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Sunday, February 16, 2014

What Social Data Can Tell You (And Why) 02-17

What Social Data Can Tell You (And Why)

Pretty much everything. In a nutshell.

Take this fascinating piece of research: Facebook's data scientists were able to determine a very particular pattern when couples were courting, and when they had started a relationship simply. How? Simply by looking at the frequency of status updates between two people.
In essence:

During the 100 days before the relationship starts, we observe a slow but steady increase in the number of timeline posts shared between the future couple. When the relationship starts ("day 0"), posts begin to decrease. We observe a peak of 1.67 posts per day 12 days before the relationship begins, and a lowest point of 1.53 posts per day 85 days into the relationship. Presumably, couples decide to spend more time together, courtship is off, and online interactions give way to more interactions in the physical world.

Social data are powerful

These predictions comes from a new class of data called "social data". Broadly speaking, social data is the data that people create when they use social platforms like Facebook, Pinterest or LinkedIn. It is our likes, pins, favourites, retweets, status messages, content of those messages, people we are friends with.

Social data is normally voluntarily and informally created by the individual during the act of using a social platform. It is voluntarily made public (or semi-public)on the platform in question. It reflects their ordinary course of business, the stuff we care about. It provides a picture which is explicitly incomplete. It can be viewed in aggregate or at an individual (although not necessarily prima facie personally identified).
Social data is new. No more than 10 or 15 years old, running back to the older social platforms (although arguably it stretches back to the earliest social protocols like Usenet).

It is also very powerful. And can predict much more about individuals and groups that the non-specialist can imagine.

Birds of a feather, that's why

Take the pattern of courtship to relationship discovered by Facebook's data scientists. This pattern emerges because of similarities in the way people behave as they go through similar life stages. We can all be different, but in aggregate we might be able to evince some underlying pattern. It is these underlying patterns which allow us to predict things which seem unlikely - like whether a couple of entering a phase of courtship.
I write 'unlikely' but it's really only 'unlikely' if you don't work with social data, or other behaviour data, and don't hang around with data scientists and other statistical types.
In truth, that we can predict courtship from signals like this is hardly at all surprising. We can predict other things too. These include wealth, gender, sexual orientation, even whether ones parent's divorced before you were an adult. All of those predictions were based on Facebook Like data - commonly acknowledged as quite noisy. (You can read an earlier blog post I wrote:Three Insights from Social Data which covers some of these predictions.)

For a moment think about that.

It's a brave new world

For companies, social data allows them to know who they are dealing with, well before that person becomes a customer. If a potential customer follows you on Twitter, you can build a pretty full picture of who they are, what they might like and what you might want them as a customer. Well before you decide to despatch advertising or a sales person towards them. For example, at PeerIndex we've helped companies modify their product offerings in near real-time on the strength of behaviours we've picked up in social data. IBM is helping companies identify customer's personality types based on social data - for better customer service.

For governments, social data allows you to understand the over-arching trends and themes in your jurisdiction. It allows for the early detection of themes that you might need to respond to. (Disease outbreaks? Or sociological changes in household behaviour?)

With the increase in people sharing data with companies - a subject I touched on a few weeks ago - such predictions will become more precise, more accurate and more relevant.