“StubHub is giving us a fantastic data set,” says Elea Feit, Wharton’s research director. None of the data is personally identifiable, she says; StubHub doesn’t keep demographic information beyond zip codes.
Grace Lau, StubHub’s director of relationship marketing, told the researchers that the company has no trouble attracting sellers—it lacks tickets for an event less than 2 percent of the time. The challenge it faces is to attract more ticket buyers and get them to buy more often.
The researchers suggested six ways of analyzing the data. One researcher plans to try to “predict customer purchasing behavior as a function of sport team performance.” Several proposals explore the effectiveness of e-mail marketing.
Fader says he has worked to bring analytic rigor to marketing questions for 25 years. But he also thinks that many companies, encouraged by hardware and software vendors, maintain and try to use far more personal information about their customers than they actually need. Many companies have “been collecting a lot of data they shouldn’t have permission to be collecting,” he says. “It creeps people out. The companies don’t even know what to do with the data.” For instance, he says, the most effective way to predict future buying behavior is to gather data that direct marketers have used since the 1960s: RFM, which stands for recency of purchase, frequency of purchase, and monetary value of purchase. Customers’ patterns differ depending on the product, so it’s the job of researchers, he says, to learn what can keep people buying more, or for longer.