Thanks to the rise of social media, there’s a mad race to measure influence and help brands harness it to their advantage. That has created an ecosystem of companies vying to prove that they can most accurately identify the social media users with the most clout.
Originally posted on Econsultancy on 15 August 2012 – Klout adds more useless signals to its algorithm.
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Back in the ’90s I worked with several well-known global companies helping them implement performance measurement systems to prioritise decisions, reward individuals and cut down on the information noise inherent in any leading enterprise.
One of the principles I drummed into them at the time was to recognise that human interaction is far more complex than cause-and-effect and I helped them grasp chaos theory and systems thinking (espoused by author Peter Senge). Chaos theory (despite its name) is mostly about recognising patterns, which are framed within the modalities of systems thinking called Archetypes.
I would recommend anyone who’s truly interested in understanding the systematic behaviour of human interaction to take a look at these – over the years I’ve proven these time and time again and they make an excellent series of test cases before implementing any tool such as Klout.
Some years later I ran Experian’s Integrated Marketing business where we managed the user data for companies such as BSkyB, Microsoft and Skype. BSkyB were especially receptive to looking deeper than the cause and effect and benefitted greatly from such a mature understanding of their customers.
The challenge for ‘Big Data’ is learning how to focus on the patterns that matter (seeing the wood from the trees), adding more data points and sources is usually in my experience a very bad idea unless of course it’s just for bragging rights.