Application Case 5.7 Predicting Customer Buying Patterns The Target Story In early 2 0 1 2 , an infamous story appeared concerning Target's practice of predictive analytics. The Story was about a teenager girlwho was being sent advertising flyers and coupons by Target for the kin d s o f things that a new mother-to -be would buy from a store like Target. The story g o e s like this: An angry man went in to a Target outside of Minneapolis, demand in g to talk to a m an ager: "My daughter got this in the mail!" he said. "S h e 's still in high school, an d you're send in g h e r coupons for baby clothes an d cribs? Are you trying to encourage her to get p regnant?" T h e manager didn't have any idea what the man was talking about. He looked at the mailer. Sure enough , it was addressed to the man 's daughter and contained advertisements for maternity cloth in g , nursery furniture, and pictures of smiling infants. The manager apologized and then called a few days later to apologize again. On the phone , though , the father was some what abashed . "I had a talk with my daughter," he said. "It turns out 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 ." As it turns out, Target figured o u t a teen girl was pregnant before h e r father did! Here is how they did it. Target assign s every customer a Guest ID number (tied to their credit card, name, or e-m ail address) that becomes a placeholder that keeps a history of everything they have b ought. Target augments this data with a n y demographic information that they had collected from them or bought from other information sources. Using this information, Target looked at historical buying data for all the females who had signed up for Target baby registries in the past. They analyzed the data from all directions, and soon enough some useful patterns emerged . For example, lotion s and special vitamins were among the products with interesting purchase patterns. Lots of people buy lotion, but they have noticed was that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester. Another analyst noted that sometime in the first 20 weeks, pregnant women load upon supplements like calcium, magnesium, and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and wash cloths, it signals that they could be getting close to their delivery date. At the end, they were able to identify about 25 products that, when analyzed together, allowed them to assign each shopper a "pregnancy prediction" score. More important, they could also estimate a woman's due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy. If you look at this practice from a legal perspective, you would conclude that Target did not use any information that violates customer privacy; rather, they used transactional data that most every other retail chain is collecting and storing (and perhaps analyzing) about their customers. What was disturbing in this scenario was perhaps the targeted concept: pregnancy. There are certain events or concepts that should be off limits or treated extremely cautiously, such as terminal disease, divorce, and bankruptcy.

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identify one data mining example within your chosen industry that may cross the line between the discovery of information and infringement of privacy. Discuss in detail the scenario and how this should be handled.