Alex Steer

698 words | ~3 min

I find it amazing that people can talk seriously about the idea that big data might spell the end of theory. After a decade in which data analysis failed to provide a credible risk model for the global financial system, you'd think we'd be a bit more circumspect about off-model risk.

But then I'm a strategist by inclination, so I would say that, wouldn't I? I tend to believe that analysis of the available data should be balanced with thought, imagination, speculation. Rather than prolonging the argument, it's worth looking at why certain claims are made about big data, and what they tell us about the competing visions of the future that they represent.

The emerging field of big data is an odd cultural mix at the moment, like the world of high finance was in the 1990s. It has put C.P. Snow's two cultures - in this case, advertising and technology - in the same room to see what happens. As we've seen, that can be a productive but very unstable mix. We need a way to spot each others' assumptions - and our own.

I'm grateful to Andrew Curry for introducing me to Jane Jacobs' model of Traders and Guardians, from her book Systems of Survival. It offers a nice way of thinking about how different mindsets approach the problem of progress (social, technological, etc.). I quote from Mary Ann Glendon's reviewof the book:

Because traders’ prosperity depends on making reliable deals, they set great store by policies that tend to create or reinforce honesty and trust: respect contracts; come to voluntary agreements; shun force; be tolerant and courteous; collaborate easily with strangers. Because producers for trade thrive on improved products and methods they also value inventiveness, and attitudes that foster creativity, such as "dissent for the sake of the task"...

Guardians prize such qualities as discipline, obedience, prowess, respect for tradition and hierarchy, show of strength, ostentation, largesse, and "deception for the sake of the task." The bedrock of guardian systems is loyalty. It not only promotes their common objectives, but it keeps them from preying on one another. They are wary of, even hostile to, trade, for the reason that loyalty and secrets of the group must not be for sale.

Guardians and traders fulfil different roles. Guardians give continuity, protection, standards, quality. Traders give disruption, innovation, growth, opportunity. According to Jacobs, problems arise when the two systems of behaviour get mixed up or imbalanced. When you have too many traders, you end up with the kind of overconfidence that generates instability and risk. When you have too many guardians, you end up with the kind of overconfidence that generates complacency and stagnation.

Big data contains plenty of traders - doing deals, chasing the new new thing. But also plenty of guardians - applying models and processes, promoting standards and best practice. The two need to be kept in balance. Too many traders and we'll end up selling way ahead of our capabilities, cutting corners, disregarding expectations of privacy. Too many guardians and we'll build white elephants that don't move with the times.

Traders tend to incline to the view that big data will kill theory - like all the other 'this changes everything' tech and financial fads of the last twenty years. They stand to be disappointed. Guardians tend to think it won't, that it's a useful accelerator of traditional research and analysis techniques. Both are likely to be surprised, and both need to pay close attention to the other's point of view.

If the guardians win the day, big data will stagnate into a world of heavy-duty IT platforms. If the traders win, we'll find ourselves in a bubble that may burst with damaging consequences. If the two learn to balance each others' demands, they might create something of lasting value.

381 words | ~2 min

By some roundabout route I found myself reading this paper today, by Sarah Kaplan of the Wharton Business School. It introduces the idea that when people get together to set strategy for a business, they end up in a 'framing contest' - a fight between competing individual worldviews, all trying to turn themselves into a consensus:

Frames are the means by which managers make sense of ambiguous information from their environments. Actors each had cognitive frames about the direction the market was taking and about what kinds of solutions would be appropriate. Where frames about a strategic choice were not congruent, actors engaged in highly political framing practices to make their frames resonate and to mobilize action in their favor. Those actors who most skillfully engaged in these practices shaped the frame which prevailed in the organization.

It's a good read, and made me think about startups - especially those in new sectors (like big data).

Startups get a lot of criticism for being willing to sell anything to anyone, often long before their strategy has been defined. And of course for selling slideware, vapourware, Photoshopware, and various other kinds of -ware which mean you don't yet have a product/service/offer and are trying to get your clients to fund your R&D.

Seen through Kaplan's lens, this becomes less a fault than an early-stage survival tactic. Rather than studying the market, setting a strategy, building a roadmap then going to the market to attract clients, startups are outsourcing their framing contests. You could call them 'framing auctions'. Defining a minimum viable strategy, then having a lot of conversations with a lot of different clients and seeing which ones lead to a sale, deal, pilot project, etc. Put this way, the definitional slipperiness of most startups becomes a way of getting prospective clients to take part in your framing contest.

This can cause sleepless nights for the pure-minded, but may be a pretty smart way to start building a brand. There's a bigger question about whether strategic planning works for startups, and even for emerging sectors, or whether a broader-based foresight model is the best way to start preparing for the different ways a market might go.

1569 words | ~8 min

The Wharton Future of Advertising Program at the University of Pennsylvania has begun a study to explore what the advertising industry might look like in 2020. According to its website (my emphasis):

Over 150 thought leaders, innovators, and visionaries from a breadth of disciplines and around the world helped co-create this concept, and Advertising 2020 reflects a mosaic of their insights and ideas. Among those who are in a position to actually make changes seek to understand what is possible, not merely what is inevitable.

I wondered: how diverse a set of predictions did these over 150 industry leaders actually make? And do these long-term forecasts offer more variety than short-term ones?

So, in the spirit of Philip Tetlock, I devised a quick and dirty test.

The data

I used two data sources:

1. The raw text of all the 2020 predictions submitted to the Wharton programme - a total of 39,405 words.
2. The raw text of the 2013 predictions I found at the end of 2012 and recorded in this post - a total of 33,041 words.

These are quite different groups. The first is a carefully chosen group of experts selected for breadth. The second is a semi-randomly chosen group of industry pundits whose blogs I happened to be able to find through an hour or two of Googling.

The hypothesis

I expected the experts to outperform my random pundits for diversity of topics covered. They had been selected for this, after all - and they were being asked to forecast the industry over an eight-year span (to 2020) instead of a one-year span (to end of 2013).

I planned to measure this by a fairly crude measure - the frequency distribution of words (excluding very common words) used in the predictions. In theory, the more diverse the conversation, the more variety there should be in the terms used.

My method (and why it's a bit dodgy)

I'm normally sceptical about the value of word frequency studies, for reasons that this post on the Lousy Linguist blog makes clear. However, there's some value to them for looking at how tightly clustered the topics of discussion in a corpus of texts are. It doesn't tell you much about opinions, differing points of view, etc. But it does tell you whether people are covering the same ground - which as a first pass at an overview of predictions, is pretty important.

It's by no means perfect and there are much better ways of doing it. But it's quick and gives us a starting point. I'd love to see people improve on it.

I took the text of both sets of predictions, and ran them through some basic frequency analysis. I removed punctuation (non-alphanumerics except hyphens), harmonised to lowercase, then excluded numeric-only strings (sorry, but it was creating a lot of low-level noise of little analytical value). Then I excluded items from the Oxford English Corpus's list of the most common words in English: so-called 'stopwords' like 'be', 'and', 'the', etc, which account for around 50% of words in most well-balanced collections ('corpora') of English prose.

I scored the results by frequency to give a list of distinct words (excluding common stopwords) used in each set of predictions:

This raises an initial red flag. The number of different words used in the 2030 predictions is barely different from (and a tiny bit lower than) those used in my semi-random selection of 2013 predictions.

How similar were the 2013 and 2020 predictions?

I started by looking at the top 20 terms used in the 2013 and 2020 predictions. I've marked in bold the ones that appear in both top 20s:

	2013	2020
1	marketing	advertising
2	brands	consumers
3	content	social
4	social	brand
5	data	brands
6	mobile	media
7	consumers	marketing
8	media	consumer
9	brand	advertisers
10	marketers	future
11	become	content
12	companies	digital
13	business	data
14	online	mobile
15	big	need
16	users	ad
17	services	like
18	trend	world
19	been	today
20	digital	technology

There's a 50% overlap. Half of the key terms my pundits used to describe next year's advertising industry were also being used by the Wharton experts to talk about the industry in 2020.

This would be fine if the overlapping words were very long-standing parts of the marketing lexicon. But (by my reading) they are disproportionately the marketing buzzwords of 2012: data, mobile, social, content, etc.

In fact, it's surprising how some very important long-standing industry terms seem to be under-represented in the 2020 predictions. 'Agencies' is in 56th place, 'messages' in 91st place. (Though by comparison, 'agencies' is in 318th place in the 2013 predictions, and 'messages' in 391st! Which suggests 2013 could be a lean year for traditional agency and comms models...)

How varied were the words used in each set of predictions?

We've seen that the 2013 and 2020 predictions cover very similar ground at first glance. But we'd expect the 2020 predictions to have a much longer tail - including more varied discussion with a further out view.

This is basic forecasting logic, by the way. The further out in time you get, the wider the cone of uncertainty and the greater the range of possible futures stretching away from the present. We'd expect longer-term forecasts or scenarios to be more varied.

But this isn't what happens. The chart below is a cumulative frequency count of words in the two sets of predictions. It shows the proportion of the total word count (y-axis) that is made up of the most frequently-used words (x-axis).

Both sets of predictions follow a classic power law distribution, even once you've removed the common 'stopwords', as we have here. A handful of extremely common 'marketing-speak' words dominate the discussion. This is a measure of the variety of the language used in the predictions.

But what's most interesting is that the 2020 predictions are less varied than the 2013 ones. Both are very low on variety - 10% of the text (excluding stopwords) consists of 23 marketing keywords in the 2013 predictions. But in the 2020 predictions, 10% of the text is accounted for by just 16 words - an average of two words in every sentence from the table above.

	2013	2020
Number of marketing terms making up 10% of total prediction text (excluding stopwords)	23	16

So what?

Word frequency analysis is a pretty crude measure. But from this initial look, I don't see any reason to believe that the broad panel of experts gave more varied or more forward-looking forecasts than the pundits doing their end-of-year run-down. They seem just as prone to the kind of buzzword bingo that we rather enjoy in the New Year predictions race.

Something more than my dodgy word frequency analysis is needed. But for now it seems that there may be too much consensus about what the future of advertising looks like. And that kind of consensus, backed up by heavy spending, has all the makings of a bubble.

1026 words | ~5 min

Once upon a time, there were full-service ad agencies. They didn't call themselves that, of course. They just called themselves ad agencies. But they did all sorts of things like media and research, as well as creative work.

Then one day - okay, one decade - they gave away those 'marketing services' functions. Media and market research went off to become entirely separate industries. The creative agency function remained boutique and special. Media and research went off and chased scale and efficiency.

At around that time, the people who did marketing realised that the web was going to be a bit useful, maybe. And the kids who made stuff for the internet realised that marketers would pay them. The kids set up their own companies to build internet stuff for marketers, and the marketers - who were getting used to paying separate agencies to do creative, media and research - were more or less happy to ad these 'digital' agencies to their lists. They never expected the internet kids to join ad agencies.

So we ended up with a world in which marketing services companies and creative agencies found themselves competing for the time, attention and love of marketers. For a long time creative agencies got most of the love - they did powerful, beautiful, interesting work; the marketing services firms were the useful, efficient drudges. As for the internet kids, they occupied an odd middle ground. Some looked more like marketing services firms, others acted more like ad agencies. Anyway, we broadly ended up with two cultures:  one tidy, scalable and efficient; one elegant, interesting, deeply connected to what people were thinking and feeling.

I think we all know how this fairy tale ends.

One day, the economy fell apart. Marketers tended to look at the slightly devil-may-care attitudes of the agencies and got a bit nervous. Big bets, leaps of faith... these things started to look dangerous rather than daring. Over on the other side, the media and research firms were testing, measuring, proving. That suddenly looked a lot more appealing.

So the marketing services people started to take the upper hand - more love, more attention, more say in how money was spent. The agencies found themselves backed into a trendily-designed corner. And as this happened, the marketing services firms realised they had a secret weapon.

The internet kids, stuck in the middle.

Technology and data could make things more robust, more measurable, easier to prove and improve. And in the meantime, the internet kids had grown up. The ones who hadn't been acting like agencies had got suits and a bit of money. They'd become technology consulting firms, and some of them had been absorbed into the big professional services firms who had moved a bit faster than some of the marketing services guys.

So the marketing services firms started doing deals, partnering with the tech consultancies, and hiring people in. After a while the line between media companies and technology companies (and even to some extent research companies) began to blur. Media became technology-driven. But more than that, it became highly technical, full of really good models of what worked, and some really smart ways of building pictures of the audiences for marketing: who they were, where they want, what they did, what they liked.

And here we are. The marketers are spending a lot of time talking to the marketing services firms (the media guys, the tech guys, the research guys), because those guys talk sense. As for the agencies, they've hired enough of the internet kids that they can talk a good game about social media and built some whizzy applications that other agencies love. But it all still feels a little bit... precious.

Not surprising, then, that you hear a lot of people saying that the marketing services firms have won the battle for love and attention. They've got the money, the evidence, the scale, the buzz...

Just one problem with that story.

The agencies still understand how to move people. And the marketing services firms don't.

If you're a marketer, the experience of working with marketing services firms on consumer marketing problems is, by and large, horrific. They've got the tech, the metrics, the models, the smarts, the money... But do one in a hundred of them have the sheer creative problem-solving ability that you need to build a strong brand, change a perception, change a behaviour?

The agencies still have it, and have it in spades. The best planners, the best creatives, are still in agencies, creating ideas that motivate people and drive growth.

And that means agencies can still win.

That's 'can', not 'will'. In the short term there's a very good chance that agencies will lose. If they keep ignoring technology, they'll lose. If they keep playing fast and loose with data, they'll lose. If they keep only using the web to be interesting and fancy, without finding ways to become the experts in what kinds of digital activity drive changes in behaviour, they'll lose. And if the marketing services firms invest hard in creating environments that smart strategists and brilliant creatives can do great work in, the stand-alone creative agency model is dead.

But that hasn't happened yet, whatever the promotional literature says. So if agencies become the experts in effectiveness again, they'll win. If they become serious strategic business partners for their clients, they'll win. If they become the specialists in building the right technology and media mix to generate change and growth, not just reach and frequency, they'll win.

The winners will be the businesses where the smartest people want to work, proving they're solving the most interesting problems in the most powerful ways. Those people will be writers, artists, editors, designers, strategists, project managers, engineers, statisticians, planners, buyers, researchers... Together. Because I don't believe marketers want to spend their time persuading different types of people to work together without fighting. They want organisations that care about relationships and results in equal measure.

Less pitching. Less bitching. More work.

1257 words | ~6 min

As we get into the New Year, I seem to have read about a hundred blog posts that all say the same thing. There's lots of hype about big data - but just having data isn't enough; it's what you do with it that counts.

To which my reply is: yes, well, obviously. That would also be true of any marketing asset, from a website to a tracking research study to some media space. Merely noting that people are excited about something, and some people will use it badly, is a lazy way to try to sound smart.

So I'm going to sound perverse instead. I think, despite all these blog posts, that big data is being undersold to marketers. Here's why.

There's a gap between the hype and the sales pitch

There's a difference between hype and sales. Hype is a precursor to sales, but sales are the closer. Having lots of people excited about something (be it big data, subprime mortgages or Dutch tulips) is a precursor to making a killing when you go round and persuade them to pay for it.

But whereas sellers of snake-oil or South Sea shares tend to be more excited than the wary customers to whom they're flogging their wares, the people selling big data services - getting the SOWs signed, agreeing the terms of contract, receiving the purchase orders - seem curiously less enthused by its potential than their would-be clients.

There's an odd gap, in other words, between the hype surrounding big data, and the things big data providers are selling.

The dismal quadrant

There are a lot of technology companies springing up to serve the marketing industry, as this diagram from the Chief Marketing Technologist blog shows. But broadly, I find you can cluster them into four big blocks, based on the answers to two questions:

1. Do they sell raw data, or services informed by data?
2. Do they promise to improve the efficiency or effectiveness of marketing activity?

Based on that, the landscape breaks down something like this:

Three of these quadrants are pretty straightforward.

1. Greater efficiency for raw data. The world of data management platforms and cloud-based storage.
2. Greater efficiency for digital services. The world of digital delivery and asset management platforms.
3. Greater effectiveness from raw data. The world of syndicated research and heavy-duty analytics.

All three are sold quite well by the people who sell them. But when marketers get excited about big data, they're not talking about any of these three. They're talking about the promise of the fourth quadrant: Greater effectiveness from digital services. To put it another way: Can data-driven services make my digital marketing better?

And by 'better' they don't mean more efficient. They mean more interesting, more powerful, more pulse-racingly good. The kind of 'better' that wins awards and makes clients famous and changes people's minds for the long term. The kind of 'better' that makes good people want to work in this industry in the first place.

That's what they're asking us for. And our answer?

Targeting.

Thomas Carlyle called economics 'the dismal science'. Targeting may be its successor - accurate, useful, but not exciting. Clients want services that realise the hype around big data - full of surprising truths, powerful insights, previously unseen revelations and ways of working that are completely unlike the often frustrating reality of trying to look after the brands by which large businesses grow and thrive. And we give them the ability to swap one banner ad for another based on some fairly simple rules.

Putting the 'big' back

We're selling big data with small ambitions, and that needs to change. In large part, it happens because the people rushing into the sector aren't marketers. To steal from Tom Morton's outstanding recent presentation, many of them 'haven't a brand strategy or creative bone in their body'.

This is the territory that agencies should be occupying, and for the most part they're stubbornly refusing to. When was the last time you heard an agency describe its main aim as making digital communications with more impact, instead of some dismal old rubbish about conversations? Instead of competing on unachievable claims about creativity - which, let's face it, should be table stakes for a creative agency - agencies should be running headlong towards this empty, underserved but incredibly important quadrant. Instead, it's populated with companies that think that marketing effectiveness boils down to nothing more than optimisation after the fact.

It's time to stop acting as if every call for greater focus on effectiveness is an attempt to hammer nails into the coffin of creativity. It isn't, because the two go hand-in-hand and you can't be serious about one without the other. But clients need to be demanding to work with partners that are equally serious about both: not agencies that go 'la-la-la' every time questions of analytics and scalability are mentioned; and not tweakers-at-the-margins who see the content of marketing creations as largely irrelevant.

And it's time to reassert the distinction between strategy and tactics. As I review the kinds of supposedly strategic advice a lot of clients are getting from the technology sector, I see little more than a set of tactics backed up by case studies of their previous effectiveness, with little reference to the context in which those effects were generated. That's the model-based way of selling technology consulting services at its worst: use technology, ???, profit. The best strategic organisations - be they consultancies, ad agencies or tech companies - know that strategy is based on a need and a theory of change, and leads to a set of actions that make a business's marketing output make more sense - sense for the business, and sense to its would-be customers.

So in short, yes, I think we are underselling big data by using that term to describe some very small, tactical solutions to problems that we're not defining well enough. Clients should be asking partners to use big data to solve big, specific, thorny problems, and not be satisfied with any solution that isn't both empirically grounded and rooted in a proper understanding of behaviour.

It may be that we need to rip up some organisational models to do this - the distinction between ad agency and tech company, for example. That won't be easy, but it will be worth it, and it's what clients should be asking for this year.

211 words | ~1 min

Okay, bit of a plug, but I wanted to say a huge 'well done' to Fabric and our clients at KFC for picking up a gold Cristal Festival award for best use of data in a campaign (It Doesn't Count If...).

Here's our case study. I'm biased, but I think it's a great example of what happens when you get a really true insight, a brave client and an agency that's committed  to using data to do what's right, not just what's easy to measure. It also offers a look under the hood at the kinds of tools we build for our clients to help them use data to do amazing work.

If you want to use your data to do more magic instead of more maths, get in touch: hello@fabricww.com.

Fabric Worldwide/KFC: 'It Doesn't Count If' - Data case study from Fabric Worldwide on Vimeo.

542 words | ~3 min

It's December, which means everyone in the advertising, marketing and technology world is publishing lists of '2013 trends and predictions'. These vary, as usual, from the insightful to the deeply suspect.

Instead of adding to the pile (yet), I've decided to start making a list of all these lists, with the aim of doing a bit of a meta-analysis later on. If you know of any others, leave a comment or email me and I'll add it.

So, in no particular order:

• Millward Brown
• Landor
• The Futures Company
• Contagious
• PSFK
• Fast Company (tech)
• Fast Company (digital services)
• Trendwatching
• JWT
• Mashable
• Branding Strategy Insider
• Hootsuite (via, you guessed it, Fast Company)
• Digitas
• Acquisio
• Sense Networks
• Business Zone
• Content Marketing Institute
• HubSpot
• ExactTarget
• B2B Marketing Insider
• Gartner
• Social Media Today (several lists in one)
• IDG Connect
• ReadWrite

318 words | ~2 min

Just seen this for the first time. This is the US version (same creative, different voiceover artist):

I love the opening line - we're the people with the smile on the box - and the thought behind it. It's got a charm and humility that throws you, and changes how you feel about a corporation that most of us think of as giant and reliable mail-order service - from ubiquitous to familiar.

Sadly, what I love less is the thought that they've chosen to govern the whole ad: We're reinventing normal, again.

See, if you're going up against Apple, don't do what they did.

But worse, it's a tone that even Apple shouldn't have taken, the first time round. It said: we're massive, everything we do is epic, and all our new products are ipso facto revolutionary.

In other words, all the things we secretly suspect Apple thinks about itself. The brand's worst side.

The switch in the Amazon ad takes us from the best side of the brand to the worst side of pretty much every big company, and the worst side of advertising. It's pushy, arrogant and entitled. Here's a new thing, it says. We expect you to respect it.

Go with the first thought, Amazon. It's the best by a mile.

If you needed proof, here's the Post Office's new spot. It's a bit weepy and worthy, but the thought - part of everyone's story - is a cracker.

577 words | ~3 min

Today I remembered something I'd forgotten about the early years of Facebook. The rule that status updates had to start with the word 'is'.

This was phased out in November 2007, around the time that Facebook reached 50 million users. It was a huge win for thousands of users who had signed petitions to ditch the verb, and who (rightly) saw it as unnecessarily restrictive, a hanger-on from an early assumption that statuses were for broadcasting where you were, or what you were doing. Rather than, say, your thoughts or feelings. (Is it a cheap shot to make a joke about the inner lives of software engineers? Probably.)

I'd forgotten all about it. And when I remembered, I did a quick bit of calculating. Facebook has 955 million users (FB stats, 30 Sept 2012). That means that 95% of Facebook users don't remember the 'is'.

That means they were never burdened with the 'is' constraint. But it also means they don't remember the bizarre sub-genre of deliberately grammatically mangled status updates that it spawned. The Wired post nodded to this at the time:

Many people ignore it, choosing instead to commit grammatical atrocities such as "Sarah is likes to dance."

That's a rather tame example. There was some rare brilliance there - from 'James is pub' to 'Jenny is WHY DISTRICT LINE WHY???' The is-busting was deliberate and perverse and gleeful. Like putting stupid things in your 'Interests' and 'Political Beliefs' (before Facebook made it harder to enter free text in these fields), it was a way of playing with the conventions of the platform.

In that sense the loss of the 'is' feels like a slight shame, because it destroyed a kind of creativity that thrived under the constraint. But then, that's the same argument that turns minority cultures into museum pieces, or that insists that dying languages should be kept on life-support. So do I wish we still had to start our status updates with 'is'? No, but I think we need a better way of recording some of these fleeting online social phenomena - given that by definition digital activity should be recordable. Just as field linguistics has arisen in response to the need to keep records of dying languages and their stores of knowledge and cultural practice, without insisting that we should all still be speaking those languages, so it's worth having a way of capturing these behaviours before we move on, without insisting that we don't move on.

There's more to say on this, but the tricky part is finding the resources and will to do it. The actors involved in studying and responding to trends as they emerge (businesses, creatives, etc.) are not the same as the ones needed to step in as they decline (historians, archivists, anthropologists). And the incentives are, of course, very different. You'd struggle to make a living advising people on dying trends - or telling them to keep everything in case it comes in useful one day. But since I suspect the need for long-term storage is going to become a sore point in the adoption of big data technology, the relationship between innovation and archiving is going to need to be worked out.

385 words | ~2 min

I enjoyed this from Dave Trott on lateral thinking, but it shows the dangers of getting your sums wrong when you're trying to prove the effectiveness of advertising.

The post tells the story of how Play-Doh went from being a generic wallpaper-cleaning putty to a branded children's toy. It signs off (my emphasis):

In the years since, Play-Doh has sold over 2 BILLION cans. Even now, every year it sells 100 million cans in 75 countries. The original wallpaper-cleaning putty sold for 34 cents a can. Marketed as Play-Doh, the virtually identical product sells for $1.50 a can. That’s an extra $1.16 a can (a 300% increase) that can’t be attributed to anything but marketing and advertising

Yep. Except inflation, of course.

There's a good historical price inflation calculator here. Play-Doh went on sale under that name in 1956 - the latest possible date (and the most generous) to which we can assign the 34 cents price for the wallpaper putty.

If a can of wallpaper putty cost 34 cents in 1956, then we'd expect the equivalent product to cost around $2.70 today. If Play-Doh sells for $1.50 a can (actually a bit less, as three tubs cost $2.99 on Hasbro's website), then it's lost about 44% of its value.

That's not a surprise if you think about it. Wallpaper-cleaning putty was a much-needed household product, and Play-Doh is an inexpensive children's toy. Admittedly, you could be more optimistic about the value of Play-Doh if you added up all the sales of the product in the years since we all stopped needing wallpaper-cleaning putty. In that sense the brand has probably netted its makers millions - but then you'd have to compare that to sales of other children's modelling clay brands, and ideally make sure you were comparing like for like in terms of output, distribution, and other thrilling things like that.

Once you've done that, it's probably still worth a ton of money as a brand. But demonstrating that means proving it to non-believers (finance people, not ad-men), and that means getting your sums right.

Maths, eh?