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rewarded. It’s actually a bit of both. I’m troubled that Harrah’s is making what can be an addictive and ruinous experience even more pleasurable. But because of Harrah’s pain-point predictions, its customers tend to leave happier.
    The Harrah’s strategy of targeting benefits is being adopted in different retail markets. Teradata found, for example, that one of its airline clients was giving perks to its frequent fliers based solely on how many miles they flew each year, with Platinum customers getting the most benefits. But the airline hadn’t taken account of how profitable these customers were. They didn’t plug in other available information, such as how much Platinum fliers paid for tickets, where they bought them, whether they called customer service, and most important, whether they traveled on flights where the airline actually made money. After Teradata crunched the numbers taking into account these bottom-line attributes, the airline found out that almost all of its Platinum fliers were unprofitable. Teradata’s Scott Gnau summed it up, “So they were giving people an incentive to make them lose money.”
    The advent of tera mining means that the era of the free lunch is over. Instead of having more profitable customers subsidizing the less profitable, firms will be able to target rewards to their most profitable customers. But
caveat emptor
! In this brave new world, you should be scared when a firm like Harrah’s or Continental becomes particularly solicitous of your business. It probably means you have been paying too much. Airlines are learning to give upgrades and other favorable treatment to the customers that make them the most money, not just the ones that fly the most. Airlines can then “encourage people to become more profitable,” Gnau explains, by charging you more, for example, for buying tickets through a call center than for buying them online.
    This hyper-individualized segmentation of consumers also lets firms offer new personalized services that clearly benefit society. Progressive insurance capitalizes on the new capabilities of data mining to define extremely narrow groups of customers, e.g., motorcycle riders ages thirty and above, with college educations, credit scores over a certain level, and no accidents. For each cell, the company runs regressions to identify factors that most closely correlate with that group’s losses. Super Crunching on this radically expanded set of factors lets it set prices on types of consumers who were traditionally written off as uninsurable.
    Super Crunching has also created a new science of extraction. Data mining increases firms’ ability to charge individualized prices that predict our individualized pain points. If your walk-away price is higher than mine, tera mining will lead firms to take a bigger chunk out of you one way or another. In a Super Crunching world, consumers can’t afford to be asleep at the wheel. It’s no longer safe to rely on the fact that other consumers care about price. Firms are figuring out more and more sophisticated ways to treat the price-oblivious differently than the price-conscious.
    Tell Me What You Know About Me
    Tera mining sometimes gives businesses a decided information advantage over their customers. Hertz, after analyzing terabytes of sales data, knows a lot more than you do about how much gas you’re likely to leave in the tank if you prepay for the gas. Cingular knows the probability that you will go beyond your “anytime minutes” or leave some unused. Best Buy knows the probability that you will make a claim on an extended warranty. Blockbuster knows the probability that you will return the rental late.
    In each of these cases, the companies not only know the generalized probability of some behavior, they can make incredibly accurate predictions about how individual customers are going to behave. The power of corporate tera mining