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.
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