Planning For Small Data

It seems that barely a week goes by at the moment without another agency group launching another real time data planning unit. The opportunity to focus ad targeting and messaging down to an individual level is being snapped up by all the main holding companies, offering a potential to craft campaigns focusing on exactly the discrete audiences of the right people at exactly the right time.

Which is all well and good, and represents the logical extreme of what media agencies have always tried to do. However, the social sciences, who already know a fair bit about how people make decisions, would suggest that it may not be the right way to apply the technology that we have at our disposal. Daniel Kahnemann in psychology and Richard Thaler in economics have shown that rational thinking plays very little role in our decision making; we naturally default to habits and rules of thumb. Robin Dunbar’s work in evolutionary psychology and Nicolas Christakis and Paul Omerod in public policy highlight how much of our habitual behaviour is derived from our interactions with other people. Peter Field and Les Binet’s work on the IPA Databank show that advertising has the greatest chance of success if it sets Fame as an objective – this makes sense in the context of our predisposition to learn socially, as it is a substitute for other visible cues of popularity: it gives us something to copy. However, micro-segmentation as a basis for targeting individuals comes with no social context, no understanding of people who live in networks not as individual rational units.

That isn’t to say that data isn’t hugely important, rather that we need to think wider about how it is applied and for what purposes. Demographics have always been used to average out difference, and create target audiences of individuals grouped together based on similarity. Real people on the other hand are diverse, passionate and socially adept at living in networks of other people. Demographic data made sense in the making of television (whether ads or programmes), as TV needs to tell a story universally relevant to a large number of people in a short space of time. TV injects cultural energy into human networks. However, time is short because costs are high, so brands have to ensure that messages are simple and have broad appeal. When creating ideas that live in the infinite number and variety of free media that are now at our disposal, we believe that the most powerful source of data is still untapped; the ‘data’ held about us by our friends. Those people that Mark Granovetter refers to as our ‘strong tie networks’, the people closest to us (and those that Facebook’s Edgerank algorithm for example is designed to approximate) know far more about each of us than any data source available to marketers. However, for a brand with big ambitions, this data is hard to scale. So how do we make use of ‘Small Data” to create ideas for millions of micro networks?

I believe that the value in Big Data is not in the data itself, but in the processing power that we apply to it. This allows us to set objectives based on patterns in data. Understanding which behaviours may be changed through social learning, from our closest ties, which are learnt through wider observation of people around us, who form part of the bigger communities we are part of, and which are learnt from experts, is of fundamental importance in setting the direction for creative development of ideas. Particularly if those ideas are going to be inclusive enough to spread through communities. It also allows us to think more accurately about the transmission of ideas. As Mark Earls and Alex Bentley outline in I’ll Have What She’s Having, social learning is often about choosing to receive an idea, not choosing to transmit it. Thinking about the role of ‘influence’ in marketing is impossible without this framework. After all, Oasis were influenced by The Beatles, but that was not the objective that The Beatles set out with.

Using Big Data to map network structures has far greater value in showing how ideas spread at an aggregate level than in understanding the individual. A small world network (ie one that has no central nodes, or ‘influencers, as classical marketing would call them) requires a very different approach to one that is scale free (and so has central hubs that are disproportionately connected). And this in turn has a major impact on the type of idea that will affect behaviour at a mass level. The standard approach to a small world structure (where one has been identified) is to create many small ideas that are set loose into the world on the basis that prediction of success is impossible – you might just as well try and predict stock market – so you see which take off and get behind them. However this isn’t an approach that marketing directors are often comfortable with: it sounds expensive.

Instead, I see the power of Big Data as providing the direction in which you can pursue something of far greater value: Small Data. Using small data means creating things that people personalize to make relevant for their friendship groups – from turning a real life experience into shareable media, like Luna Park in Sydney to selling named bottles of Coke across Australia to create opportunities to buy presents and take personalized product photos, to Smirnoff tapping into intercity football rivalry to vote for and against the UK’s top clubs, to the many remixes of Mastercard’s ‘Priceless’ meme/ad. It is about remixing ideas because that has value to the individual and their friends, rather than because a brand thinks that lots of people want to re-edit their ad. It isn’t about people participating in a brand’s idea. It is about brands participating in people’s ideas.

Those examples above are about small data, but they are also small in size. What I’m trying to do on this blog is play around with the approaches that make them bigger. Big data is a starting point, but what all those examples use is Regular Data: sales data, visitor data, media data, that has been analysed to focus on WHY they exist, whatever form they take. Too often marketers and agencies who are focusing on Big Data, or on the Small/social sides of data, lose sight of the data that we have always had access to: the bit that explains why we are doing what we are doing, and why it is the most effective route to growth. Insight, rather than data. To paraphrase a line from Rory Sutherland:

Without a theory, data is useless. Without a theory, all Darwin had was “some funny birds and lizards and shit”. Darwin saw insight in the data.