Five Ideas That Matter

This from Robin Murray at the Young Foundation is an interesting set of claims around the new era of production where we are the designers and makers, the uber prosumer.

There’s a lot of truth in the argument and it goes further – we will produce energy for the grid, and food in modest quantities, provide more deeply formed local services,  earn revenues from our contributions to prosumer activity, and we will exchange in our virtual marketplaces, where our identities will be deeply validated by such measures as our energy consumption. What will be the role of the public sector locally – not to create jobs but to enable this transformation, and to ensure that the economy is configured for local needs even as it plugs into global networks. Managing this balance!

This is an area that needs adding to the innovation literature. Innovation literature is a little overloaded these days. Going back a decade and more Michael Porter began the trend towards innovation clusters. It is quite amazing how European policy makers pounced on clusters as a strategic tool. Richard Florida’s work on creative cities has given the idea of the cluster a second wind.

And yet…. we all know that economic activity is fundamentally less place based than it was. Innovation has to be so too – it has to be more virtual and it has to break out of the confines of clusters. I’ve been writing about that over on Innovation Management. Be interested in your views.

Marketers tink of sentiment analysis as a kind of golden goal, something that wins over and above all other action, all things being equal, at the end of the day.

In the global attitudes project we’ve been looking closely at sentiment as expressed across a range of concepts, by people on all kinds of social sites. We’ve yet to find an area where a truly positive or negative polarity exists in a way that adds meaning to an analysis other than in product reviews – which is where early theorizing around sentiment analysis took place.

The problem of sentiment analysis has generally been something like this – people create double negatives such as not unkind, to mean a positive. And then there is the general sense that people are ambiguous in how they express what they mean.

My problem with sentiment analysis is neither of these. It’s the fact that companies need to connect with people in different ways rather than through a scaled up focus group or opinion survey; and that people are extremely granular in their sentiments. Companies need to know people’s ideas and beliefs at the granular level not at a polarised level.

So software that claims to detect sentiment and marketers that want sentiment measures, I believe, are looking for a simple metric when what they really need is a decent analysis. The CIPR are discussing the issue here.

Of course it is possible to say in general people are negative or positive about my brand but in general this has value when it is referring to a breakout – ie when people are suddenly turning negative. In general, again, people are positive about brands, otherwise a brand would have no value.

If there is a sudden breakout against a brand then in general there’s a clear reason for it. The Australian site TheAge, a couple of years back, helpfully produced a top ten of PR disasters.  A brand or organization can be caught hopping but the reason won’t be too hard to find.

When people express sentiment it is simultaneously expressed in many contexts. For example in the context of their religious beliefs, their political beliefs, and last nights chance meetings, or too many drinks and so on.

Where brands should focus is on their strategy. If it is a good social strategy then it will be trying to conenct with people in many of their significant contexts.

It too is bound to have many components.That complexity is what can be analyzed and measured.

Though brands want to think of themselves as homogeneous they are trying to achieve on many fronts. For example they might want to be creating more of a sense of community, convincing people they represent excellence in service or product quality, are unfaltering in their commitment to environmental improvement, or good fun to be around etc etc.

Contrary often to their own beliefs, they have these multiple aspects. Great brands are beginning to realise that and exploit it. Take BMW, which is now the “ultimate driving machine“, the “best engineered environmental solution” in auto manufacturing, a “family’s” favourite car, an “executive” saloon, a “fleet car”, a pathway to “joy” and at the same time pretty ruthless in drawing people into a struggle to display social mobility as they move through the 3, 5 and 7 class (or divert to the Z, M or 6 series), or sidestep to a Mini.

It is in this sophisticated world where brands are balancing a myriad of messages that social media analysis is truly useful. Because it is a source of volunteered personal information I believe it has far more value than an opinion survey. But it really has to be matched to more complex strategies than simple positive negative sentiment. Before the analysis becomes useful, the strategy has to change.

My Sunday mornings are currently spent catching  up on John Hagel and John Seely Brown’s Shift Index work. Though this concept is not directly in their work I would like to air it – the ambivalent purpose of social networks in business. To all the people I am linked to in LinkedIn, at least for a while, I used to say, so how can we help each other? How can I help you? What will you do for me? I never got a constructive response to those questions.

Here is why organisations are better than networks.

First, in passing this morning I read JSB and JH’s comments on the large organisation:

Only 20% of people are passionate about their work, and the least passionate are likely to be working in large organisations.

The fear is large organisations end up staffed by people who really don’t care.

My feeling about large organisations is their future lies in being platforms that organise people in any way that leads to sustainable revenue. JSB and JH see it slightly differently:

“We believe big institutions will become more relevant than ever–once they focus not just on efficiency but on providing platforms for individuals to systematically experiment, learn, and innovate. As scalable learning replaces scalable efficiency big institutions will become more appealing to talented individuals.”

I like the idealism in that statement but as a knowledge worker I educate myself, by and large. It gives me advantages to do it that way. And I am increasingly inclined towards greater degrees of individualism.

And yet, I like organisations. I like them for this reason:

“….the second reason we believe that large-scale corporations will remain a prominent feature of our professional landscape: because they will be best positioned to develop and support scalable, long-term, trust-based relationships. Think about it. Even the most accomplished networker supported by social networks like Facebook can develop only a limited number of trust-based relationships. On the other hand, a large institution could scale these kinds of relationships far more rapidly and broadly than any individual could.”

Yes, this is what makes me an organisation man. The fact that they provide a short cut to trusted relationships. And relationships where a revenue purpose might emerge without ambivalence. Ambivalent purpose is one of the big snags with social networks like LinkedIn and Facebook – really guys I want to be linked to you not because I admire that profile but for business purposes and that is both enough and worth while.

Search tools are not infallible, as anyone can testify who has taken content metrics sourced from Google, Technorati or their peers at face value, and been burnt. Here is some evidence, from a recent internal research project.

Chart 1 plots the number of results reported by Google Blog Search in a simple search string, banded by month using the advanced search interface.

Chart 2 plots the same results if you throw away the number of results reported by Google, and count actual results by hand.

There is a critical issue here for metatrend analysis – the old business cliche of garbage in, garbage out applies: if data are junk, no valuable insights will be created. Researchers working within social media need to understand the limitations of the tools that they use, and incorporate an effective validation process into their workflow.

A Metatrend method paper is in draft form, so more on the implications of accuracy and reliability in tool selection later.

Behavioural economics is the dismal science with people brought back in. I think it tells us that the more perfect a market, the easier it is to see human irrationality at work. The web is a perfect information market, full of glitches of course, but one that REALLY shows human irrationality and its consequences.

The definition of a perfect market would be something along the lines of information being freely and equally available in the same timely manner to all participants. Sounds like the web.

This is one of the reasons why we think the web acts in a bull and bear kind of way that stock markets also do. And why we think marketing has to think less rationally and change its expectations.

My vote for the most irrational market behaviour of people is the Apple iPhone. Apple dominates the market in information about mobile handsets, and specifically that of smart phones.

First a piece of external data from Hitwise: iPhone almost immediately outstripped interest in the iPod just about from the minute it was launched.

Then there was consolidation of that interest until by late 2008 the iPhone was unassailable in the market for smart phone information. We studied the iPhone and Android, the Google-led open source operating system for mobile phones, from December 2008 to March 2009 to compare the two, to understand the web as an information market for handsets.

If you want to get on top of the very latest in the iPhone bull market turning bear take a look at this post of yesterday from Michael Arrington at Techcrunch. Om Malik is also an iPhone sell advocate. Interesting point is both have lived with serious iPhone negatives for two years and chose not to write or mention this. And the sell recommendation is not based in performance negatives. It is based on new contract conditions that Apple and AT+T want to impose.

So let’s turn the clock back. During our study period there was no major iPhone initiative or announcement. There was however the announcement of the first Android handset, the HTC G1.

Under normal circumstances Android, with the infinite reach of Google, and HTC should have dominated the information market for mobile handsets during this period.

What happened? During that time iPhone garnered 12 times more information flow than the G1 and Android combined.

The normal way to look at this is to say the iPhone gets stronger information flow because it is more popular. But we don’t believe the web works that way.

Take Facebook as a parallel. There is little in the way of competition to Facebook. It has define a service that is similar on concept to many other social networks but no-one else tops 200 million users. There is a bull market in Facebook information. Its numerical success bears only an ill defined relationship to its inherent qualities.

In our view there is a tipping point in cases like iPhone and Facebook where the fact of information becomes more critical than their features, price or quality.

The iPhone is a poorly constructed product, with a long lock-in contract, arguably with poor performance characteristics compared to competitors.

If the only answer to its success is the user interface or usability then we’d have to conclude that all products should focus primarily in usability regardless of performance. In the case of the iPhone consumers choose to pay a higher than normal price to ignore poor performance.

Instead we believe iPhone addiction is the phenomenon we are looking at. As with Facebook. And other online bull markets. The kind of factors that are at work are confirmation bias – a tendency to take on only information that works as confirmation of our views; and availability bias – a tendency to use the latest information available rather than the balance of information.

Neither of these explains the peculiar web characteristic that we see with the iPhone, that people are generating information about it, all the time and that this information is crowding out other information about smart phones. To get at that we need to look at the sources of influence in web information markets. We’ll be digging out our Android study to do that in our next post.