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last payment was received on February 9. If you need to speak with a customer-service representative, press 1….” A phone call that might have taken twenty or thirty seconds, or even a minute, now lasts just ten seconds. Everyone wins.
    Super Crunching also has transformed customer service calls into a sales opportunity. Data analysis of customer characteristics generates a list of products and services that this kind of consumer is most willing to buy, and the service rep sees the list as soon as she takes the call. It’s just like Amazon’s “customers who like this, also like this” feature, but transmitted through the rep. Capital One now makes more than a million sales a year through customer-service marketing—and their data-mining predictions are the big reason why. Again, everybody wins.
    But maybe not equally. CapOne gives itself the lion’s share of the gains whenever possible. For example, a statistically validated algorithm kicks in whenever a customer tries to cancel her account. If the customer is not so valuable, she is routed to an automated service where she can press a few buttons and cancel. If the customer has been (or is predicted to be) profitable, the computer routes her to a “retention specialist” and generates a list of sweeteners that can be offered.
    When Nancy from North Carolina called to close her account because she felt her 16.9 percent interest rate was too high, CapOne routed her call to a retention specialist named Tim Gorman. CapOne’s computer automatically showed Tim a range of three lower interest rates—ranging from 9.9 percent to 12.9 percent—that he could offer to keep Nancy’s business.
    When Nancy claimed on the phone that she just got a card with a 9.9 percent rate, Tim responded with “Well, ma’am, I could lower your rate to 12.9 percent.” Because of Super Crunching, CapOne knows that a lot of people will be satisfied with this reduction (even when they say they’ve been offered a lower rate from another card). And when Nancy accepts the offer, Tim gets an immediate bonus. Everyone wins. But because of data mining, CapOne wins a bit more.
    CapOne Rolls the Dice
    What really sets CapOne apart is its willingness to literally experiment. Instead of being satisfied with a historical analysis of consumer behavior, CapOne proactively intervenes in the market by running randomized experiments.
    In 2006, it ran more than 28,000 experiments—28,000 tests of new products, new advertising approaches, and new contract terms.
    Is it more effective to print on the outside envelope “LIMITED TIME OFFER” or “2.9 Percent Introductory Rate!”? CapOne answers this question by randomly dividing prospects into two groups and seeing which approach has the highest success rate.
    It seems too simple. Yet having a computer flip a coin and treating prospects who come up heads differently than the ones who come up tails is the core idea behind one of the most powerful Super Crunching techniques ever devised.
    When you rely on historical data, it is much harder to tease out causation. A miner of historical data who wants to find out whether chemotherapy worked better than radiation would need to control for everything else, such as patient attributes, environmental factors—really anything that might affect the outcome. In a large random study, however, you don’t need these controls. Instead of controlling for whether the patients smoked or had a prior stroke, we can trust that in a large randomized division, about the same proportion of smokers will show up in each treatment type.
    The sample size is the key. If we get a large enough sample, we can be pretty sure that the group coming up heads will be statistically identical to the group coming up tails. If we then intervene to “treat” the heads differently, we can measure the pure effect of the intervention. Super Crunchers call this the