Alex Steer

101 words | ~1 min

It's Superbowl day today, so if you work in advertising, expect your social feeds to be full of analysis of which brands 'won' based on online buzz around their ads.

All this is good and interesting, and gets what we do in the spotlight. But don't mistake it for effectiveness.

TV brand advertising works hard - but over weeks, months and years, not minutes. Being famous for fifteen minutes is a good start, but just that - a start, not the endgame.

Social buzz is to effectiveness what journalism is, famously, to history - lively, interesting, but just the first draft.

479 words | ~2 min

The release of iOS 9, which enables ad-blocking apps on iPhones, has caused no end of controversy.

One the one hand, advertising is the sponsor of lots of things on the internet that are free and wouldn't be otherwise. On the other people find online ads sufficiently annoying that they want to block them - to an extend that far exceeds ad avoidance in any other medium.

And annoyingly, both sides are right, which suggests something is broken in the online advertising market.

In fact, it's very clear what this is. Online advertising still suffers from an enormous measurement problem that has led to the proliferation of bad ads.

A vast amount of online ads are still measured on a 'last-click' basis. They are deemed effective only if they are the last thing that drags someone over the threshold to your website, app or online store.

This is, obviously, a horribly flawed way of thinking about how advertising works. To take an offline analogy, this is like saying that if someone sees a big TV ad for a new brand of baked beans; then a great series of press ads; then sponsorship at their favourite sports game; then a PR story about how the beans are sustainably farmed; then goes to the supermarket where there are shelf wobblers pointing him to the brand... then the shelf wobblers should take all the credit if he buys a tin.

This is a problem that has been solved many times over - by marketing mix modelling, and more recently by more detailed digital attribution methods that can see entire customer journeys to purchase, and calculate how important each advertising exposure along that journey was to the final outcome. We've run dozens of mix modelling and attribution studies for clients, and in almost every case, we've found two things:

1. Clicks barely matter. Seeing ads is what makes people more likely to purchase.
2. All the advertising people see matters - not just what they see last.

This is not surprising. Yet we're still buying adverts based on a cost-per-click basis, and attributing sales based on clickthrough, because it's easier to keep doing that than to change how we measure and report. Since clicking is an unnatural behaviour, we flood the web with ads in order to get a few clicks, and we reward shrill, intrusive, noisy advertising that leads to clicking, a behaviour that (with the exception of paid search) has almost nothing to do with how advertising works.

No wonder people want to switch off the advertising hose. By measuring properly, understanding which exposures to advertising are effective are worth paying for, we might avoid crashing our own market.

233 words | ~1 min

Good to see M&C Saatchi's mad PR equation is back in the adland headlines:

After long hours looking at data from Nielsen and Unilever, the Saatchi Institute was able to map the correlation between the ability of a brand to maximise differentiation and minimise deviation. The equation Saatchi proclaimed as "the answer" back in June is the formula for the curve created when the Unilever data was plotted on a graph.

Well, that's more than we got a few months back when it was first shown (with no explanation). It's unnecessarily obscure for a curve equation, though. It looks like a power law equation to me. On the plus side, it's doing a great job of winding people up, a classic Saatchi move.

If I had to guess, I'd say it maybe describes the factors that condition the extent of a brand's ability to steal market share (which normally operates on a power law basis), presumably by balancing differentiation with the minimisation of loss of sales due to short-term factors, like competitor price-cutting. If so, that's a perfectly good basis on which to think about your advertising.

As and when some detail about it actually gets published, I'll be all over it and looking to test it on data from other brands.

504 words | ~3 min

This is a quick follow-up to an equally quick Twitter conversation with Faris Yakob about his interesting piece in the Guardian on the currency of online impressions. The piece's main argument is that the assumption that the impression is the currency of attention is faulty:

In order to buy and sell something, we needed a currency. We settled on the impression: one person being exposed to something once. Attention is a complex and analogue aspect of consciousness – its most directed form – which makes it a small part of the most complex system in the known universe. The complex, fundamentally analogue, nature of attention, which has many different facets, is converted into the simple, inherently binary, impression.

The piece is both mostly fair and a bit unfair. There are better ways of measuring attention; they are granular and specific to specific ad exposures; but they're not yet a properly tradable currency for online media.

So what are they? And what should the currency for attention be?

They don't really have a name yet, but they do exist, we're working with them, and my shorthand for them would be Lift Points.

Here's how it works. Using log-level ad-server or site analytics data (the same thing that gives us impressions), it's possible to identify the number, order and nature of exposures an individual has had to online advertising during a time period. This is particularly true if you can deduplicate across devices, tie cookies/device IDs back to real people, and so on. So far, so obvious.

Using sufficiently large behavioural tracking + attitudinal research panels (e.g. Millward Brown's Ignite network), it's possible to tie these granular impressions to well-controlled brand tracking surveys.

Briefly, this means you can effectively regress the test-vs-control uplift in brand awareness/equity/whatever to specific patterns of exposure - creative, site, placement, order, recency, frequency, and so on. By treating this like an attribution model you can assign percentage points of brand uplift to specific factors in the advertising mix. This can be done at a very large scale, and very quickly - and you can use it to isolate the contribution of any factor and give its typical contribution to uplift. And those are Lift Points.

The most obvious - and most easily tradable - would be Awareness Lift Points - the average incremental points of brand awareness delivered by an ad / placement / etc per single exposure. Because ads that are unseen have no impact on awareness, like any good attribution it controls for viewability automatically.

Is it immediately tradable the way impressions are? No, but if used it would quickly build up a tradable market value the way that media owner ratecards or viewability scores do - based on the typical delivery of uplift per exposure. It's also challenging to the economics of the research industry as it means a vast number of very small and fast-turnaround post-exposure test-and-control surveys, but some providers are already moving in this direction.

220 words | ~1 min

Sometimes it's good to be reminded of what really good brand planning does: takes the latent potential of a brand and makes it into an asset, by connecting something obvious about the brand to something important in life.

Lexus have made fairly bland ads for years. They always tried to be about emotion but got clogged with distracting functional claims about the fuel pipelines, the energy efficiency, the power/weight ratio, or whatever. They ended up being forgettable ads about engines.

Their new work seems to have owned up to the fact that, as a business, they clearly get off on technical ingenuity rather than poetry. It's enabled a slight but powerful shift - from ads about engines, to ads about engineering.

It's a feat of subtle brand planning that's given them something that they want to talk about, that is worth listening to.

Rather than another ad about fuel injection, they've built a working hoverboard, and used that as the focus of a film about trying, failing, learning and succeeding. The internet is, rightly, passing it round like crazy, and it's really worth watching. (The craft of the film is also great.)

Well done to everyone involved. I hope it sells you some cars.

508 words | ~3 min

Reading this piece by Simon Law, on the creative challenge of programmatic and adaptive media, this thought stuck out:

We’re all trained to find the best answer – both at agencies and in marketing departments. But the best answer is inherently singular. It doesn't include a set of 'maybes' – so we need to change our attitudes, too.

I worked with and for Simon for three and a half years at Fabric, and it's a principle we put in practice as an agency, testing and measuring creative ideas and scaling up the ones that worked.

But 'designing for "maybe"' strikes me now as a good foundational principle of building analytics functions, as well as creative departments. There are only two components of an analytics team: the people, and the technology. We need to design for 'maybe' when assembling both.

People first. Everybody recognises the need for expertise - you can't really bluff your way in advanced statistics or data integration. But we should be hiring people who approach their jobs as collaborators and inventors, not just as experts. Being an expert is a defensive posture (I'm an expert only insofar as you're not, and I'm an expert in a particular thing...); being a collaborator and an inventor makes you the kind of person who can be approached with a new problem and look for a way to say 'maybe we can...'.

And then technology. More and more of the challenges we face in analytics are problems of technology - its capability and its scale, but also its ability to help us respond to uncertainty. There are a lot of good, powerful marketing technology software products around these days, with serious amounts of data behind them; but most of them exist to make doing certain things more intuitive, user-friendly, foolproof. They offer a set of definite 'yeses', lots of 'nos', but very few 'maybes'. We need technology and software that we can tinker with, recombine and plumb together in new ways to answer original questions - Lego bricks, not works of art.

At Maxus our analytics technology stack is designed for maximum flexibility - scalable on-demand big data warehousing, a lot of SQL, a lot of R and a bit of Python for analytical programming, and build-your-own visualisation layers using Tableau, Shiny and PowerBI among others. It's not always pretty, but it lets us say 'maybe' a lot more, and 'no' a lot less, when we're asked to help solve a problem we haven't encountered before, and get solutions working in days and weeks rather than months. And, of course, we put them in the hands of people who see 'maybe' as a challenge.

If you're buying analytics products or services, look beyond the elegant user interface. Most analytics tools, behind the scenes, involve a lot of people prodding scripts. Ask how open they make the underlying data; ask how locked the development roadmap is; and ask whether they will let you answer 'maybe' to an interesting question you haven't thought of yet.

563 words | ~3 min

George Monbiot is on fine form in the Guardian, pointing out all the jobs that he regards as tantamount to a nihilistic death spiral for graduates who choose to do them:

Those who graduate from the leading universities have more opportunity than most to find such purpose. So why do so many end up in pointless and destructive jobs? Finance, management consultancy, advertising, public relations, lobbying: these and other useless occupations consume thousands of the brightest students. To take such jobs at graduation, as many will in the next few weeks, is to amputate life close to its base.

In the face of this, the temptation is to go on the attack. Instead, I'd like to go on the defence.

So here, for the record, is why I chose to work in advertising and why I believe it's a meaningful way to spend a large proportion of your waking time.

I believe that ideas are the only things that make our lives better. Ideas can be good or bad, dangerous or consoling, humane or cruel, but almost no positive change in the human condition happens without an idea. An idea is, after all, a conception of how the world could be different.

But to create change, ideas need a voice. The world is full of ideas and most of them don't get heard. There are so many that even the ones that are heard need to go very quickly from being heard to being felt, or they are forgotten.

Advertising is the practice of giving ideas a voice. Good advertising gives ideas a voice that people will listen to, and makes ideas felt, not just heard.

There is bad advertising, and there are bad ideas that are advertised well; but if we want to live in a society where ideas circulate and have the chance to create change, the advertising of bad ideas is a risk we accept. This does not mean we shouldn't take away the ability to advertise some ideas in some ways, but it does mean we should do so with great caution. We should, at least, be careful about who gets to decide which ideas can and can't be advertised.

Advertising is not a particularly noble end, because it is not an end at all. It exists to improve ideas' chances of making change. And yes, for all we talk about big ideas most of the ideas we are paid to advertise are very small: a warmer home, a better razor, a slightly faster train journey. Taken together they have amounted to a revolution in the lives and living standards of hundreds of millions of people over the last century, though, so perhaps we should treat them with less contempt.

We cannot advertise big ideas without advertising small ones, or even good ones without bad ones, because we do not know where the next good, big idea will come from. Advertising is necessarily neither virtuous nor vicious, neither ingenious nor dumb, though its practitioners may be all of these. It is a means to uncertain ends, and for that reason I find it a hopeful discipline as well as an interesting one. To say we can do without it is to say we would rather not hear any more ideas, thank you very much; and I can't accept that as a creed.

208 words | ~1 min

We talk about measurement these days as if it gives us confidence. Sit through any sales pitch by an agency or tech company and they'll describe some version of a narrative in which some poor client goes from craven uncertainty to fearless innovation. (Sign on the line.)

In some ways, of course, measurement can and should make us more confident in making decisions.

But the best measurement is done with confidence, not before it. No amount of measuring will ever galvanise an organisation to act until it believes it can do something powerful. Good measurement and a data-driven approach can help define what to do, but the will has to be there first.

Richard Huntington wrote recently about the power of a confident client to generate great creative work. It's the same with measurement. A confident business does better measurement, even if it doesn't do more of it, because when we're confident, we're unafraid of answers that challenge us to change our ways.

Those of us who help clients measure should be unafraid to be confident ourselves about the value of what we do. We should help create the conditions for fundamental change, not just fractional improvement. That starts before we've measured anything.

765 words | ~4 min

Last week, we officially closed the doors at Fabric, the company where I've been working for the past three and a half years. Most of the business is merging into Possible, where Neil Miller, one of our two chief execs, has taken over as UK CEO.

It's a great next move for the little agency that could.

When I joined Fabric as a senior planner there were about eight or ten of us, split between a pair of tiny rooms in Westbourne Terrace and, if anything, an even smaller space in Seattle. At our peak, last year, we were about 65 - a genuine mix of creative, design and UX, strategy, technology, analytics and client service. Over that time we worked with some of the biggest and best businesses in the industry - among them Colgate-Palmolive, the Co-Operative, GSK, Heineken, KFC, News UK, the NHS, Samsung, Telefonica and Unilever.

When we began, as a WPP Digital start-up, we pitched Fabric as 'an agency with a digital brain' - strategically and creatively smart, but with big data technology under the hood that would give us a clear view of user journeys and behaviours across sites, campaigns, CRM, email, ecommerce, etc. It's still probably the best single description of what we did. On the one hand, we did groundbreaking, demonstrably effective, award-winning digital/social strategy and creative for clients, solo or alongside their advertising and media agencies. On the other, we built what's probably still the fastest and most scalable analytical data management platform I've ever seen, with a seriously talented and hardworking dev/ops team. I was lucky enough to see both sides of the business up close, moving as I did from being strategy director on the 'agency' accounts, to being head of product management and analytics on the technology side (the 'digital brain', if you like).

One criticism you could level at us - and fairly - is that we never found a single business model, the big bet on big data that would generate explosive scale and turn us into a marketing technology superpower ripe for acquisition, another Omniture or BlueKai or Xaxis. We could never resist the lure of the next interesting project, the next smart way to tell stories with data. An objective weakness, maybe, but one I will always have a sneaking admiration for, in a world where too many startups are looking for a quick exit.

When I left, in the spirit of being inspired by data, I decided to tally up what we'd achieved as a company in all that time. The results (in only-slightly-edited form below) speak to Fabric's variety, enthusiasm, occasional distractedness, and sheer originality:

1,400 days of social management. 940 creative Facebook posts (that's a new ad every day and a half on average). 5,100 tweets. 694 million Facebook impressions. 34 million views of tweets. One big data platform. An app used by 189 people, for 29 brands and 9 agencies, across 150 countries. Three years of site and media data collected. Over 1 billion recorded pageviews. 250 million cookies. 30,000 domains. 4.7 billion media impressions. 1,847 ad campaigns. Twelve websites built for clients. Five websites built for Fabric. 40 sites hosted. Five Facebook apps. One film. One mobile app. Three crisis management situations. One eye-tracking game. One national tour in in an airstream trailer. One big orange book on social media. Two brand new brands. 6 million people reached a week. Double-digit growth for Heineken. The fastest-growing premium cider in the UK. The second biggest fast-food brand on the internet. One set of government data guidelines. Seven awards for creativity and effectiveness.

And no regrets.

It's why the incorporation into Possible makes sense, letting us turn those different kinds of excellence - in brand planning, in analytics, in creative - into valuable parts of a bigger whole that wants to be a genuine digital full-service agency. Fabric adds so many different kinds of talent into that mix, not least an ambitious leadership team.

As for me, I'm moving on. On Monday, I join Maxus as Head of Data and Effectiveness in the London office. It's a dream job, for a great business with an some outstanding list of clients. If there's a media agency today, five years after Fabric was founded, that can claim to have been born with a digital brain, Maxus is surely it. I hope I can bring with me an enthusiasm for what data can do, and an obsession for getting to the heart of what really creates change and growth for clients.

Here goes...

952 words | ~5 min

The internet of things is the new big data, to judge from the hockey-stick ascent of its Google search volumes:

(Big data is blue, the Internet of Things is red).

The paid-for search results are stacked three to a page like waiting aeroplanes. The thought leadership pieces are popping out all over. The venture capitalists are circling.

Having lived through the hype cycle around big data, this sounds like a good time to try to pitch in with something sensible, because pretty soon the question 'What does the internet of things mean for brands?' will soon be on every industry masthead. Shortly before 'And what is it, by the way?'

What is it?

An industry buzzphrase for the increasing internet-connectedness of business IT systems and consumer electronic devices. Lots of things in your business and in your home, car, etc., contain sensors and other bits of tech that can now connect to the internet and push data out into it. There's a huge amount of this data, and it comes in lots of different formats. Some devices - for example, the charming Nest thermostat - have well-designed interfaces that let them talk to other devices (your smoke alarm, for instance), and be controlled straightforwardly using online tools. But for every Nest that lets you control your central heating from your home, there are a thousand devices spewing out gigabytes of raw and messy log-file data.

To put it in an old-fashioned, less buzzworthy way: it's a huge increase in the amount of information you get back from your supply chain.

Does it matter to marketers?

I think so, yes - but that's no reason to go crazy. It matters not because it's new technology, but because it provides fresh information. The strongest brands over the next decade (I think) will be those that integrate their distribution chains and provide good-quality, differentiating brand experiences at each point from advertising, to exploration, to purchase, to service and delivery. Think Amazon, think Apple, think Starbucks as examples of brands who own more of their route to market than their competitors, and make each bit of that route a good branded experience for their customers.

You might be in one of those businesses already, or working for one of their agencies. If you are, think about how many of those moments of interaction - from your ads to your apps to your loyalty cards to your contactless payment systems to your delivery trucks - are now built with technology that can spit out data about how well they're working.

Think of it like ad tracking or web analytics - only with much, much more data about a lot more things. Noisy, confusing, but potentially very useful if you can get to it and ask it the right questions.

What should I do about it?

Right now, nothing. If you're a CIO or a CMO, pretty soon people will be pitching stuff at you left, right and centre with software platforms that claim to be purpose-built to help your brand survive the internet-of-things era.

Most of this is hype. Remember, for most of these firms, you're not the main source of revenue - venture capitalists are. 90% of these firms will go under because they fail to find a business model that marketers will pay for. So take a breath before you sign.

If you're a marketer, start by talking to your colleagues in IT, operations and finance, not to software salespeople. Find out how your supply chain is built, how much of it you own, and where you might be able to get more data out. Set your team and your agency the challenge of prioritising that information. Which bits, if you had them, might make you change what you're doing today?

A point of view - general beats specific

I worked through the big data hype, I built data and analytics products and I used them. I saw a lot of competitors, and a lot of in-house projects, succeed and fail..

The thing most of the failures have in common is that they tried to be too specific. They ended up creating digital products that did a particular type of analytics well - e.g. they only looked at social media data, or they only created timelines, or they assumed that every business pushed its customers along the same four-step customer journey.

The successes didn't bet the farm on trying to predict how marketers would use big data. They concentrated on capturing the data in really granular, flexible ways, and making it fairly easy to roll that data up in lots of different combinations, so you could ask it lots of different questions. Rather than being completely foolproof from the start, they made it easier to spin up new applications, views and metrics quickly.

It will be the same for the internet of things. If you want to pick a supplier, or back a start-up, look for one that's focused on getting the fundamentals right - answering the question, 'how do I capture individual events from devices that may not even exist yet?' These won't be the firms that have the prettiest mock-up dashboards, or even the slickest sales pitches, but they're the ones that will still be around in three years' time when you realise you need to be able to measure some obscure performance metric on the app you made for a driverless car that hasn't yet been built.