For years now Target has been using analytics to increase the wallet share or value captured from shoppers. Using predictive analytics, Target and many of its competitors have been able to categorize customers based on anticipated buying patterns and influence those customers with advertisements or coupons. For example, Target\u00a0identified adults with young children, and sent\u00a0them toy catalogs before Christmas. But customer shopping habits become ingrained and are incredibly difficult to change so targeted advertisements meant to alter buying habits have had limited success. However, when customers go\u00a0through major life events like the birth of a child, their shopping habits do change and thus brand loyalty is\u00a0up for grabs. Target and their competitors know this and birth records are public so after the birth of a child parents are barraged with product ads. Target wanted to identify these women earlier, to access new parents and establish brand loyalty before other marketers, so they utilized advanced predictive analytics to do so.<\/p>\n
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How Target creatively uses data to increase value capture: <\/em>Target assigns each customer a guest ID number which tracks any and all information available like name, credit cards used, email address, purchase history, if they\u2019ve clicked on email advertisements, where they shop online, etc. Target also purchases demographic information like where the customer lives, their job history, and where they went to school.<\/p>\n <\/p>\n From here, Target analyzed buying patterns associated with baby registries. They found that 25 products could be used to predict the likelihood of a customer being pregnant and her associated due date. For example, vitamins like calcium, magnesium, and zinc are associated with the first trimester and unscented lotion is usually associated with the second trimester or when a customer purchases cocoa butter lotion, a purse large enough for diapers, and a blue rug, she is likely having a boy. Target did not disclose the analytics used to identify these 25 predictive products but complex regression analyses were likely used to determine which variables (products) were correlated with pregnancy. With this predictive data, Target could then send coupons to mothers-to-be for products before she even knows she needs them. By doing this, Target could\u00a0capture more value from customers by creating brand loyalty at a time when shopping habits are changing.<\/p>\n <\/p>\n A word of caution: <\/em>Target got better and better at predicting pregnancies and a few years ago they were able to identify that a 16-year-old in Minnesota was pregnant before her father even knew. As the story goes, a father walked into a Minneapolis store with a coupon book for his 16-year-old daughter full of coupons for diapers and maternity clothes. He was angry and accused Target of encouraging his daughter to get pregnant. When the store manager called the father to apologize, the father had spoken to his daughter and learned that she was in fact pregnant. This brings up concerns of privacy. Many Target shoppers would be uncomfortable with Target knowing so much about them. To avoid offending shoppers, Target has adapted their strategy to send targeted coupon books that include many of the items they know the shopper will need but hidden among items Target knows the shopper won\u2019t need like lawnmowers and wine glasses.<\/p>\n <\/p>\n Target doesn’t disclose sales figures broken down by division but since the company started this\u00a0analytics\u00a0initiative in 2002, sales have increased from $44 billion to $72 billion and the former CEO\u00a0Gregg Steinhafel\u00a0has attributed some of this growth to the “heightened focus on items and categories that appeal to specific guest segments\u00a0such as mom and baby”.<\/p>\n","protected":false},"excerpt":{"rendered":" Target has identified 25 products, which when purchased together, predict the likelihood of a woman being pregnant and her associated due date.<\/p>\n","protected":false},"author":26,"featured_media":2559,"comment_status":"open","ping_status":"closed","template":"","categories":[122,870,962],"class_list":["post-2558","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-predictiveanalytics","category-regression","category-target"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/data-driven-value-creation-value-capture-and-operating-models\/","yoast_head":"\n