Revisiting the future of social networks

(Long read: 1,400 words.)

Social networking may be ones of those areas where change is the only constant, but over the last few months there have been more reasons that normal to think about how it has changed, and where it is heading.

Facebook, now a 1.2 billion user network, recently had its tenth anniversary and invited users to create ‘lookback’ videos showcasing the time they’d spent on the platform. At around the same time, an earnings call showed declining (though still dominant) usage among teens. Not long after, it acquired WhatsApp, a single-minded messaging app whose founders proclaim, in a sign taped to their desk, ‘No ads! No games! No gimmicks!’ But WhatsApp’s new owner has spent the last two years commercialising its platform in the wake of its IPO, making it near impossible for brands to reach audiences without paying for ads. Facebook and Twitter remain the big-league networks but, with 240 million monthly active users (and its own recent IPO), Twitter has been almost caught up by Instagram (with 200m), and overtaken by the professional network LinkedIn (with 259m+).

So what do we make of all this, except to whistle ‘The times they are a-changin’?

In 2011, working at The Futures Company, I set out to apply a scenario planning approach to the noisy world of social networking, in a report called Status Update. Starting with the idea that user preference would be the the long-term critical success factor for any network that monetised its users, we mapped the user choices that seemed most uncertain at the time – those which could go either way.

We identified six. In honour of the term used to describe fundamental changes of direction within tech startups, we called them Pivot Points and build plausible future scenarios for social networking from the trade-offs they implies. They were:

  1. Scale. ‘Big Net’ futures where users prefer large networks; ‘Tight Knit’ futures where they prefer intimate ones.
  2. Privacy. ‘Open Hand’ futures where users willingly disclose personal data; ‘Closed Fist’ futures where they prefer to remain anonymous and control their data.
  3. Focus. ‘One for All’ futures where a few multi-functional networks win; ‘One for Each’ futures where many functionally specific ones are used.
  4. Time spent. ‘Turn On’ futures where users prefer to be always connected; ‘Tune Out’ futures where usage is occasional.
  5. Utility. ‘Plug’ futures where social networks are utilities; ‘Play’ futures where they are entertainments.
  6. Worldview. ‘Challenge’ networks where users are exposed to many differing perspective; vs ‘Confirm’ networks dominated by the reassuring and familiar.

Three years on, how did these uncertainties turn out? Which are resolved, which are still open – and what’s new?

Looking back – Pivot Points in review

The scale question is largely resolved – users still incline towards big, popular networks. The dip in Facebook’s use among teens is a slight counterfactual to this, but this is more motivated by the ‘focus’ and ‘utility’ drivers than by scale or privacy, as the excellent recent Pew research demonstrates. Teens are using Facebook less because maintaining a presence there and checking up on friends is too burdensome vs the quick clean interactions enabled by Twitter, Instagram, Snapchat etc. They are less concerned about privacy than they were a few years ago, because they feel better able to manage it. Scale of networks is not a significant factor. In general, the promise of small, intimate networks was not fulfilled – remember Color, Path or Diaspora? (Thought not.)

The privacy debate has moved on significantly. On the one hand, there is high expressed concern around third-party access to personal data held in networks – by spies and insurers more than advertisers. Yet behaviour around the broadcasting of personal information has become more permissive, with the growth of networks that depend more on public broadcast models and less on intimate sharing with friends (e.g. Instagram, Tumblr, Twitter, or YouTube as a subscriber model for video blogging). As noted above, we’ve got better at managing and negotiating online privacy – a trend mapped by the excellent work of Danah Boyd and Alice Warwick. We’re worried less about our bosses seeing last night’s photos, more about spooks putting together metadata profiles about us that we can neither argue with nor control.

The areas of focus and utility have seen some dramatic changes and to some extent converged, as the WhatsApp acquisition exemplifies. Younger users, in particular, have rushed in the direction of networks that do one thing, quickly and well. Twitter and Instagram have also been beneficiaries here. This is driven, though, by a desire for functional efficiency, not for conceptual leanness – and this in turn is likely prompted by the expectations of users who increasingly use mobile devices as their preferred means of access. So Facebook has done well by splitting its user experience into different mobile apps (e.g. Messenger, Paper), while Twitter and WhatsApp have thrived by avoiding distracting bloat – and Twitter has aggressively killed off third-party apps, stripping the user experience back to basics and re-engineering its platform to optimise for speed. Even Facebook on desktop has focused down on the news feed. And LinkedIn has thrived by being all about business networking (stalking in a suit and tie).

The time spent debate is, similarly, largely over. As mobile becomes the dominant way of interacting, so always-on becomes the expectation. The idea of the ‘digital detox’ is more talked about than done. As seen above, this has become a contextual driver – the idea of very regular connection is so embedded that it conditions the shift towards more focused networking applications. There are, though, probably some niche opportunities for the app equivalent of the ‘slow food’ movement, targeting jaded thirtysomethings (ahem) who have run out of things to say, who notice their friends have too, and who may want a less persistent, more occasional relationship with their networks. This will remain a counter-trend, though.

Lastly, worldview. Despite the early promise of networks like Quora, we prefer to be in the company of the like-minded, to the point that Facebook has had to act to squash the runaway virality of ‘social news’ sites like Upworthy which trade on human interest stories and other ‘link-bait’, in favour of giving breathing room to more ‘serious’ organs of the press.

Looking forward – revised Pivot Points

So thinking ahead over the next few years, what would we carry forward? I’d suggest that the issues of scaleprivacy and focus will remain relevant, but I’d briefly reframe the uncertainties as follows, and add a fourth which is new.

  1. Utility. Will we want to carry our identity and preferences across networks (‘Passport’ futures) or keep them separate (‘Padlock’ futures)?
  2. Privacy. Will we want tighter controls over third-party data access (‘Speak No Evil’ futures), or limits on what networks know in the first place (‘See No Evil’ futures)?
  3. Focus. Will we want networking applications to be provided by a few big companies (‘Big House’ futures) or by many small ones (‘Small Holding’ futures)?
  4. Discovery. Will we want to discover content based on the people we know (‘Connected’ futures) or the things we’re interested in (‘Curated’ futures)?

The fourth one is a genuinely new addition because, even in 2011, the use of social networks as channels for large-scale discovery of news, information, entertainment etc. (broadly, ‘content’) was in its infancy. Since then it has exploded, but its terms have changed. Facebook, in particular, has switched its focus from the social graph (whom you know) to the interest graph (what you like) as a way of serving content and monetising users. This has been a largely unstated change and is generating a backlash from users, as this recent wildly popular post shows. But even while users are demanding their social graph back, they are making more use of interest-based networks (e.g. YouTube subscriber channels, Instagram, Tumblr among teens, Pinterest among young women). In either case, networks will need to be straight about the grounds on which they enable networking and discovery.

As before, these are not predictions, just signposts. These new Pivot Points are more commercial in orientation than before – more about business models and ownership of data, less about the specifics of features provided. Assuming that doesn’t just reflect my interests (maybe), I think it indicates the growing maturity of the category, and users’ growing awareness of the need to come to an accommodation with what are, after all, businesses trying to make money out of them. If these readings are valid, then even while social networking services become a more established part of everyday life, the business environment could get tougher for those who provide them.

Thanks to Andrew Curry for the invitation/prod to revisit the Pivot Points.

Using real-time data in a crisis

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.

Making ad retargeting less annoying

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.

Plug: Multichannel Marketing Ecosystems

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.

Why teens are leaving Facebook (it’s not privacy)

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.

Why is no-one talking about reach on Twitter?

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?

We need to earn data

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.

WPP Marketing Fellowship: Advice for applicants

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.

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.


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.

Big data has an advertising problem

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.

Three ways to be fearless with big data

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…