machine gun etiquette
Then creative people mess with it and create something new and unexpected.
Artists never invented oil paint, or the movie camera but they saw the opportunity the technology gave for creativity.
Bill Drummond once made this point (I sometimes see it attributed to Lee Clow, either way it’s a useful insight).
Historically, the advertising business has erred on the side of caution in its adoption of new technology. The first ever TV ad, a whopping $4 dollar production for Bulova Watches, ran in 1941 but it was almost 20 further years before the industry embraced television as a platform.
But things have speeded up in recent years.
In fact it’s been a head-first dive into digital and social media, then virtual and augmented reality, black boxes of every flavour and now artificial intelligences and machine learning.
As a bonus, with each of these new developments in technology comes the processing of huge amounts of new consumer data – we have more than any other generation of communicators could have even imagined - so it should naturally follow, fully stacked, we can now connect with consumers better than any other generation of marketers.
Yet it can often feel like more data actually means less. We are even less connected.
Because, in spite of the bluster and gusto, advertising hasn’t had a good time figuring out how to make tech, data and creativity work together, and therefore doesn’t appear to have a clear articulation of its own future.
Indeed, in most of the industry the conversation is still stuck with a false dilemma.
As if the data-driven and creative are incompatible.
It need not be this way, and we need to resolve this dichotomy fairly urgently.
Data is everywhere, and every day there is more and more data.
For many, simply being exposed to the idea of data at this scale is enough to just switch off and become misty-eyed for simpler times, whereas for others the accumulation of data has become something of an end in itself, as if simply possession of the data constitutes a silver bullet.
But the daily reality, for the most part, is more mundane. Agencies may tend to limit their view of data as either, oft times inconvenient, input to inform or rationalise strategic choices, or as, equally inconvenient, output in the form of metrics and measurement.
What’s even worse is that during this process they tend to obsess over the wrong data, giving disproportionate focus to small and insignificant differences, get distracted by noise rather than finding the signal, get dazzled by vanity metrics and miss the big important things that really matter in guiding strategy.
From that standpoint, any lofty ambitions to assimilate data as a part of the creative process seem a long way off.
Direct marketers and digital marketers will, of course, disagree. They will crow of how they can already effortlessly track and retarget elusive consumers, whilst micro-segmenting audiences and optimising each campaign to within an inch its life.
But is that all there is? Efficiency?
All of the time each of us spends on the internet, and on our smartphones, all the websites we visit, the apps and services we use, everything we buy or think about buying and the people we talk to generates an incredible amount of data on our behaviour and our preferences that could be used by brands to better connect. However, just this observation is banal.
Yes, the domination of programmatic delivery, automation and further advertising technology is inevitable. Very soon all media will be bought and distributed in this way. It’s a wonderful thing, but the tech, on its own, is not good enough.
We desperately need our best creative minds to grab the opportunity that data and technology provide for creativity. But we need a bridge to connect the two.
To that end, I propose that the role for strategic planning in agencies will have to change in this new data-rich environment.
While no planners should be strangers to data analysis - some may even have a basic grasp of statistics and recognize a NBD curve when they see it - but the key imperative for strategic thinking in agencies will be to provide the human understanding that connects the data and technology to the creative product.
As a starting point it’s worth remembering that any data is really only as useful as the questions asked of it. Data has no intrinsic value.
Understanding what consumers actually do rather than what they say they do is critical. We’ve learned from the recent advances in behavioural economics and consumer psychology that consumers have, pretty much, no access to the unconscious mental processes that drive most of their decision-making.
However, this doesn’t prevent people providing plausible-sounding rationalisations for their behavior, when asked. Even the process of asking people what they think exerts its own unconscious influence. To the extent that much of the survey data that has traditionally fueled marketing decision making is, at worst, a total fiction or at best only an artefact of the research process, itself.
The consumer psychologist Philip Graves famously channeled Edgar Allen Poe by remarking ‘Trust nothing consumers say, about half of what we see them do, and nearly everything the sales data tells us they have done’.
Graves is adamant that real sales data and covert behavioural observation should always be the start point of any research.
The use of the words ‘covert observation’ can quickly divide a room. However when the focus of any research is overt – i.e. the participants are aware of what’s being investigated – then, while it feels like it’s more transparent or ‘ethical’ it is mostly useless. Knowing one’s behavior is being observed is intrinsically biasing. When people are aware that they are being observed they become more self-conscious and their behaviour changes.
This is where the new developments in data technology might become interesting.
Artificial Intelligence and Machine Learning are two buzz phrases being used right now - often interchangeably - but they are not quite the same thing. For our purposes as advertisers, it’s enough to know that one is effectively an application of the other.
Machine Learning, then, is a particular application of one AI based around the idea that - given access to enough data - machines can learn for themselves. Put simply, a machine learning AI is essentially a system fueled by algorithms, and as these algorithms are exposed to new data they teach themselves and grow.
Basic Machine Learning applications can read and interpret text (making inferences about the tone of the text it is reading), all programmatic ad trading is applied AI, chuck in other applications like self-driving cars, Siri and rudimentary speech recognition and a lot of this kind of applied AI is all around us, now. But these examples are what the boffins would label ‘narrow’ AI.
Narrow or not, these developments are reasonably impressive from the technology standpoint and present a platform for creative people to do something new and unexpected.
In simple terms, the ability to identify an individual consumer, rather than trying to make sense of multiple cookies and multiple devices that may be associated with an individual, is not just about micro targeting and extreme personalization. This ‘narrow’ view (to borrow the technical jargon of our AI engineer friends) is just more of the Peppers and Rogers circular logic.
AIs and PII (Personally Identifiable Information) are going to be far more useful in accurately sizing markets, uncovering the real sales and behavioural data and the necessary covert behavioural observation that allows us to group together bigger sets of consumers through shared insights.
Advertisers should be interested in observing these network effects. As anyone with even a basic understanding of simple network theory will tell you, the value of a network increases as it grows bigger. A simple applied description of machine learning with personal information is described nicely for the lay person (or advertising practitioner) Kevin Kelly’s 2017 book ‘The Inevitable’ and in the chapter on ‘Cognifying’ (one of the 12 tech forces that he predicts will be the most important in the next couple of decades).
‘The more people who use an AI, the smarter it gets. The smarter it gets, the more people who use it, the more people who use it, the smarter it gets. And so on’
Kelly tells of a moment in 2002 when this became clear to him. While making conversation with assorted engineers at a private party within Google HQ he came to the realisation that we had been looking at our Silicon Valley overlords ultimate goals the wrong way round. Google were not interested in the application of AIs to make their core products like search better, it was OUR usage of search that was feeding Google’s AIs. Google was fundamentally an AI company.
Our usage feeds the AI. The more we use it the smarter it gets, and so on.
Today, smartphone data is obviously they key - about 90% of all these devices are uniquely identifiable with an individual – we can know almost the exact composition of a total audience, as well as where and when media is used. It’s also worth noting that the full-tilt expansion of personal media means that the next decade promises to bring new technologies with capabilities far beyond the abilities of our smartphones.
The mainstreaming of machine learning capabilities, will provide agencies with better building blocks for smarter campaigns, and constitutes something of a leap in marketing intelligence, but as we’ve noted before, simply turbo-boosting targeting and delivery of ads is not where the real potential for AI applications in communications lies. Even adding the benefit of population level behavioural data and insights we are still working with ‘narrow’ AIs.
Things start to get much more interesting when we can map human psychology onto the data.
We live in a modern world of complex social networks. We interact with hundreds of people each day, in both physical and virtual environments. Success in this environment means being best adapted to interacting with, and working with other people.
And getting what you want from others.
Each of us has things that annoy us and things that make us happy. We have become very skilled good at remembering other people’s preferences and they, ours.
But we are limited by our cognitive capacity. It takes a huge amount of cognitive effort to remember other people’s preferences. But the pay-offs are there when we get it right.
This skill evolved long ago in our ancestral past, one of many adaptations that shaped our minds into the way they are because these adaptations enabled our stone-age ancestors to succeed with their (and our) principal concerns, namely survival, reproducing, forming mutually beneficial alliances and looking after families.
When the anthropologist Robin Dunbar was trying to solve the problem of why primates (including humans) and other social species devote so much time and effort to this kind of ‘grooming’ behavior, he happened upon his eponymous number.
Dunbar’s number (around 150) described a theoretical limit to the number of people with whom any individual is able to sustain a stable or meaningful social relationship.
150 is a best case number and even in the age of digital social networks, the number of friends with whom you keep in touch, and groom, is likely to be significantly less than Dunbar’s number.
But for brands, companies and institutions – for whom the Holy Grail is to sustain stable relationships, keep in touch with and groom literally millions of consumers - the really big opportunities that the harnessing the tsunami of personally identifiable data and the power machine learning and other AI applications offer lie in these areas.
The ability to manage relationships with and remember the (often implicit and unarticulated) preferences, of millions of individuals with the same intimacy as these tight-knit groups of humans manage their own relationships, is the bridge that finally connects the technology, the data and the creativity.
To a degree, I’m carried by Kelly’s optimism when he proposes, ‘There is almost nothing we can think of that cannot be made new, different, or interesting by infusing it with some extra IQ. In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI.’
Take market research and add AI.
Take consumer psychology and add AI.
Take creativity and add AI.
So, in theory, machine learning and AIs do offer us much more than just the better mousetraps of targeting and delivery. The big opportunity lies in how these technologies will aid understanding what people value, why they behave the way they do, and how people are thinking (rather than just what). This could bring new, previously hidden, perspectives to inform both the construction of creative interventions and understanding exactly where, when and how these interventions will have the most power.
The more sensible proponents for the digital economy have always hoped for this, but if it were that simple then perhaps a lot more would have already been achieved by earlier iterations of the internet and this indicates that there are significant hurdles still to be overcome.
For a start, the impersonality of digital communication almost certainly affects our interactions with others in comparison to face-to-face communications. Spend five minutes on Twitter or in the comments section of any of the ad industry trade websites and this should be self-evident.
These challenges also have their roots deep in human nature and our evolutionary past.
In ‘The Evolution of Language’ Dunbar also notes that ‘Whenever person-to-person interaction is a necessary feature of the process (as in the striking of deals), the old and trusted cognitive mindsets will come into play. Suspicion of the unknown and the fear of being duped by untrustworthy strangers will continue to dictate our decisions…the lack of personalized contacts means that individuals lack that sense of personal commitment that makes the world of small groups go round’
Anyone who uses, the slightly more transparent and therefore marginally more civilized, LinkedIn will be familiar with the words of the data-scientist W. Edwards Deming, which seem to pop up in my own feed at least twice a week.
‘Without data you are just another person with an opinion’.
In our business there are no shortage of opinions. Unfortunately, many are spectacularly uninformed opinions.
Deming, quite rightly, demands the objective facts. And we have more facts and data at our disposal than at any time in human history.
However to complete the picture, and to take the opportunity that data and technology give for creativity, I propose an addendum to Deming’s thesis.
Without data you are just another person with an opinion? Correct.
But, without a coherent model of human behavior, you are just another person (or AI) with data.
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This is the original and longer version of an op-ed that appeared in AdNews in August.
