable errors in their predictions about technology and its uptake, what hope does our industry have?

Well, it turns out that there is good reason to be hopeful. Sometimes knowing less than the leading experts can actually be a good thing, as long as it’s not too much less.

This sweet spot of knowledge creates just the right amount of doubt in a point of view, which in turn helps create more open-mindedness to possible future outcomes and thereby results in better predictions than the aforementioned experts.

It’s based on the Dunning-Kruger Effect, which is more commonly used to explain why people with limited talent manage to be so overconfident i.e. people at karaoke who think they can sing like rock stars but sound tone deaf. It’s essentially an ignorance bias, whereby people with limited knowledge don’t know enough to know what they don’t know. It’s commonly represented by the below graph.

Apart from the initial burst of confidence from the ignorant, which notably is at a level even an expert fails to ever attain, the chart makes intuitive sense, as we learn more we become less confident until we approach expert level and become increasingly confident (know that we know).

However, whilst confidence amongst experts is clearly more desirable than confidence amongst idiots, it still remains problematic. Berkeley psychologist Philip Tetlock studied this area extensively in the early 2000s.

He recruited 284 experts in politics and economics, and had them answer various questions along the lines of ‘Will Canada break up?’ or ‘Will the U.S go to war in the Persian Gulf?’ etc. In all he collected over 82,000 expert predictions.

To quote Tetlock himself: “We reach the point of diminishing marginal predictive returns for knowledge disconcertingly quickly, in this age of hyperspecialisation there is no reason for supposing that contributors to top academic journals – distinguished political scientists, area study specialists, economists and so on – are any better than journalists or attentive readers of respected publications, such as the New York Times, in ‘reading’ emerging situations”.

He also concluded that in many situations, the more famous or expert the person doing the predicting was, the less accurate the prediction.

The key issue being the expert’s knowledge and expertise combined with their high level of confidence prevents them from entertaining less likely but still highly possible outcomes.

They drink their own Kool Aid – whereas the merely knowledgeable, who are less confident in themselves and their predictions, are far more likely to assess alternative outcomes, thus making their view points and predictions more accurate.

The agency sweet spot

This position of knowing a reasonable amount is the natural place for agencies. Our ‘expertise’ is more an accumulation of many areas, none of which we are specifically experts in.

We know a fair bit about human behaviour, a fair bit about marketing, a fair bit about technology, a fair bit about media channels and popular culture and a fair bit about our clients’ businesses.

We know less about each individual area than a single discipline expert or global authority, but our ‘expertise’ is in blending our working knowledge of each area together.

In today’s world, this isn’t something easily copied by a tech firm, a consulting firm or by clients themselves. They’re all deep experts in their own fields, making them less open minded toward non-typical outcomes or new ideas and innovations.

However, this is perhaps a position we have not always employed as well as we could. Whilst Gartner has clearly over-hyped more than a few technologies, the world of marketing and advertising has also been responsible for a number of unnecessary websites, (my personal favourite is still bidforsurgery.com) apps and VR/AR games.

Our most current industry obsession seems to be AI, which incidentally Gartner currently has at the top of the peak of inflated expectation. Many of the headlines talk about AI taking jobs, killing brands, taking over marketing, making ads and getting smarter than us and becoming existentially dangerous.

This isn’t to suggest that we should stop using AI or employing the latest technologies, just that to exploit the ‘reasonably knowledgeable’ position, we must be balanced in our assessment of new innovations.

The objectivity that comes from sitting in the middle of the idiots and the experts is a place that is becoming ever more valuable as the world gets filled with more brilliant, but close minded, experts. If we exploit it well, it should help us compete against consulting firms, client ‘in-housing’ and some areas of automation.

And at the very least, it should help us avoid sounding ludicrous, something that is going to be rather tricky when we start talking about smart dust, the most recent entrant in the latest Gartner Hype Cycle.

Simon Bird is group head of strategy at PHD New Zealand.


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