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stores in the hurricane’s path. Analyzing sales of other stores in areas hit by hurricanes, Wal-Mart was able to predict that people would be yearning for the gooey comfort of Pop-Tarts, finger food that doesn’t require cooking or refrigeration. Firms are engaging in “analytic competition” in an explicit attempt to out-data-mine the other guy, struggling to first uncover and then exploit the hidden determinants of profitability.
    Some of this Super Crunching is done in-house, but truly large datasets are warehoused and analyzed by specialist firms like Teradata, which manages literally terabytes of data. Sixty-five percent of the top worldwide retailers (including Wal-Mart and JCPenney) use Teradata. More than 70 percent of airlines and 40 percent of banks are its customers.
    Crunching terabytes helps predict which customers are likely to defect to rivals. For its most profitable customers, Continental Airlines keeps track of every negative experience that may increase the chance of defection. The next time a customer who experienced a bad flight takes to the air, a data-mining program automatically kicks in and gives the crew a heads-up. Kelly Cook, Continental’s onetime director of customer relationship management, explains, “Recently, a flight attendant walked up to a customer flying from Dallas to Houston and said, ‘What would you like to drink? And, oh, by the way, I am so sorry we lost your bag yesterday coming from Chicago.’ The customer flipped.”
    UPS uses a more sophisticated algorithm to predict when a customer is likely to switch to another shipping company. The same kind of regression formula that we saw at play with wines and matchmaking is used to predict when a customer’s loyalty is at risk, and UPS kicks into action even before the customer has thought about switching. A salesperson proactively calls the customer to reestablish the relationship and resolve potential problems, dramatically reducing the loss of accounts.
    Harrah’s casinos are particularly sophisticated at predicting how much money they can extract from clients and still retain their business. Harrah’s “Total Rewards” customers use a swipeable electronic card that lets Harrah’s capture information on every game played at every Harrah’s casino they’ve visited. Harrah’s knows in real time on a hand-by-hand (or slot-by-slot) basis how much each player is winning or losing. It combines these gambling data together with other information such as the customer’s age and the average income in the area where he or she lives, all in a data warehouse.
    Harrah’s uses this information to predict how much a particular gambler can lose and still enjoy the experience enough to come back for more. It calls this magic number the “pain point.” And once again, the pain point is calculated by plugging customer attributes into a regression formula. Given that Shelly, who likes to play the slots, is a thirty-four-year-old white female from an upper-middle-class neighborhood, the system might predict her pain point for an evening of gambling is a $900 loss. As she gambles, if the database senses that Shelly is approaching $900 in slot losses, a “luck ambassador” is dispatched to pull her away from the machine.
    â€œYou come in, swipe your card, and are sitting at a slot,” Teradata’s Gnau said. “When you get close to that pain point, they come out and say, ‘I see you’re having a rough day. I know you like our steakhouse. Here, I’d like you to take your wife to dinner on us right now.’ So it’s no longer pain. It becomes a good experience.”
    To some, this kind of manipulation is the science of diabolically separating as many dollars from a customer as possible on a repeated basis. To others, it is the science of improving customer satisfaction and loyalty—and of making sure the right customers get