The first part is easy. Target has a baby shower registry in which pregnant women register for baby gifts in advance of the birth of their children. These women are already Target shoppers, and they’ve effectively told the store that they are pregnant. But here is the statistical twist: Target figured out that other women who demonstrate the same shopping patterns are probably pregnant, too. For example, pregnant women often switch to unscented lotions. They begin to buy vitamin supplements. They start buying extra big bags of cotton balls. The Target predictive analytics gurus identified twenty five products that together made possible a “pregnancy prediction score.” The whole point of this analysis was to send pregnant women pregnancy related coupons in hopes of hooking them as long term Target shoppers.
How good was the model? The New York Times Magazine reported a story about a man from Minneapolis who walked into a Target store and demanded to see a manager. The man was irate that his high school daughter was being bombarded with pregnancy related coupons from Target. “She’s still in high school and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?” the man asked. The store manager apologized profusely. He even called the father several days later to apologize again. Only this time, the man was less irate; it was his turn to be apologetic. “It turns out there’s been some activities in my house I haven’t been completely aware of,” the father said. “She’s due in August.” The Target statisticians had figured out that his daughter was pregnant before he did. That is their business . . . and also not their business.
Charles Wheelan, Naked Statistics: Stripping the Dread from the Data (Kindle Location 4250), 2014, W. W. Norton & Company
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