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June 2012
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August 2012

Off The Grid

Paid public holiday

This chart published over on The Atlantic shows how out of kilter the US is in not requiring companies to give their staff paid annual leave. Almost worse than that is the fact that, according to a JetBlue/Harris Interactive study, no less than 57% of Americans ended up not using all the holiday time they were actually given in 2011. Most of them averaged about 70 percent of their time-off left unused. And Americans also work longer hours than just about any advanced country.

But bad as it is, I don't think this is something unique to the US. I've done a bit of work in the Far East this year and people I spoke to out there told me that they not only get comparatively few days holiday, days off sick eat into their entitlement, and most of them seemed to be working ten or more hours a day. Long-hours culture seems to be all around us in the UK as well. Technology blurs the divide between our work and our home lives of-course so even when we're out of the office, we're often not really out of the office. It feels as though work is slowly creeping into an ever-increasing portion of our lives.

The answer is of-course to switch off. But this is easier said than done. As Johnanna wrote on the Undercurrent blog, we've evolved into making use of not just one or two versions of out-of-office, but three:

  1. You are out of the office, but still working – from home, a coffee shop, across an ocean, take your pick;
  2. You are out of the office/on vacation, but still “connected” – regularly checking and responding to emails;
  3. You are out of the office/on vacation, and completely off the grid

Too often, sadly, I think we intend to be 'off-the-grid' and end-up still 'connected'. And my sense is that this is becoming more common.

Running my own business gives me the luxury of setting my own rules, so I count myself as fortunate. I'm about to take a short summer break and I shall be without a connection/reception for most of it. 

I'll be back soon enough but in the meantime, this is my plea for everyone to fight the creep of work and take some real time out. And if you're about to embark on a holiday, choose number 3. You know it makes sense.

Mobile Love

This is fascinating. Eric Barker quotes from Martin Lindstrom's book Brandwashed when he suggests that rather than being addicted to our phones, we're actually in love with them. When our phones go off, fMRI brain scans apparently illustrate a flurry of activation in the brain's insular cortex, which plays a key role in functions usually linked to emotion:

"In short, these participants didn’t demonstrate the classic brain-based signs of addiction to their iPhones. What the sights and sounds of a ringing or vibrating cell phone did reveal, however, was that our study subjects loved their iPhones; their brains responded to the sound of the phones the same way they would respond to their boyfriend, girlfriend, niece, nephew, or family pet. In short, it may not be addiction in the medical sense, but it is true love."

We're used to (or at least should be by now) thinking about mobile devices as highly personal. As well as an instrument of continuous connection, they are a device that carries all of our most important contacts, photos of our kids, our favourite music, and so on. But love? Perhaps when you consider that the mobile is the device through which we connect with all the people who matter most in our lives most commonly this is not as surprising as you might think. But it emphasises again that this is no place for stomping in with your size 9 marketing biker boots.

Small, Frequent, Fine-Grained Interactions

I really liked this post by Graham Oakes over on the Econsultancy blog describing the challenge organisations face in adapting to the 'small, frequent, fine-grained' patterns of interaction that characterise mobile. He talks about how they erode organisational boundaries ("each one chips away at the edge, breaking up the clean line that many organisations like to place between themselves and their external environment") by breaking transactional boundaries and cycles and bringing an informality to interactions. It reminded me of Paul Adams' post from a couple of months back on how the future of advertising will be about many lightweight interactions over time

Undoubtedly, the growing significance of always-on platforms (and owned and earned media) makes this kind of stuff more important. And doubtless this brings new challenges of exactly the kind described by Graham and Paul. I liked Graham's suggested imperatives about devolving power to the edges, giving people the tools and insight they need, and sensible use of automation. Many of these challenges and themes arose in the research I did at the turn of the year into evolving client-side marketing structures and agency digital capability.

But I don't think this means that we've seen the end of 'campaigning' approaches. Bursts of activity that have a beginning, middle, and an end. The real challenge, I think, is how organisations can balance both types of activity and successfully combine small, frequent, ongoing, unplanned and informal interactions with big, bold, brassy, planned bursts of activity. How they might really capitalise on what Matt Locke once called the 'new patterns of culture' - slow, fast, and spiky. There's a whole book to be written on that one. 

On The 'Death' Of Online Advertising


I've lost count of the number of articles I've read over the years declaring the death of online advertising. The reasons cited usually touch at some point on banner blindness, falling click-through-rates (the average CTR having dropped to less than a tenth of 1%), and the uneven distribution of clicks (Comscore's 'Natural Born Clickers' study for example showing that only 8% of Internet users account for 85% of all clicks).

And yet still it grows. The reason, as Ben eloquently puts it, is that "advertising is a game of what you catch, not what you spill". At the rates which advertisers pay for impressions, the results are sufficient enough to still make it worthwhile ("Most advertising never catches anyone. But that doesn't matter, because as in love, marriage, kisses and fishing, all that counts is what you catch").

So (whilst click-through remains a flawed metric in many cases) the game remains one of trying to gain enough attention to keep it that way. The longterm answer to this is highly unlikely (as some seem to think) to be bigger, more intrusive formats. This lesson should have been learnt over a decade ago with the failure of pop-up formats which simply annoyed people to the point of distraction and negatively impacted user experience. 

Instead, data has ridden to the rescue of online advertising. We've seen the increasing automation of trading, and targeting becoming an ever more complex activity based often on data collected through third party or proprietary networks. As an illustration of the importance of data now in the ecosystem, tracking app Ghostery shows me (screenshot above - click to enlarge) that when I visit Business Insider (as a random example) I'm tracked by no less than 30 third party sources. Some of these are social platforms that have developed a distributed presence, some are analytics tools, some are third party network vendors. But of-course this isn't unusual. According to tag management company Tagman, there are an average of 14 vendor third party tags on websites. All of these businesses are tracking you in order to gather valuable data with which they might provide more relevant services and advertising, and so make more revenue. 

This is, perhaps, not entirely a bad thing. As Dave Winer once said: "Advertising will get more and more targeted until it disappears, because perfectly targeted advertising is just information". Unfortunately we seem to be some way off that. A case in point being the annoyance of poorly executed re-targeting ads (after visiting one retailer's website I've been followed around by their advertising for more than six months and still never bought anything from them). Use of data in this way though, means that the return is still good enough and that online advertising will be around for a good while yet.

But this does mean that display advertising is increasingly a game played at scale. You need scale to make data work because you need to tag as many different people as possible and then have as great a chance as possible to catch them again, potentially at a moment of intent. In this game, the more data you have the better. 

This creates a challenge for those who don't have access to sufficient scale of their own - those content producers who rely, at least in part, on revenue from banner impressions served against good quality content and who find that (even if they serve millions of them a month), their inventory will simply never deliver the kind of payback they need. Without a distributed presence, a huge network, or a data play of your own, you become reliant on that provided by third parties, which inevitably dilutes your margins. As media agencies build out proprietary networks and targeting capability, and other data-rich networks enter the game (Facesense anyone?), inventory becomes increasingly commoditised. The real challenge here is that the balance of power has shifted and it will become ever more difficult for content owners to charge premiums based on context and the quality of their editorial environment.


Robocop 3
There was something of a kerfuffle recently when it became public knowledge that travel website Orbitz were recommending different price ranges of hotels based on the user's operating system. Data mining had told them that Mac users typically pay a premium of upto 30% on a night's stay so they were using data to improve content recommendation, and in the process their chances of selling products at premium prices.

Dynamic personalisation of content based on data is nothing new of-course. Amazon does it all the time, generating highly customised pages that pull on your purchase history and other data making it highly unlikely that any two of Amazon's millions of customers will see the same home page when they're logged in (a remarkable thing, if you stop to think about it). Systems that optimise decision-making around merchandising and pricing by modelling the result of individual discounts or promotional changes are also not new. 

But whilst Orbitz may not have been showing different prices for the same goods to different customers, it seems that a growing number of retailers may be doing just that. Using sophisticated software that combines the data they already hold on customers along with location and other cookie data stored on people's browsers, retailers can detect price-sensitivity and adapt content in real-time.

Such 'price-customisation' software uses sophisticated algorithms to identify, for example, when a user may be willing to pay more, or whether they are likely to need an additional pricing incentive to buy. Specialists quoted in this Economist piece on the subject suggest that allocating discounts using price-customisation software can bring more than double the return than offering the same discounts randomly, and that at least six out of the ten biggest US online retailers are now customising prices in some way.

Kevin Slavin, in his brilliant TED talk on how algorithms shape our world (highly recommended if you haven't yet seen it) gives an example of the potential downside in pricing algorithms. Many different merchants (two million of them) use Amazon's platform to sell goods and some are using data-mining software and algorithms similar to that which have been developed to inform trading decisions on the stock market. These algorithms can be set to follow simple rules (e.g. to ensure that prices track, or are always pegged below, those of competitor) or can act in more dynamic ways to adjust prices on the fly in response to changing conditions. As algorithms respond to each other and get locked in loops, says Slavin, anomalies can appear such as the genetics book 'The Making Of A Fly' being inadvertantly offered for sale on Amazon for more than $23 million in 2010. Similarly, algorithmic trading was believed to be responsible in the same year for a short period of huge stock-market volatility which saw the second biggest point swing in the history of the Dow Jones Industrial Average happen in the space of twenty minutes.

Slavin says in the intro to his talk that we need to transition our thinking about contemporary maths from something that we derive and extract from the world to something that actually starts to shape it. Algorithmic curation of content is inevitable. Along with professional and social curation (ovelayed with a layer of self-curation) I believe it will be one of the fundamental determinants for how we discover and consume content in the future. Perhaps we need to start getting used to the fact that algorithmic customisation of pricing is just as inevitable.

Image courtesy