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Predictive Analytics World – Methodology and Business Learning

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This is the second correspondence on last week’s Predictive Analytics World (PAW) in San Francisco. About a year and a half ago, I wrote a book review on Super Crunchers by Yale economist Ian Ayres, in which I noted that super crunching as the amalgam of predictive modeling and randomized experiments. Randomization to treatment and control groups allows investigators to minimize the risk of study bias so that the only important differences between groups out of the gate are that one is named treatment while the other is called control. Predictive modeling by itself allows analysts to infer relationships and correlation; the addition of experiments sharpens the focus to cause and effect. The combination of predictive modeling and experiments is thus a very potent tool in the business learning arsenal of hypothesize/experiment/learn.

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Comments (1)
This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves. Analytics tends to be back-end but it has to move and adapt to this new world order in marketing even if means companies start doing simple things well.

http://www.cequitysolutions.com/insight.php

Posted by Jason F | Friday, April 17 2009 at 1:42AM ET
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