This is a (probably) under-conceived blogpost but it's based on a few things that I've been thinking about over the last couple of days.
I've been pulling together a few strands of thought about applying maths (math for my American chums) to various problems. This is well illustrated by two examples. Firstly the Oakland Athletics baseball team who famously, and successfully, ditched hunches to focus almost exclusively on statistics to pick the team and win matches. And very successful it was too, as famously shown in the movie Moneyball. The second example is Nate Silver, US pollster, who predicted 50 out of 50 states in the US in the most recent election and correctly called it comfortably for Obama while other pollsters were predicting a closer race. The important thing in Silver's case is that he was focusing solely on the numbers. What was the polling data saying? He ignored all the other extraneous noise inherent in a US election.
The two examples above have a lot in common. They involve dispassionate analysis based on as much data as possible. Surely there's a lesson for marketeers worldwide. Marketing has historically been based largely on hunches. However it doesn't need to any more. With more an more data available, including real time information on customer usage , marketing becomes less an art and more a science. This inevitably means that marketing moves into the domain of the data scientist. The lesson is clear: gather more data, crunch more data, base your decisions on that. Perhaps it's a little sad to admit that the good old fashion hunch isn't the best way to proceed, but it's probably the case.
I've been pulling together a few strands of thought about applying maths (math for my American chums) to various problems. This is well illustrated by two examples. Firstly the Oakland Athletics baseball team who famously, and successfully, ditched hunches to focus almost exclusively on statistics to pick the team and win matches. And very successful it was too, as famously shown in the movie Moneyball. The second example is Nate Silver, US pollster, who predicted 50 out of 50 states in the US in the most recent election and correctly called it comfortably for Obama while other pollsters were predicting a closer race. The important thing in Silver's case is that he was focusing solely on the numbers. What was the polling data saying? He ignored all the other extraneous noise inherent in a US election.
The two examples above have a lot in common. They involve dispassionate analysis based on as much data as possible. Surely there's a lesson for marketeers worldwide. Marketing has historically been based largely on hunches. However it doesn't need to any more. With more an more data available, including real time information on customer usage , marketing becomes less an art and more a science. This inevitably means that marketing moves into the domain of the data scientist. The lesson is clear: gather more data, crunch more data, base your decisions on that. Perhaps it's a little sad to admit that the good old fashion hunch isn't the best way to proceed, but it's probably the case.
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