Publications

b-eye-network, by Eric Siegel, January 6, 2009

This article summarizes the wide range of business applications of predictive analytics, each of which predicts a different type of customer behavior in order to automate operational decisions. A named case study is linked for each of eight pervasive commercial applications of predictive analytics.

Predictive analytics - the concept is dazzling yet daunting. To foresee what each customer will do next may sound like an overwhelmingly complex feat of math and statistics. Is such a thing even possible? And, if so, tell me specifically how it helps my business!

Here's the bottom line: the predictions don't actually have to be all that accurate to deliver great value to your business. For example, if predictive analytics identifies a customer segment 3 times more likely to defect than average, you can target a retention offer such as a discount accordingly, and avoid incurring the revenue loss that results from providing the discount to those customers not destined to defect.

This "numbers game" translates into a high return, and doesn't depend on highly accurate predictions. If your overall defection rate is 5%, the segment we've discovered has a higher defection rate of 15%, so, in this case, we're not actually confident in predicting whether any one individual customer will defect. Rather, the value comes from identifying this kind of customer segment which is predicted in aggregate to behave much differently than your overall customer base.

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