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June 13, 2007, By Allan E. Alter, CIOinsight.com

Peter Fader, Wharton's quantitative marketing wizard, has a message for CIOs: Stop collecting so much customer data, and stop misusing data mining.

Peter Fader, professor of marketing at University of Pennsylvania's Wharton School, is the ultimate marketing quant -- a world-class, award-winning expert on using behavioral data in sales forecasting and customer relationship management.

The energetic and engaging marketing professor has a pet peeve: He hates to see companies waste time and money collecting terabytes of customer data in attempts to make conclusions and predictions that simply can't be made. Fader has come up with an alternative, which he is researching and teaching: Complement data mining with probability models, which, he says, can be surprisingly simple to create. The following is an edited version of his conversation with CIO Insight Executive Editor Allan Alter.

CIO INSIGHT: What are the strengths and weaknesses of data mining and business intelligence tools?

FADER: Data mining tools are very good for classification purposes, for trying to understand why one group of people is different from another. What makes some people good credit risks or bad credit risks? What makes people Republicans or Democrats? To do that kind of task, I can't think of anything better than data mining techniques, and I think it justifies some of the money that's spent on it. Another question that's really important isn't which bucket people fall into, but when will things occur? How long will it be until this prospect becomes a customer? How long until this customer makes the next purchase? So many of the questions we ask have a longitudinal nature, and I think in that area data mining is quite weak. Data mining is good at saying, will it happen or not, but it's not particularly good at saying when things will happen. ...

People keep thinking that if we collect more data, if we just understand more about customers, we can resolve all the uncertainty. It will never, ever work that way. The reasons people, say, drop one cell phone provider and switch to another are pretty much random.

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