Leftish

A man walked into a Target outside Minneapolis and angrily demanded to see the manager. In his hand were coupons that Target had sent to his daughter.

“My daughter got this in the mail! 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 local Target manager apologized and then called a few days later to apologize again, but the father was much more conciliatory. “There’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.” How did they know details about this girl that even her father wasn’t privy to yet?

The Target Corporation has been collecting enormous amounts of data for over a decade on every person who walks into one of its stores. NY Times Magazine recently reported on the ways companies like Target can identify key inflection points in consumers’ lives, or “life events” that will impact their shopping habits (and, more importantly, when the right coupon would get them to spend in new ways.)

In the world of life events, none are more important (or more expensive) than the arrival of a new baby; so new parents are the holy grail for a retailer like Target.

In 2002, marketers at Target asked statisticians to answer a very creepy question: ‘If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know, can you do that?’ However, they wanted to get more specific. They wanted to target women in their second trimester and send them specifically designed advertisements, because that’s when most expectant mothers begin spending on a wide variety of new items, like maternity clothing, prenatal vitamins etc.

“We knew that if we could identify them in their second trimester, there’s a good chance we could capture them for years… As soon as we get them buying diapers from us, they’re going to start buying everything else too.” — Andrew Pole, Target statistician.

Computers crawled through Pole’s data, and eventually he found he was able to identify about 25 products that, when analyzed together, allowed his team to assign each shopper a ‘pregnancy prediction’ score. He quickly developed a list of tens of thousands of women who were very likely pregnant.

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