Over the last few years in advertising, media and marketing, there has been a civil war going on.
That sounds a bit dramatic, but there has been a pretty constant battle between art and science, creative and big data, digital and traditional, mass and targeted, salience and optimised, and emotion and information.
Generalising a little, there’s basically a ‘pro-digital side’ believing mass advertising is inefficient and full of wastage, and that marketing today is all about the right message/right place/right time, or DM at scale.
And on the other side, an ‘anti-digital side’ believes digital advertising is guilty of spurious numbers and spurious models of marketing, and is the online version of the tactical retail stuff from your letter box that you put straight in the bin.
There hasn’t been much in the middle, especially as it doesn’t tend to make good headlines.
Both sides of the argument have some valid points. However, one side is winning this war. One side has the lion’s share of the press articles, the lion’s share of new media spends, and all of the big technology companies on its side. It has all the modern language too: real-time optimisation, dynamic trading, always-on, programmatic trading, etc.
Many people will say this is a good thing and that it’s the industry modernising itself. ‘Advertising 2.0’. Of course, some level of change is completely necessary to help us exploit the new digital ecosystems, but maybe big data sounds a lot more precise than it should and potentially, for the good of our industry, this marketing civil war is one that we cannot afford for just one side to win.
A Harvard Business Review article from late last year points out public companies are disappearing faster than ever. The current rate is six times faster than forty years ago, and listed companies now have a one-in-three chance of being delisted in the ensuing five years.
Some of this precariousness is explained by three fundamental changes in the business environment. First, speed of technological change; second, a much more interconnected world; and finally the business environment being more diverse than ever.
However, companies could be also inadvertently magnifying the effects of these new forces. That is, companies are actually complicit in creating an environment that makes them more likely to fail than less.
As the business environment gets more and more complex, it also gets less predictable, yet the corporate structures and processes designed for more stable and predictable times are preventing companies from adapting to the complexity of their environment. As the system gets more complex, the negative effects of this get larger.
This way of thinking is based on the work of theoretical physicist called Geoffrey West. It sounds a little odd for a scientist to be writing so insightfully about corporate survival, but it turns out businesses are ‘complex adaptive systems’ of which the sciences have many – cities, bacteria, evolution and economies to name just a few.
When West was asked why cities are so stable and companies so fragile, he answered simply: “It’s easy, cities tolerate crazy people and companies don’t”. In essence, the inability to understand how best to operate with unpredictability (crazy people) is one of the key reasons companies are becoming increasingly less stable.
Marketing (and advertising/media) exists in the aforementioned complex adaptive business ecosystem. One could argue ours is more complex further still, due to the even stronger influence of new technology in our industry and the constant stream of new media channels that our target customers are using and adapting to.
Yet much of our industry is currently obsessed with looking for more and more precision from models. Are we really sure our models are as good as we think? Perhaps we are also guilty of thinking we can measure more accurately than we should.
We are not the only field that has grappled with the philosophy of how much to measure and how precisely. Physics went through this around a hundred years ago when they moved on from the precision of Newtonian physics into the far less precise field of quantum physics. Indeed, its most famous principle is aptly named the Heisenberg uncertainty principle. It boldly states that we can know where an atom is or how fast it is going but not both. Quantum physics’ lack of precision has by no means made the field any less important or scientific; in fact, many would say quite the opposite is true.
Economics has been battling with this area also. Their models have become more and more sophisticated over the last fifty years to the point where half the syllabus of most economic degrees is statistics and only 10 to 15 percent is conceptual thinking and real-world examples.
Despite the models using extremely advanced mathematics, they have regularly failed to predict economic crisis – indeed their models were complicit in exacerbating the GFC due to so much misplaced confidence in what they could measure and predict.
Behavioural economist Gerd Gigerenzer pointed out if you work with complex systems you should use simple models, if you work with simple systems, use complex models. It is clear we work in an extremely complex system, but it’s not so clear that we are always using the right models.
The work of the IPA (Field & Binet), which is not for or against any channel or marketing approach, has been showing for some years that the rise in the usage of new digital tactics has created a bias toward more short-term retail marketing at the expense of longer term brand building marketing.
It’s not that digital channels cannot build brands per se, but because it tends to operate on a philosophy of precise measurement, the metrics and timeframes it tends to use are those that are easiest to measure, which inherently biases them towards the short-term, direct-response style of marketing.
The ‘big data approach’ requires fast, direct feedback loops to work. But mass, traditional, emotion based, brand advertising doesn’t provide very good fast, direct feedback loops. However, just because it isn’t as precise to measure, like Quantum Physics, or provide as fast a feedback, should not devalue its role. However, this seems to be exactly what is happening.
One of the metrics that short-term campaigns most focus on, for good reason, is ROI. However, one of the often forgotten issues with ROI is that it is mostly an efficiency metric. When people say they’ve improved ROI by 30%, it rarely means they’ve sold 30% more product. It could do, but more often it means that they’ve reached customers that are more efficient to reach.
This is the issue P&G fell into when they targeted their fabric cleaner to a ‘better’ audience (households with pets), driving ROI up but sales down. Over-confidence in what they were measuring resulted in them ignoring the less efficient to reach, but still important ‘households without pets’.
The types of data that the digital half of our civil war tend to use: click through rates, sales, ROI etc are all short-term metrics and are much easier to measure than long-term metrics.
This is because in the short term, marketing operates in a fairly stable, not too complex system. There are a certain amount of people in the market at any one time looking for a product, and the marketing is designed to capture as many of them as possible.
Whilst measuring what is easy to measure can be seductive, it isn’t necessarily good for marketers or agencies in the long term. Many clients will end up selling less and/or selling product at a lower margin. If this happens, clients will make less money, marketing budgets will drop and then agency budgets and profits also drop.
Longer-term metrics are much harder to measure because the marketing system becomes more complex over longer timeframes. In most categories, we don’t know with any great precision when the vast majority of potential customers might come into market (one reason click-through rates from banner ads remain so low), but when they do, we want them to think our product is better and worth paying more for. This is a far more complex and unstable system than short-term sales and, accordingly, we should not expect to be able to measure it the same way or with the same precision.
One of the main things that emotion-based, mass-targeted, brand marketing tends to do better than anything is justify a price premium (over time). Consumers, despite what they might say in focus groups, also tend to feel better about the products and services that are better branded (unless the product is terrible, which is rare in 2017) and are happy to pay a premium.
They believe these products taste better, look better and function better. The big issue is that this type of marketing takes a long time to work and is hard to measure, so it can often look like it has done nothing at all. Especially if it is measured over short time frames.
So for all the ‘this versus that’ in the industry, the civil war is really one of measurability. Of what we can measure precisely and what we can’t. And over what time frames we measure. If we think we need to measure everything precisely and quickly, then this will continue to limit the value the wider marketing industry delivers to businesses. It will make us complicit in adding to the growing instability of our clients’ businesses and ultimately, we will become less relevant, less useful and less paid.
This is not to say that we shouldn’t be using all the digital tools available to us, and optimising what can be optimised, but we need to understand that this philosophy must not be applied to all marketing. The business ecosystem is complex and the behaviour of the people we are trying to influence is more complex still.
As David Oglivy once wryly observed: “People don’t think what they feel, don’t say what they think and don’t do what they say.”
To end the industry war, we need to find the middle ground. To get there, we need to remember that statistics is primarily about the estimation and measurement of uncertainty, not certainty. Ultimately, to be as valuable as we can be to our clients, we need to move toward a framework where we can use complex models to measure, analyse and optimise as much as possible in the short term, but also be content with using simple, far less precise and un-optimisable models in the mid and longer term.
At the very least, let’s not measure ourselves out of our jobs. The crazy people in the city need a place to work.