Alex Steer

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Last week I presented to the Market Research Society on using real-time data in crisis management situations. I'm putting it up here in case it's useful to anyone who finds their brand melting down.

As with all my presentations, at least half of it is pictures. So here's a quick rundown of roughly what I said:

This is a presentation about process in a real-time environment. Nobody gives out awards for process. But everybody wishes they had one when they get to their desk and realise their brand has gone from 'fine' to 'critical' overnight.

I'm a strategy director at Fabric. We're a creative optimisation agency backed by WPP - we help brands use their data to deliver creative advantage. So we spend a lot of time helping clients bring their data together, measure what matters, and find simpler ways to consume and share it, in real time (or very close).

We work with over 150 brands in 25 markets, and with some genuinely global clients. Because we're focused on helping clients use their data better, we do a lot of work advising on capabilities as well as measurement - how clients should work with their data more effectively. And a lot of that, these days, is about using data fast, including crisis management.

A real-time media environment - one that's fast-moving and constant, with lots of participants (like social media) - has lots of interesting new ways of putting brands in crisis. And I really do mean brands. Lots of businesses have good crisis management capabilities, but they lie in corporate communications or legal, not with brand teams.

Even great brands can suddenly find themselves in an unfamiliar world of pain. Sometimes it's your fault, sometimes it's not. It's easy to panic when you most need to be calm.

For the first time, data moves (almost) as fast as a crisis does. Good use of data during a crisis requires the discipline of research at the speed of social. No easy task.

Getting it right isn't just about having data, it's about being really, really diligent and organised in how you organise and use it, from the start. That kind of discipline can keep you out of crisis, and help you deal with it maturely and quickly when a crisis happens.

So, five tips for using data effectively in a crisis...

Keep perspective. Know how big a problem is, how fast it's moving, and how big your response needs to be. Know what your blind spots are when it comes to measurement or listening. Use data to stop people from panicking.

Measure from the start. Know what the problem is, how it affects you, how you measure the damage and how you measure your recovery. Do that at the start. Set some key performance indicators and keep everyone focused on today's task.

Sort out your chain of command. It's probably not the same as your normal approvals process - it may need more senior people, but may also need to be shorter to get things done quickly. Know how you're going to communicate with your crisis team, do it consistently, and keep it simple. Know when you'll escalate, and who needs to know what, and how often.

Set stages and gates. Work out which order you need to solve problems in. Set threshold measures that you'll monitor every day, so you know when you've moved from stage one to stage two of a crisis response, etc. (what Churchill called 'the end of the beginning'). Use data to let everyone know how far you've come - and what's left.

Know your exits. Seriously, a crisis can feel like it will last forever, but it does end. Don't get addicted to being in a crisis. (It's easy to do - when you're back's against the wall, every move feels important.) Know when and how you'll move on.

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Yesterday I wrote this post about a book in which I have a chapter, which meant going to this page to get the link. I obviously didn't buy the book because I already have a copy, not that I expect the internet to know this. And behold, today in Facebook's sidebar I see this ad:

Which is sort of fair enough, and sort of not. It got me wondering, how do we reconcile these two truths?

1. Ad retargeting is effective.
2. Ad retargeting is annoying.

As I said, I can't expect the internet to know that I own a book whose page I visited just for the purpose of getting the link. So by the logic that says This person looked up a book, then didn't buy it, it's reasonable to infer They may act when given a second opportunity to look at the same book.

But on the other hand, the same inference is not reasonable. There are many, many reasons why someone may have visited the page for that book and not bought it (disinclination, lack of time, vanity, blue book cover fetishism, etc.). Some of these are more likely than others, but in all cases the outcome is the same: a person had the chance to buy the book, and didn't.

Straight retargeting is the online manifestation of a mindset that says: I heard your first answer, and it was 'no', and I'm going to keep nagging you. That is the equivalent, in offline sales, of you popping into your local bookstore, looking at a book, putting it back on the shelf, leaving... then being phoned several times by the bookseller saying, 'Did you want that book?'

And beyond the world of sales, there's a word for people who don't realise that 'no' means 'no'.

Look, retargeting works well relative to other forms of online display media. But it works best when there's been a genuine signal of intent to purchase, such as adding a product to a basket. The evidence base is patchy, but according to this 2011 study, 71% of online shoppers abandon baskets - but 75% of those come back, typically spending 55% more than direct converters, and the uplift from retargeting 'basket abandoners' within 12 hours is around 15-20%. So a nudge in that critical period reflects a normal behaviour and can be useful. But even in this case, the conversion rate from retargeted display ads to basket abandoners is still only 0.3%.

Now imagine how dismal the uplift will be on retargeting people who have just visited a product page. Yes, it's better - but it's better than something really bad. The reason it's better is because you're applying a segmentation over your ad inventory - albeit a fairly dumb one. People who have looked at a book are certain to be more likely than average to be interested in that book.

A recent (Dec 2013) survey on retargeted ads found that 38% of people found them offputting, in addition to the 46% who ignored them and the 16% who claimed to have been prompted by them. Even if we take this optimistic 16% figure (rather than the 0.3% conversion rate from the SeeWhy study), that means that retargeted ads annoy more than twice as many people as they win over. Not surprising, as 53% in the same survey said they had privacy concerns over retargeted ads.

So why not take the 'stalker' factor out of retargeting? Product-view data gives you a very simple segmentation, if you can be bothered to connect the product back to its category. In this case, all you need to know is that the book I looked at is a book about marketing. Then you can target me with other marketing books. I'll feel a bit less creeped out, and you'll still outperform non-targeted advertising because you won't be serving me books on stuff I don't care about, or marketing books to people with no interest in marketing. You'll also be able to switch tactics - if one book doesn't grab my attention, another one might.

I'd love to see some data on whether this is any more or less effective than same-product retargeting when served to people who have given no intent signal. But it's got to be less annoying.

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This post is a shameless plug for this book, the catchily-named Multichannel Marketing Ecosystems, which may not make the New Year bestseller list, but which does contain a chapter by me and Chris Perry (CEO of Fabric).

Despite sounding a bit science-fiction-y, the book is a collection of essays by people working on the problems associated with trying to plan and execute marketing campaigns that exist in lots of different channels, to varying degrees of breadth and depth, and whose audiences may encounter them in whole, or in part, and in any order.

Our chapter - the alliteratively-titled 'Making money with metrics that matter' - argues that multi-channel marketing requires an approach to metrics which goes beyond simple conversion funnel logic and that brings channel-level analytics more thoroughly into the domain of marketing strategy. A marketing strategy should be clear on the role of each channel, and attach meaningful metrics and goals to each channel (not just a 'bottom line' of brand equity or sales metrics), that do not depend on a channel being encountered at a particular point on a journey. This understanding should be shared by all those accountable for the strategy and not merely by analysts, and information about channel performance should be used to optimise and where necessary re-organise the channel mix. This idea - know what you're trying to do, where, why, whether it's working, and when it's not, why it's not - isn't rocket science but requires a serious and shared commitment to measurable standards of effectiveness from everyone in the marketing mix. That's more challenging, and more rare, than most of us like to admit. For agency types, for example, it means choosing the metrics by which your work will be judged in advance - not waiting to see which ones look best in the wash-up.

The book is edited by Markus Ståhlberg and Ville Maila, and is published by Kogan Page.

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For several weeks, the tech press have been talking about what people who spend a lot of time working on social media data have known for several months: that teenagers are using Facebook less.

The spike of interest in this story was driven by Facebook's recent admission that daily active usage by teens has fallen.

Today's Guardian adds some necessary extra detail to the picture:

Their gradual exodus to messaging apps such as WhatsApp, WeChat and KakaoTalk boils down to Facebook becoming a victim of its own success... Facebook is no longer a place for uninhibited status updates about pub antics, but an obligatory communication tool that younger people maintain because everyone else does. All the fun stuff is happening elsewhere. On their mobiles.

This is exactly what all the recent data I've seen is suggesting. There's no particular destination to which teens are moving away from Facebook, but a long tail (or medium-sized tail, anyway) of mobile messaging apps.

The question is: why are they moving?

And the answer we keep hearing in the press is: privacy. It must be, surely? Teens must be moving because Facebook feels too exposed, now their parents and teachers and would-be employers are on it, right?

That's not what I'm seeing. Though you can always find some vox pops of teens worrying about over-exposure, some of the data points suggest exactly the opposite.

First of all, teens are already skilled at managing their privacy on Facebook. (There's some more detailed anthropological research on this that I'll post when I dig it out...)

Second, one of the big winners from the move away from Facebook is Twitter. Many teens are using Facebook as a short-form messaging app, predominantly using the service to @-message each other rather than to broadcast. A very different behaviour from its other big user base of tech-savvy late-20s and 30-somethings. All of this chat activity happens in public, but this does not seem to deter its teenage users.

So don't assume its privacy. Everything I'm seeing suggests that teens are switching to apps that offer greater focus and speed - that do one thing well, and very fast, on mobile devices. This is corroborated by some recent Pew survey data which finds that teens find Facebook burdensome rather than exciting to use.

In other words: it's not that Facebook is too open. It's that it's too slow.

Facebook is still not optimised for nearly continuous use, which is how a lot of teens communicate with each other. For all that we (poor old grown-ups) talk about always-on social media channels, teens are testing that definition to the limit, and finding that Facebook just isn't always-on enough.

Well, I feel old. And so, more alarmingly, does Facebook's display-driven advertising model.

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I spend a lot of time working with clients to help them understand and apply social media metrics. I've noticed that over the last year the general state of knowledge about metrics, and how to use them, has risen sharply. But there's one question that almost no-one is able to answer.

How many people did my tweets reach?

It bothers me that nobody is talking about this, as Twitter heads for its IPO. Because there is no way of measuring the reach of a tweet - at least, not a normal, non-promoted tweet.

Every piece of evidence I've seen, and every rule-of-thumb calculation I do, suggests that the reach of a tweet is terrifyingly low. And all the metrics that people bandy about - e.g. 'your total followers, plus the total followers of anyone who retweets your tweet' - are transparently nonsense.

Despite all the social media snake-oil that gets passed round, reach is the fundamental metric for measuring the ROI of social media marketing activity. It's the basis by which you judge all your performance ratios, and from which you calculate whether or not you're just wasting your time.

So why is nobody bothered by this?

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I'm working with a client to develop a set of guidelines on a particular topic at the moment. The detail doesn't matter, but the approach does. The challenge set was: how to create guidelines that don't strangle invention and creativity, in an area where the cost of failure is fairly high.

Not easy. I thought about it again as I read about the latest wave of data privacy stories in the British press: from the NSA wiretapping foreign leaders and intercepting Google data centre information, to Tesco trialling face-detection software at petrol station tills to serve targeted advertising, to the new shock-doc about privacy policies.

To most people, paying no more attention than they need to (like all of us), this is all part of one rather sinister, rather seedy story. It goes: everything you do online, you're being spied on. You have no privacy, because big government and big business can do what they want and know everything about you. And you didn't know.

That's just a step away from 'something must be done', and from a legislative crackdown that will do nothing about genuine privacy intrusions but cripple easy targets like online advertising.

If that happened, today, you'd have to admit the advertising industry deserved it. We haven't been vocal in our defence of consumer privacy, haven't been exacting in how we use data, and haven't been creative enough at finding uses for data that are genuinely valuable. Better targeted advertising is not the answer there, because the most targeted advertising is also, at the moment, the worst. So we're just offering intrusive advertising that you can resent in a way that's uniquely tailored to you.

We need to set ourselves some guidelines as an industry that start with creativity and invention as ambitions. To start investing in creative uses of data, and in commitments to the responsible use of the data we collect. But the first move needs to be better data-driven creative work - new services powered by the new information we're asking people for. So that we can legitimately, non-furtively ask people to share data, and make it obvious why they should.

In short: we shouldn't be showing people why data collection is harmless. That's an excuse, not a vindication. We should be showing them what data makes possible. We should be earning data, not leeching it. Which means we'll all have to work a lot harder than retargeted ads and the occasional coupon.

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Applications for the WPP Marketing Fellowship (WPP's leadership development programme) close in a few days, on the 7th November. That means there'll be people putting the finishing touches to their applications right now.

I wrote the tips below three years back, when I was on the Fellowship, and I think they still hold true now that I'm a wizened former Fellow (still very happily employed within the WPP group). The blog this appeared on has long since fallen victim to a dot-com buyout, so I'm reproducing it here. If you're applying, good luck.

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I won't go into all the details about what's in the application form - you can see that for yourself; and anyway, that's not what most of the emails we get are about. Most of them ask, fairly enough, about what's not obvious.

Does the competition for places cause you to break out in a sweat and lose your marbles? Were you under pressure to write something fabulously interesting about your life? Are all the current Fellows former rock stars and Nobel prizewinning scientists? Are the questions really as random as they seem, or is there some sort of Da Vinci-style code that will unlock the doors of international marketing? What are the personal likes and dislikes of the people who read the forms? Coke or Pepsi? Chicken or egg? Who is Keyser Soze?

In response, here's my top form-filling tip.

Relax.

There are no secret codes. No open sesame, no small print, no insider dealing. The application form is exactly as it appears, and the people that read it only have one head each, and very few of them breathe fire. As far as I can tell - and please correct me if I'm wrong - what they're looking for in the application form is an answer to each of the following two questions:

1. Does this person think clearly, and write well, about communication?
2. Can I stand to spend an hour in a room talking to this person?

Some of the sections of the application form are designed to answer the first question, others to answer the second. The questions may be tricky, but they're not designed to trick. They're designed to make you think, and give you the chance to show how you express that thinking, and to let your readers get a first glimpse of the smart, interesting, personable person you are. Really, that's all.

And yes, there will be lots of other applicants also trying to show that they're smart, interesting, personable people. Let them worry about themselves. Worrying about them is a distraction, and it'll only panic you.

Don't tie yourself in knots because you think you have to sound, or think, like a certain type of person. That kind of thing is hard to fake. If you pretend to be someone else, you'll inevitably come off worse, because you have so little experience of being that other person. By contrast, you have so much experience of being yourself. So give that a try. (The other person will probably be a boring marketing stereotype anyway.)

Chances are, if you get a kick out of answering the application form, you might enjoy a career in communications. If not - if you find yourself hating it, or forcing yourself to come up with answers that sound right - listen to your instincts. Don't apply for the Fellowship just because it's a big job with a big firm. Don't click the 'submit' button unless you've enjoyed the first stage. Life's too short to do jobs you know you're not cut out for.

And remember, it's just a job application. Honestly. It's not worth losing sleep over.

So relax. Take a deep breath, have a go at the form, then go out for a long walk, get some sleep, go out with your friends, or whatever. Don't think about it. Do something fun. Then come back to it with a fresh mind, and see if you'd give yourself an hour of your time.

Oh, and make sure you proof-read. Spelling, punctuation and grammar mistakes look bad if you're after a job in communications. (If there are any in this post, well, that's me taking one for the team.)

Good luck. Enjoy.

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I hear a lot about how big data is going to transform the advertising industry. Some of which, for the record, I agree with. I think the things that make big data 'big' - the classic 3 Vs of volume, velocity and variety - make possible new ways of working with information to make decisions in the communications planning and measuring process.

But big data, as a sector of the technology industry, has proved surprisingly bad at advertising.

Some players are spending heavily on media and messaging, of course. You can't move without seeing one of the big IT firms telling you that big data is going to change everything. But much of this messaging commits some common errors that make advertisers and marketers cringe. It's patronising, shouty and assumes a passive audience who, when told 'everything you currently do is rubbish,' say 'oh, okay, let me get my wallet out'. It's afflicted by the kind of 'solutions-based' messaging that is based on logic rather than the evidence of how effective communication actually works.

This combination of naked self-interest and failure to understand the principles of advertising explains, I think, why most big data products for marketers are borderline unusable. They are narcissistic in their design - consumed by their own cleverness and determined to force their users to change their ways of working to accommodate them. Hence why many of them are thinly-disguised excuses for selling consulting services to get them back into the hands of the power users for whom they were designed.

The flexibility of a lot of these tools - the idea that they can do everything - is actually toxic, a symptom of the refusal to do a few things well. A properly designed big data product for marketers should be a product (not a consulting shill) that presents the most important data at the point of need, straightforwardly and memorably. In other words, it should be an effective piece of communication in itself. Great apps should work like great ads, and the salespeople going round proclaiming the death of advertising should bear that in mind.

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Okay, it's been a bit quiet on this blog recently - largely because it's been rather busy with actual work.

But I wanted to share a presentation I gave a few weeks back to the Market Research Society's advertising research summit in London. It's called Three ways to be fearless with big data, and was the last presentation slot after a really impressive and varied day in which some genuinely smart people from across the research industry showed how they were innovating and informing creative advertising. I decided to round off the day by talking about the gap between how people are talking about big data and how people are really using it - and with some concrete proposals about how to use it realistically.

At some point I'll add speakers' notes to this. For now consider this a contribution to Slideshare's ongoing game of modern hieroglyphic detection via PowerPoint...

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Update: Since I wrote this, the merger has collapsed.

Disclosure: I work for a company in which WPP has a financial stake, and am a former WPP Marketing Fellow. All opinions are my own.

As more or less everyone in advertising and marketing now knows, Omnicom and Publicis today announced that they are merging, forming what's set to be the world's largest marketing services holding company.

As the New York Times notes, the CEOs of the two companies paid the customary homage to big data when talking about the benefits they expected such a huge merger to deliver:

“The communication and marketing landscape has undergone dramatic changes in recent years including the exponential development of new media giants, the explosion of Big Data, blurring of the roles of all players and profound changes in consumer behavior,” he [Mr. Lévy] said. “This evolution has created both great challenges and tremendous opportunities for clients. John and I have conceived this merger to benefit our clients by bringing together the most comprehensive offering of analog and digital services.” At the news conference, he expanded on that notion. The “billions of people” who are now online and providing data to companies,” Mr. Lévy said, provide an opportunity to use advertising technologies to “crunch billions of data in order to come with a message which is relevant to a very narrow audience.”

I'd suggest this should be taken with a pinch of salt. To some extent this is the sort of talking-up you'd expect from any company latching on to fashionable industry concepts to enhance its future share price, and good on them for doing that. But it does raise some interesting questions about the relationship between holding companies and big data.

Whose data is it anyway?

It's worth remembering that this data is not - or should not be - a strategic asset for Publicis/Omnicom. The data is collected on behalf of, and should be owned by, its clients.

Clients may find themselves with a bigger menu of marketing services options as a result of this merger. It remains to be seen to what extent these are powered by data, or whether the newly-merged holding group will do a good job of helping clients join data together and break down operational walls so they can make more effective use of their data.

Finally, this will come down to a question of what clients want - and it is likely to be more driven by clients than any holding company wants to admit. At the moment, I see no reason to believe that clients will want to deal direct with holding companies for their data management services. They will expect their agency partners to provide data in common formats, be more open about data sharing and act faster on opportunities found in data; but they may not want their data management platforms provided by their agencies, or even their holding companies. It's good if Publicis/Omnicom adopt and enforce common data openness standards across all their agencies, because it will make it faster and easier to access, integrate and audit. But the agenda will be set by clients, who will want the data provided by those agencies to play nicely with data from all other sources, regardless of source.

The awkward matter of scale

Holding companies have a bad rep. For small agencies, positioning themselves as the 'hot shops' in contrast to the lumbering industry giants is a pretty easy win. There's a nagging doubt - which clients clearly share to some extent, and perhaps rightly - that dealing with a big agency or group is a recipe for cookie-cutter work or grindingly slow bureaucratic ways of working.

Big data has provided a way for holding companies to talk unashamedly about scale. A few years ago, media planning and buying provided a similar opportunity. This time round there was far more talk of data than of media, which implies that data is currently a more fashionable and persuasive way of implying 'value through scale'. This was backed up by plenty of talk of efficiency savings too.

What's missing, for me, in a lot of this rhetoric, is any mention of creative pride in the scale that big data provides. I've yet to hear a holding company come out and say that, yes, having tons of data and making a lot of it can lead to more focused strategies, more interesting creative opportunities, better briefs, fewer things done on a whim, fewer lazy assumptions. I know from experience that some holding companies value these things highly. Mine (WPP) was awarded as most effective holding company at Cannes for the third year in a row. But it would be great to hear holding companies come out and say that being huge, and having better data resources, gives advertisers more scope to be fast, focused, and fearless.

From big data to marketing coordination

Speaking of scale, people say some stupid things about big data. One of the stupidest is, 'Big data's not about how much data you've got, it's about what you do with it.'

No, big data is explicitly about how much data you've got. Of course, the utility value of data is also an important characteristic (duh). But let's not pretend that volume, timeliness and granularity of data aren't also decisive. My view is that if you don't value the scale of your data, you're doing it wrong.

This thoughtless utterance, though, is simply a bad articulation of a very important insight: that at the moment data is typically being used at the wrong points throughout the advertising planning and execution process. Everybody with a bit of common sense in marketing knows this, and could tell you where and when they want to use data more - when identifying specific challenges and opportunities, when taking the temperature of an issue and figuring out a response in real time, when measuring and adapting the performance of creative content mid-stream during a campaign, and when measuring the relationship between short-term activity and long-term brand value and behavioural change.

This stuff is hard, and a lot of it won't be sorted out for several years. But most of the difficulties aren't theoretical ones. There's plenty of chatter about what big data could do - the possibilities, the opportunities, the brilliance of theory. But very few of the pundits are really doing it, and doing it in ways that scale up and let you do it quickly and repeatably and every single day, without going insane.

Doing it well does mean more than having data. But hidden under the banner of 'what you do with it' is, I think, a complete reconfiguration of how marketing works, and how agencies work together. As we all know, clients are demanding more for less - more efficiency, more speed, and more transferability. International clients, in particular, want ideas that travel well, assets that can be localised but that are basically globally consistent. As creative agency people we can tend to rebel against this, because our instinct is to assume that marketing strategy and creative advertising development necessarily sit very close together. And we tend to see all the bits of the industry that are more replicable or scalable - research, media, analytics, production, adaptation - as a bit Luddite-ish.

Unless there's an astonishing creative revolution (which seems unlikely in the current climate), that way of thinking needs to change fast. If clients want fast, replicable, measurable work, then as people who care about long-term effectiveness and creative difference, we need to find ways to give it to them. That means taking a deep breath and bringing together strategy, creative and production in new combinations, working data seamlessly throughout that process, and using technology and information to make sure we can measure short-term effects and predict long-term ones. Call it marketing coordination if you like. And yes, we'll need to find the right balance between creative speed and quality of craft, and know - as ever - where to focus our efforts and battles to fight.

So we can stop talking about what big data could do, and just get on with doing amazing work every day.