What type of person takes a midweek ski trip? American Skiing Co., which operates eight ski resorts in the United States, needed to find out. Its flagship East Coast resort, Killington in Vermont, was full on weekends but emptier midweek, and it was clear that untargeted marketing wasn’t going to fill the chairlifts.

Together with Newburyport, Mass., startup Genalytics, American Skiing combed its customer database for the answer. Applying what it calls "Darwinian genetic algorithms," Genalytics created more than 50,000 predictive test models for American Skiing in three days, each emphasizing different combinations of variables, such as travel time or number of kids. The software then took the most predictive models and "mated" them to "breed" even more insightful models.

The exercise showed that the customers most likely to visit Killington midweek were those who had never visited the company’s western resorts?such as Steamboat in Steamboat Springs, Colo.?presumably because skiers tended to spend a week at Steamboat, thereby using up their midweek vacation days. Based on this finding, the company stopped marketing midweek Killington packages to those customers and instead started offering more synchronized cross-promotions between Killington and Steamboat. (And like most predictive intelligence users, the company refuses to discuss actual results, for competitive reasons.)

"The ability to be iterative was crucial," says Diane Murphy, former director of database marketing for American Skiing in Bethel, Maine. The new fast-modeling technology helped avoid misleading indicators and helped get to a truly predictive result at a manageable cost.

Power Surge

Welcome to the realm of predictive intelligence, a new generation of analytical applications that are leveraging faster processing speeds, more complex algorithms and existing data mining infrastructures to help enterprises push back the fog of the unknown. Although statistical forecasting has been around for decades, what’s changed is the ability to quickly and cheaply analyze huge amounts of data, examine more variables, uncover previously hidden relationships and deliver startlingly accurate predictions without hiring a roomful of white-coated PhDs.

"The technology for processing has gotten remarkable," says Stacie McCullough Kilgore, a senior analyst at Cambridge, Mass.-based Forrester Research. "This really changes the nature of modeling."

Across large enterprises, predictive intelligence technology is being used for a stunningly broad set of applications. Sports teams are using it to predict when star athletes might get injured. Banks use it to detect money laundering and insider trading. Retailers are forecasting demand down to the store and item level. Manufacturers use it in product design and to forecast equipment failure. Drug companies use it to develop drugs and then figure out what marketing programs will cause doctors to write more prescriptions.