Alex Steer - analytics and digital productshttp://alexsteer.net/239.html05/01/2018Algorithms will not kill brands. Really.

Right then, marketing industry, we need to talk. See, there’s this story going round that the future of brands is under threat from algorithms. It’s nonsense, and it does disservice to our trade.

Most versions of the story go like this. Over the last decade or so, and especially in the last few years, media consumption has switched dramatically from environments controlled by editors, to environments controlled by algorithms, which filter and prioritise the content we see (hear, watch, etc.) based on knowledge of our own preferences, generated through machine learning. I talked about this to the Advertising Association in early 2017, and I raised the prospect that as the use of algorithmic decision-making extends from media prioritisation to e-commerce, existing brands might have to work a bit harder to make sure that they don’t get relegated to becoming back-end service providers. For example, if I am constantly asking the Amazon Echo on my kitchen counter to tell, sell or play me stuff, I may lose the sense of regularly interacting with the brands who supply those services (Spotify, National Rail, Jamie Oliver, etc).

But the narrative of ‘algorithms vs brands’ is taking this to a ludicrous extreme. Take for example this extraordinary rundown from Scott Galloway:

Brands are shorthand for a set of associations that consumers use for guidance toward the right product. CPG brands have spent billions and decades building brand via messaging, packaging, placement (eye level), price, creative, endcaps, etc. The internet loses much of this, since the impact of zeroes and ones is no match for atoms, and much of the design and feel of the product loses dimension, specifically from three to two (dimensions). As a result, the internet has become a channel to harvest, rather than build, brands.

However, all these weapons of brand … all of them go away with voice. No packaging, no logos, no price even. The foreshadowing of the death of brand, at the hand of voice, can be seen in search queries.

Crikey.

Before we go any further, for some reason ‘brand’ is one of those terms that everybody seems to interpret differently. Which is surprising, because a company’s brand is normally its single most valuable asset, typically account for about two-thirds to three-quarters of volume across a year. You would think that, as an industry, we’d understand this and be pretty clear about what a brand is, the way that businesses tend to be pretty clear about what a pension fund or a manufacturing plant is. So, for the avoidance of doubt, I define a brand as a recognisable identity of a business in the marketplace, which creates value by increasing demand and discovery for its products or services.

So, I think this ‘death of brands’ narrative is rubbish. Not just because it’s not true, but because it’s the opposite of the truth. Let’s be loud and clear on this one.

The environment the scare stories describe, is the exact environment that brands were built for.

Cast your mind back to the mid nineteenth century in Western Europe and the young United States. As the economy went through a series of dramatic structural shifts, large populations began to urbanise and living standards went up, and with them so did competition for goods and services. Manufacturers began to find their profits under threat by new intermediaries. These intermediaries were large, powerful, and had enormous and rapidly growing user bases (as we’d now call them). Their power was cited as unfair influence, as the death of the manufacturer, as a slippery slope towards commoditisation and a race to the bottom. These intermediaries exercised almost total control over the goods that people saw, they could put substantial pressure on pricing, and their customers loved them for it.

They were called shops.

So manufacturers began to invest in raising the profile of their products in people’s minds. They used media to push back against the price wars and the margin pressure. They used creativity to make their products more appealing, more pleasing, more meaningful and differentiated — so that customers would ask for them by name, and so that shops would look bad if they did not stock them.

Sounding familiar yet?

Brands thrive under this sort of pressure, because they become the only unfair advantage that a business can deploy. Algorithms and ecommerce won’t kill brands, they will kill some brands, and they will raise the stakes. If your route to market involves someone standing in a kitchen and asking a plastic and metal box to ship a product to you, you need to make sure you’re already in the kitchen, by being in the mind. And there is a very good, reliable, extremely well proven mechanism for making that happen.

It’s called brand advertising. Ask for it by name.

http://alexsteer.net/239.htmlAlex Steer
http://alexsteer.net/238.html22/10/2017False optimisation

Right then. It's been almost a year since I last posted here - a year in the life of my agency, Maxus, that I look forward to talking about in more detail in future when the paint has dried. (Short version: IPA Effectiveness awards; I became CSO; restructure and retraining; building new cross-media planning tech; best agency in Top 100 Companies to Work for list; big new business win; merger with MEC to become Wavemaker as of Jan 2018.)

For now, a few notes on an idea that sits behind an increasing amount of what we do, and talk about, as an industry: optimisation.

First, a quick definition. Optimisation is the use of analytical methods to select the best from a series of specified options, to deliver a specified outcome. These days, a lot of optimisation is the automated application of analytical learning. I wrote a long piece last year on some of the basic machine learning applications: anomaly detection, conditional probability and inference. Optimisation can take any of these types of analysis as inputs, and an optimiser is any algorithm that makes choices based on the probability of success from the analysis it has done. Optimisation crops up in all sorts of marketing applications, that we tend to discuss as if they were separate things:

  • Programmatic buying
  • Onsite personalisation
  • Email marketing automation
  • AB and multivariate testing
  • Digital attribution
  • Marketing mix modelling
  • Propensity modelling
  • Predictive analytics
  • Dynamic creative
  • Chatbots

...and so on, until we've got enough buzzwords to fill a conference. All of these are versions of optimisation, differently packaged.

When I say optimisation 'sits behind' a lot of what we do in marketing and media today, it's because optimisation is almost the opposite of an industry buzzword: a term that has remained surprisingly constant in marketing discourse over the last few years, while its application has broadened considerably. By way of lightly-researched reference, here are Google search volume trends for 'optimisation' and 'machine learning' in the UK over the last five years (it makes little difference, by the way, if you search for the US or UK spelling):

Google trends: Optimisation and Machine Learning, UK, past five years

Search volumes for optimisation (blue) have remained fairly constant over the last half-decade (and are driven mainly by 'search engine optimisation'), whereas 'machine learning' (red) has risen, and crossed over in early 2016. I show this as just one cherry-picked example of a tendency for marketing language to imply that there is more innovation in the market that actually exists. We can see this more clearly by looking at the phenomenon of hype clustering around machine learning.

Hype clustering

Let's look back at the Gartner Hype Cycle, the canonical field guide to febrile technology jargon, from July 2011:

Gartner Hype Cycle: emerging technology, Q2 2011

We can see a good distribution of technologies that rely on optimisation, all the way across the cycle: from video analytics and natural-language question answering at the wild end, to predictive analytics and speech recognition approaching maturity.

Fast forward six years to the most recent hype cycle from July 2017:

Gartner Hype Cycle: emerging technology, Q2 2017

'Machine learning' and 'deep learning' have found their way to the top of the hype curve... while everything else on the list has disappeared (except the very-far-off category of 'artificial general intelligence'). Fascinatingly, machine learning is predicted to reach maturity within 2-5 years, whereas some of the technologies previously on the list six years ago were predicted to have matured by now. In other words, several of the technologies that were supposedly past the point of peak hype in 2011 are now back there, but rechristened under the umbrella of machine learning.

Machine learning is a classic piece of hype clustering: it combines a lot of analytics and technical methods that are themselves no longer hypeworthy, with a few that are still extremely niche. The result is something that sounds new enough to be exciting, wide-ranging enough to be sellable to big businesses in large quantities - very much the situation that big data was in when it crested the hype cycle in 2013.

Sitting behind a lot of 'machine learning' is good old-fashioned optimisation, albeit increasingly powered by faster computing and the ability to run over larger volumes of data than a few years ago. Across digital media, paid search, paid social, CRM, digital content management and ecommerce, businesses are beginning to rely hugely on optimisation algorithms of one sort or another, often without a clear sense of how that optimisation is working.

This is, it won't surprise you to learn, hugely problematic.

Doing the right thing

Optimisation is the application of analysis to answer the question: how do I do the right thing? Automated mathematical optimisation is a very elegant solution, especially given the processing firepower we can throw at it these days. But it comes with a great big caveat.

You have to know what the right thing is.

In the disciplines where automated optimisation first sprung up, this was relatively straightforward. In paid search advertising, for example, you want to match ad copy to keywords in a way that gets as many people who have searched for 'discount legal services' or 'terrifyingly lifelike clown statues' to click on your ads as possible. In ecommerce optimisation, you want to test versions of your checkout page flow in order to maximise the proportion of people who make it right through to payment. In a political email campaign, you want as many of the people on your mailing list to open the message, click the link and donate to your candidate as possible. In all of these, there's a clear right answer, because you have:

  1. a fixed group of people
  2. a fixed objective
  3. an unambiguous set of success measures

Those are the kinds of problems that optimisation can help you solve more quickly and efficiently than by trial and error, or manual number-crunching

The difficulty arises when we extend the logic of optimisation, without extending the constraints. In other words, when we have an industry that is in love with the rhetoric of analytics and machine learning, that will try and extend that rhetoric to places where it doesn't fit so neatly.

False optimisation

Over the last few years we've seen a rush of brand marketing budgets into digital media. This is sensible in one respect as it reflects shifting media consumption habits and the need for brands, at a basic level, to put themselves where their audiences are looking. On the other hand, it's exposed some of the bad habits of a digital media ecosystem previously funded largely by performance marketing budgets, and some of the bigger advertisers have acknowledged their initial naivety in managing digital media effectively. Cue a situation where lots of brand marketers are concerned about the variability of the quality of their advertising delivery, especially the impact of digital's 'unholy trinity' of brand safety, viewability and fraud.

And what do 'worry about variability' plus 'digital marketing' equal? That's right: optimisation.

Flash forward and we find ourselves in a marketplace where the logic of optimisation is being sold heavily to brand marketers. I've lost count of the number of solutions that claim to be able to optimise the targeting of brand marketing campaigns in real time. The lingo varies for each sales pitch, but there are two persistent themes that come out:

  1. Optimising your brand campaign targeting based on quality.
  2. Optimising your brand campaign targeting based on brand impact.

Both of these, at first look, sound unproblematic, beneficial, and a smart thing to do as a responsible marketer who wants to have a good relationship with senior management and finance. Who could argue with the idea of higher-quality, more impactful brand campaigns?

The first of them is valid. It is possible to score media impressions based on their likely viewability, contextual brand safety, and delivery to real human beings in your target audience. While the ability to do this in practice varies, there is nothing wrong with this as an aim. It can be a distraction if it becomes the objective on which media delivery is measured, rather than a hygiene factor; but this is just a case of not letting the tail wag the dog.

The second looks the same, but it isn't, and it can be fatal to the effectiveness of brand advertising. Here's why.

Brand advertising, if properly planned, isn't designed towards a short-term conversion objective (e.g. a sale). Rather, it is the advertising you do to build brand equity, that then pays off when people are in the market for your category, by improving their propensity to choose you, or reducing your cost of generating short-term sales. In other words, brand advertising softens us up.

Why does this matter? Because optimisation was designed to operate at the sharp end of the purchase funnel (so to speak) - to find the option among a set that is most likely to lead to a positive outcome. When you apply this logic to brand advertising, these are the steps that an optimiser goes through:

  1. Measure the change in brand perception that results from exposure to advertising (e.g. through research)
  2. Find the types of people that exhibit the greatest improvement in brand perception
  3. Prioritise showing the advertising to those types of people

Now, remember what we said earlier about the three golden rules of optimisation:

  1. a fixed group of people
  2. a fixed objective
  3. an unambiguous set of success measures

Optimising the targeting of your brand advertising to improve its success metrics violates the first rule.

This is what we call preaching to the nearly-converted: serving brand advertising to people who can easily be nudged into having a higher opinion of your brand.

It is false optimisation because it confuses objectives with metrics. The objective of brand advertising is to change people's minds, or confirm their suspicions, about brands. A measure for this is the aggregate change in strength of perception among the buying audience. DIagnostically, research can be used to understand if the advertising has any weak spots (e.g. it creates little change among older women or younger men). But a diagnosis is not necessarily grounds for optimisation. If you only serve your ads to people whose minds are most easily changed, you will drive splendid short-term results but you will ultimately run out of willing buyers, by having deliberately neglected to keep advertising to your tougher prospects. It's the media equivalent of being a head of state and only listening to the advice of people who tell you you're doing brilliantly - the short-term kick is tremendous, but the potential for unpleasant surprise is significant.

Preaching to the valuable

The heretical-sounding conclusion is: you should not optimise the targeting of your brand campaigns.

Take a deep breath, have a sit down. But I mean it. You can optimise the delivery, by which I mean:

  • Place ads in contexts that beneficially affect brand perceptions
  • Show your ads only to people in your target buying audience (not to people who can't buy you, or to bots)
  • Show better-quality impressions (more viewable, in brand-safe contexts)
  • Show creative that gets a bigger response from your target audience

But do not narrow your targeting based on the subsets of your audience whose perceptions of you respond best. That is a fast track to eliminating the ability of your brand to recruit new buyers over time and will create a cycle of false optimisation where you not only preach to the converted, but you only say the things they most like to hear.

Brand advertising is the art of preaching to the valuable. It means finding out which people you need to buy your brand in order to make enough money, and refining your messaging to improve the likelihood that they will. Knowing that requires a serious investment in knowledge and analysis before you start, to find your most viable sources of growth and the best places and ways to advertise based on historic information. This is anathema to people who sell ad-tech for a living, for whom 'test and learn' is of almost theological importance, not least because it encourages more time using and tweaking the technology. The 'advertise first, ask questions later' approach looks like rigour in the heat of the moment (real-time data! ongoing optimisation!) but is the exact opposite.

Testing and learning is exactly the right approach when you have multiple options to get the same outcome from the same group of people. It is precisely the wrong thing to do if it leads to you changing which people to speak to. It's like asking out the girl/boy of your dreams, getting turned down, then asking out someone else using the same line, and thinking you've succeeded. Change the context, change the line, but don't change the audience.

http://alexsteer.net/238.htmlAlex Steer
http://alexsteer.net/237.html11/11/2016From data science to marketing science

Last week I had the privilege of attending the IPA Effectiveness Awards, where we picked up a Silver for our work with Plusnet over the past five years. Our paper was called out in the judges' review for creating a culture of sustained effectiveness with an ongoing commitment to rigorous testing. The result is that for every pound spent on advertising (creative, media and agency fees), Plusnet have got over £4 back in new customer revenue.

For me it's a great example of effectiveness done right, by a team of true marketing scientists working alongside a great media planning team, a strong creative agency and a supportive client.

But it also provides a strong contrast to the way I see analytics done in a lot of marketing organisations these days.

A few years back, Harvard Business Review proclaimed data scientist to be the sexiest job of the decade. A quick job search reveals thousands of vacancies, many of them with wildly varying job specs. In fact, right now one of those vacancies is in my own team. By our definition, a data scientist is a statistical developer - someone who can turn data analysis methods into working, scalable code so they can be automated and run faster. This is a vital skill for any team that wants to be taken seriously in a world where the volume of available data to be processed, and the frequency of having to make sense of it, have both increased vastly.

But the rise of the data science star in marketing departments and agencies is not an unqualified good. It mirrors the emergence of a generation of mid-level to senior marketers many of whose training is almost entirely within either digital or direct response businesses. This is due in large part, I suspect, to the reductions in brand marketing spend and headcount in the years after the 2008 financial crash. There is a 'lost generation' of brand marketers and that cohort is now becoming relatively senior but without having had the training in many of brand marketing's craft skills, like designing a brand tracker, interpreting qual research or using econometric modelling.

The result is an assumption in many businesses that marketing analytics is largely just a data and software problem - a view often promoted by technology companies too, unsurprisingly. The result is that we as an industry have been hiring people who can do stats and write code hand-over-fist, calling them data scientists and assuming that they can figure out the complexities of predicting marketing performance.

It's an enormously dangerous tendency, the same one that the financial trading industry fell victim to a decade ago. The reason why is hard to explain if you don't do analytics for a living, but in a nutshell: pure data scientists, without marketing experience, tend to make bad assumptions about how marketing works. They bake these assumptions into their code and their models, and you end up with a badly skewed view of how your marketing works. It's very fast, the code is efficient, and it's fully automated, but it's wrong, like a driverless car accelerating smoothly towards a cliff edge. Worse, if you don't have the ability to check the assumptions (because you can't code, or don't understand the statistics), you have no way of knowing whether the assumptions in your ultrafast machine learning algorithms are brilliant or bogus.

Even when done manually rather than automated, bad assumptions can kill a brand. Often these assumptions are incredibly basic - for instance, I've seen a lot of marketing attribution models that assume that marketing only influences sales on the day (or even in the hour) that people see it, or that a marketing channel only works if it generates a direct response. This is the logic of early 2000s email marketing, being applied to large integrated brand marketing budgets with predictably hilarious but terrible results.

Data science - turning maths into code - is a vital skill, but it is not the whole game. It's time to start valuing marketing science - advanced analytical ability informed by practical experience of working with brands and advertising. It's time to start growing our own talent rather than hiring from outside; time to start training people in the harder disciplines of econometric modelling, research design and segmentation; time to recruit social scientists and economists as well as pure mathematicians and computer scientists; and time to be proud of the bits of what we do that aren't automated as well as the bits that are. Time, in other words, to insist that analytics knows a bit more about the complexities of how real people respond to brands.

http://alexsteer.net/237.htmlAlex Steer
http://alexsteer.net/236.html08/10/2016Why the pound is down: a crash course in machine learning

You might have seen in today's news that the trading value of the pound fell by 6% overnight, apparently triggered by automated currency trading algorithms. Here's how that looked on a chart of sterling's value against the dollar:

Sterling vs Dollar - late Sept to early Oct 2016 (Source: FT.com)

It's a fascinating read - we live in a world where decisions made by computers without any human intervention can have this sort of impact. And since 'machine learning' of this kind is a hot topic in marketing right now, and powers a lot of programmatic buying, today's news is a good excuse to think about the basics of how machines learn.

So, here's a a quick guide to how machine learning works, and why the pound in your pocket is worth a bit less than it was when you went to bed (thanks, algorithms).

Anomaly detection: expecting the unexpected

Machine learning is a branch of computer science and statistics, that looks for patterns in data and makes decisions based on what it finds. In financial trading, and in media buying, we need to find abnormalities quickly: a stock that is about to jump in price, a level of sharing around a social post that means it is going viral, or a level of traffic to your ecommerce portal that means you need to start adding more computing power to stop it crashing.

For example, these are Google searches for Volkswagen in the UK over the past five years. See if you can spot when the emission scandal happened.

Google search trend for Volkswagen, UK, 2011-16

If you wanted to monitor this automatically, you'd use an anomaly detection algorithm. If you've ever used a TV attribution tool, you've seen anomaly detection at work, picking up the jumps in site traffic that are attributable to TV spots.

Anomaly detectors are used to set triggers - rules that say things like If the value of a currency falls by X amount in Y minutes, we think this is abnormal, so start selling it. This is what seems to have happened overnight to the pound. One over-sensitive algorithm starts selling sterling, which drives down its value further, so other slightly less sensitive algorithms notice and start selling, which drives down the price further, and so on...

Conditional probability: playing the odds

Most decision-making, especially at speed, isn't based on complete certainty. Algorithms need to be able to make decisions based on a level of confidence rather than total knowledge.

For example, is this a duck or a rabbit?

Duck-rabbit illusion - 1

At this point you might say 'I don't know' - i.e. you assign 50:50 odds.

How about now?

Duck-rabbit illusion - 2

You take in new information to make a decision - if it quacks out of that end, it's a duck. (Probably.)

This is conditional probability - updating your confidence based on a new information in context. 'This is either a rabbit or a duck', becomes 'this is a duck, given that it quacks'. We use conditional probability in digital attribution ('what is the likelihood of converting if you have seen this ad, vs if you haven't?') and we use it in audience targeting for programmatic buying: given that I've seen you on a whole load of wedding planning sites, what is the likelihood that you're planning a wedding?

Again, conditional probability can go wrong if we're too strict or not strict enough with our conditions. If I decide you're planning a wedding because I've seen you on one vaguely wedding-related site, I'm probably going to be wrong a lot of the time (known as a high false positive match rate). If I insist that I have to see you on 100 wedding planning sites before I target you as a wedding planner, I'm going to miss lots of people who really are planning weddings (a low true positive match rate).

Currency trading algorithms use conditional probability: given that the value of the pound is down X% in Y minutes, how likely is it that the pound is going to fall even lower? An over-sensitive algorithm, with too high a false positive rate, can start selling the pound when there's nothing to worry about.

Inference: how machines learn, and how we use brands

Anomaly detection and conditional probability are used together to help machines learn and classify, known as inference because computers are inferring information from data.

For example, a few years ago Google trained an algorithm to recognise whether YouTube videos contained cats. It did this by analysing the frame-by-frame image data from thousands of videos that did contain cats.

Google machine learning - cats

But it also trained the algorithm on lots of videos that weren't cats. That's because the algorithm is a classifier, designed to assign items to different categories. The Google classifier was designed to answer the question: does this image data look more like the videos of cats I've seen, or more like the videos of not-cats?

Good inference requires these training sets of data so that. A badly-trained classifier will assign too many things to the category it knows best, assuming that everything with a big face and whiskers is a cat.

Cat and seal

We use classifiers in audience targeting and programmatic buying, to assign online users to target audience groups. For example, in Turbine (the Xaxis data platform) each user profile might have thousands of different data points attached to it. A classifier will look at all of these and, based on what it's seen before, make a decision about whether a user is old or young, rich or poor, male or female... So inference and classification are vital for turning all those data points into audiences that we can select and target.

But we are also classifiers ourselves - our brains are lazy and designed to make decisions at speed. So when we go into the supermarket we look for cues that the thing we're picking up is our normal trusted brand of butter, bathroom cleaner or washing-up liquid. Retailers like Aldi hijack our inbuilt classification mechanisms to prompt us to choose their own brands:

Named brands and Aldi equivalents

From metrics to models

There's so much data available to us now - as marketers, as well as stock traders - that we can't look at each data point individually before making decisions. We have to do get used to using techniques like anomaly detection, conditional probability and classification to guide us and show us what is probably the right thing to do, to optimise our media or our creative. Machine learning can help us do this faster and using larger volumes of data. At Maxus we call this moving from metrics to models and it's one of the things we can help clients do to be more effective in their marketing. As we've seen today on the currency market, though, it can be scary and it can have unexpected consequences if not done properly.

http://alexsteer.net/236.htmlAlex Steer
http://alexsteer.net/235.html24/09/2016Facebook video metrics, and why platforms shouldn't mark their own homework

Originally posted on the Maxus blog

Facebook has revealed that for the last two years it has been overstating video completion rates, due to an error in the way it calculates views.

Because Facebook only counts as a 'view' any video consumption over three seconds, it has been applying the same logic to its video completion rate metric - so the metric tells us not how many people who started watching a video then finished it, as we would expect, but how many got past the first three seconds and then finished. It is estimate that their video conversion rates have been overstated by 60 to 80% for the last two years.

Facebook are now hurrying to amend the metric, which they are treating as a replacement, but which is in reality a bug fix.

The news is understandably shocking to advertisers and their agencies, many of whom have been investing heavily in video and using these metrics to monitor and justify spend.

But it is also sadly predictable - an inevitable consequence of the lack of auditability in the metrics produced by many media platforms, not just Facebook.

Facebook have not allowed independent tracking of video completion rates on their platform, meaning that the only way to get video completion data is from Facebook itself. They are not unique in this, and we see this 'metric monopoly' behaviour from many of the digital media platforms, usually citing reasons such as user experience or privacy. Rather than allow advertisers to conduct their own measurement, many platforms are now offering to provide advanced analytics to brands who buy with them, including digital attribution and cross-device tracking. The data and the algorithms that power this measurement remain firmly in the media owner's black box.

Today's news makes it clear how unacceptable an arrangement this is. At Maxus we talk about the importance of 'Open Video' - planning video investment across many channels and touchpoints, reflecting people's changing use of media and making the most of the vast and proliferating range of video types that exist today, from long-form how-tos and product demos to seconds-long bitesize experiences in the newsfeed. As video changes, it creates more opportunities for brands, far beyond the thirty-second spot.

But Open Video requires a commitment to open measurement. As advertisers and agencies we have to be able to gather a coherent, consistent picture of what people are seeing and how content is performing. We are investing significant effort in building the right measurement and technology stack to help clients plan, deliver, measure and optimise Open Video strategies, including advanced quality scoring, attribution and modelling that lets us see how exposure in one channel compares to another in terms of quality, completeness and effectiveness.

Media platforms create amazing new possibilities and are important partners to advertisers and agencies in innovation and delivery. But they should not be allowed to mark their own homework. Measurement and attribution should always be independent of media delivery, available to agencies and auditable by clients. Any other arrangement is a compromise - and, as we've seen this week, a risk.

http://alexsteer.net/235.htmlAlex Steer
http://alexsteer.net/234.html27/04/2016YouTube vs TV: where should advertisers stand in the 'battle of the boxes'?

Tom Dunn and I wrote this on Brand Republic this week. Reposting...

It’s been an extraordinary couple of weeks on planet video. The TV industry body, Thinkbox, and Google’s YouTube have been engaged in a full and frank exchange of views that, both are at pains to point out, is absolutely not a fight. The topic they are definitely-not-arguing about is a fundamental one: where advertisers should spend their video advertising budgets.

The totally-not-trouble began brewing back in October, with a punchy statement from Google’s UK & Ireland Managing Director, Eileen Naughton, making the case the advertisers should shift 24% of their TV budgets into YouTube, especially if they’re targeting 16-34 year olds.

Last week, Thinkbox came back swinging, calling the Google claim ‘ill-founded and irresponsible’. In the intervening months they have been analysing viewing and advertising data, to find that while YouTube made up 10.3% of 16-24 year-old’s video consumption (v.s TV’s 43.5%), it made up just 1.4% of their video advertising consumption (with TV coming in at a whopping 77.5%).

Within a few days, Google wheeled out their econometric big guns and shot back with an even bigger claim: making the case for advertiser that YouTube offers a 50% better return on investment than that of television, and that 5-25% of video budgets should be spent on YouTube.

Now, it’s definitely not a scrap, but it seems that marketers and agencies are stuck in the middle and in a Brexit kind of way, need to make up their minds where they stand. And worst of all, the kinds of spats that used to be conducted via general pronouncements about consumer trends and attitudes are now being tooled up with findings from data.

Or, should we say, “findings”. From “data”.

Thinkbox and YouTube have stood out in the industry over the years for their commitment to research and measurement. Yet, in the battle of the boxes it seems both have lost focus and the numbers used raise more questions than answers.

As the heads of effectiveness and futures at a media agency, we both spend a lot of our time trying to find the balance between what’s working today and what’s changing tomorrow. This conversation about the impact of video channels matters. Because of the scale of the change we are already seeing in media consumption, and the greater scale of changes to come. Is the leapfrogging of linear TV by online video channels among the under-25s a temporary behaviour or a deeper generational shift? Will the box in the living room lose its next generation of viewers permanently, or will it welcome them back with open arms as a large generation, now house-sharing (or overstaying their welcome with their parents) find themselves with living rooms (and remote controls) of their own.

Either way, the world in which video advertising lives is changing. This stuff matters to all of us who use video to tell stories, make connections and grow our brands. That’s why it’s good to see media owners and industry bodies taking it seriously – but also why the use of data as weaponry has left something to be desired.

In the blue corner, ThinkBox. We’re puzzled by their argument more than by their numbers. They seem to be saying the because more advertising is consumed on TV, clients should advertise on TV more. Yet this comes across as circular logic – saying we should put our ads on TV because that’s where the ads are. If there is a 4:1 ratio of content consumption between TV and YouTube, but a 98:1 ratio of advertising consumption, surely that implies that YouTube has a lot more headroom? It’s fair to say that as consumers we still accept a far higher payload of advertising per piece of content on TV than we do on YouTube, but that’s as much to do with the vastly different buying models, available formats, and modes of consumption than ability of the platforms to deliver exposure.

In the red corner, YouTube, with is headline-grabbing claim of 50% higher ROI. The rationale for this is a study done with Data2Decisions, an econometrics and analytics consultancy. This is a good sign that there will be some robust measurement underpinning this, but more transparency is needed before this can be taken seriously.

The analysis uses a combination of market mix modelling (econometrics) to show the total contribution of TV vs. online video, and ecosystem modelling to dig down into the performance of different individual video channels. This is interesting stuff, and makes for good headlines, but it raises a lot of questions. We think there are three reasons to be cautious.

First, we don’t know what the period of research was, or how many brands, campaigns and categories were included. We don’t know what kind of campaigns they were. Brand-building vs. short-term sales-driving, for example. Like a clinical trial, we need to be confident that if we give you the same budgetary medicine, we know what the side effects might be.

Second, we’ve only seen the headline figures (mainly about ROI). This would be a misguided basis to start shifting huge chunks of budget around.

For example, if we spend £1 million on TV and drive £1.2 million in sales, we have an ROI of £1.20. If we spend £10,000 on YouTube and drive £18,000 of sales, we have an ROI of £1.80. This is 50% higher than TV, but is also delivering far less money. The research headlines don’t tell you what would happen to the ROI if you put more money into YouTube. Would it stay at 50% better than TV or would it start to diminish?

Third, the headlines are only comparing TV and YouTube. To do this properly, we need to understand the relative impact of other video channels to. YouTube’s ROI might be higher than TV’s, but how does it compare to the rest of the online pack?

We welcome the industry taking cross-platform video measurement seriously. At Maxus we have an ‘Open Video’ philosophy to setting video investment strategy, and we are developing tools and technology to plan, measure and optimise across different video channels efficiently and effecitvely. We use market mix modelling and attribution to identify the impact of different video channels, and advanced tracking to make sure that we have a common approach to measuring things like viewability, brand safety and inventory quality across video channels.

That’s why we’re asking both YouTube and ThinkBox to put down their sharpened spreadsheets and to back up the headlines with evidence. It’s not a matter of suddenly shifting money from TV into YouTube, but of understanding what the right channel mix is for individual brands based on their needs, their priorities and their audiences.

Entertaining as the ringside seat has been, advertisers deserve a bit better. It’s time for a grown-up conversation about what’s working now, and what’s changing next.

http://alexsteer.net/234.htmlAlex Steer
http://alexsteer.net/233.html01/04/2016Saying no to marketing tech's Project Fear

I wrote this for the Maxus blog - reposting here...

I got an email this morning whose subject line read: 'If you're just keeping up to date in marketing tech... You're not doing enough.'

I get similar emails every day, and so do our clients. They reflect the growing tendency of marketing technology companies to sound like people who are trying to sell you gym membership. Except that rather than muscle-bound personal trainers shouting about rock-hard abs, this assault on marketers' sanity and dignity comes via whitepapers, webinars and other content marketing channels.

In some ways this is nothing new. 'Fear, Uncertainty and Doubt' has been part of the IT salesperson's kit for a generation – and is still, famously, associated with technology giants like Microsoft and IBM as they slugged it out for dominance of the enterprise computing sector in the 1990s. But whereas old-school FUD was all about knocking the competition, the new school is all about knocking the client.

Those of us who work in digital, technology and analytics are subjected to a sustained Project Fear campaign from many technology providers. (Before you write in and complain, there are notable exceptions, of course – but sadly they're notable because they're exceptions.) It's as if, now that marketers are huge spenders in data and tech, many vendors are determined to keep them feeling confused and vulnerable. Despite all the evidence to the contrary, the industry is behaving as if it's a seller's market. The kind of advertising hard-sell that went out of favour in the mid 1960s seems to be alive and well here.

If we as marketers still talked to our consumers the way many tech and data companies talk to us, those consumers would long since have abandoned our brands.

The narrative of Project Fear is consistent: every client who has bought our product has transformed their relationship with customers in ways you haven't thought of yet. You're being left behind. Your customers will abandon you and tough guys will kick sand in your face. Without this gym membership – sorry, enterprise software license – you'll be laughed out of the bar by your peers.

This message is broadcast through social media and the trade press every day. It continues to have power because there are so many topics it can cover. If as a marketer you feel like you've mastered web analytics or ad serving, there's always digital attribution, cross-device tracking or containerisation (don't ask) waiting in the wings. And just behind them are the looming bogeymen of machine learning and the internet of things...

To understand Project Fear – to get a handle on how some marketing tech firms feel so able to harass their customers in this way – you need to follow the money. Despite appearances, this is not a seller's market. There is colossal over-supply in marketing tech and the reasons are structural and come down to one point:

You, the marketer, are not the customer.

Now, again, there are exceptions. Large public businesses like Google, Oracle or Adobe depend for their success on satisfied marketers (in part, at least). But for every one of them there are a thousand marketing tech startups who depend on venture capital funding. VC money works in an entirely different way from marketing revenue. It comes in huge, infrequent waves rather than a steady trickle. It is given, or not, depending on funders' perceptions that a business has fairly rapid growth potential. When your business model is to attract the next big round of VC funding, you need lots of marketers to come on board fast. Marketing spend in this case isn't the big fish – it's bait.

When we understand that, Project Fear makes sense – and the need for change becomes apparent.

As marketers and as the agencies that work with them, we need to start demanding customer service and customer satisfaction. The best technology companies, whose incentives are aligned with our own, will support us in this because they profit when we profit. The rest need to understand that we will not maintain the pattern of scattered, reactive hoarding of technology and data assets that has characterised the last half-decade of marketing analytics and tech.

As an agency we work with clients to help them define their marketing technology, data and measurement strategies. In almost every case we find that there are more tools, more capability and more smart thinking already in place than the business realises. Very often, it's not a case of buying a shiny and intimidating new capability, but of making existing ones work harder and work together. Most digital business transformation happens with software, not because of software.

Saying no to Project Fear means saying yes to a more considered, design-led approach to crafting your technology, effectiveness and data ecosystem. It means embracing the subtler arts of data planning and technology plumbing. Above all it means acknowledging that change comes through teams and partnerships, not bells and whistles.

http://alexsteer.net/233.htmlAlex Steer
http://alexsteer.net/232.html12/02/2016The dangers of data dependency

AdAge contains an article about a 10,000-person advertising research study with one of the least surprising findings imaginable:

The study, which the companies said involved 189 different ad scenarios, found that "viewability is highly related to ad effectiveness"

No, you did not misread that. It took a study of 10,000 people to establish that ads are more effective when you see them.

And in fact, this wasn't really an effectiveness study in any meaningful sense - it was an ad recall study.

So in short, the finding is: You're more likely to remember ads that you've seen than ads that you haven't.

There is such as thing as being too data-driven.

http://alexsteer.net/232.htmlAlex Steer
http://alexsteer.net/231.html07/02/2016Buzz and effectiveness

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.

http://alexsteer.net/231.htmlAlex Steer
http://alexsteer.net/230.html19/09/2015Ad-blocking comes from a measurement problem

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.

http://alexsteer.net/230.htmlAlex Steer