As Pole and his colleagues studied the purchasing data, they were able to \u201cidentify about 25 products that, when analyzed together, allowed him to assign each shopper a \u201cpregnancy prediction\u201d score. More important, he could also estimate her due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy.\u201d[ii]<\/a><\/p>\n This application of predictive analytics opened a revenue growth opportunity, as Target was able to effectively target women with coupons and ads for the products they would soon need as expecting mothers with the hope to convert a consumer into a loyal Target guest.<\/p>\n Source[iii]<\/a><\/p>\n However, a year after Pole developed this \u201cpregnancy-prediction model,\u201d[iv]<\/a> a father of a teenage daughter entered a Target angrily with coupons for baby\/maternity clothes and cribs that his daughter had received. After the Target team member followed up with the father a month later, the father shared that he had learned that his daughter was in deed pregnant.<\/p>\n <\/p>\n Create Value:<\/strong><\/p>\n As consumers, we want companies to anticipate our needs, we want products to be available when we need them, and that companies will adapt to our needs as they change.[v]<\/a> Predictive analytics allows retailers to fulfill these customer demands and expectations.<\/p>\n \u201cAlmost every major retailer, from grocery chains to investment banks to the U.S. Postal Service, has a \u201cpredictive analytics\u201d department devoted to understanding not just consumers\u2019 shopping habits but also their personal habits, so as to more efficiently market to them.\u201d[vi]<\/a><\/p>\n This use of advanced analytics and predictive modeling is changing the face of retail. [vii]<\/a><\/p>\n From predicting trends, demand forecasting to maximize inventory, price optimization, and attracting new customers, [viii]<\/a> predictive analytics is learning more about customers, sales patterns, inventory management and how to attract new customers at major life events when the customer is willing to change shopping habits \u2013 such as the birth of a child.[ix]<\/a><\/p>\n Source[x]<\/a><\/p>\n <\/p>\n Capture Value: <\/strong><\/p>\n Retailers are capturing value from predictive analytics by targeting customers through metrics such as demographics, age, income, and other variables to guide the customer to new products based on previous purchasing histories.[xi]<\/a> By building a comprehensive customer profile, the retailer can ensure the customer receives the appropriate coupons, advertisements, promos etc. resulting in an increase in purchases and revenue while minimizing the amount of wasted spending on less targeted advertising.<\/p>\n Retailers are capturing consumer dollars by offering incentives at the right time for the right items while the customer is in the right mindset, maximizing the likelihood of a purchase and thus increased sales revenue.<\/p>\n Source[xii]<\/a><\/p>\n <\/p>\n Challenges<\/strong>:<\/p>\n As Target realized soon after news went public of the pregnant teenager, using data to predict a woman\u2019s pregnancy can be a public relations disaster. [xiii]<\/a><\/p>\n Target shared that \u201cwe are very conservative about compliance with all privacy laws. But even if you\u2019re following the law, you can do things where people get queasy.\u201d[xiv]<\/a><\/p>\n In addition to the challenge of identifying the right customer data and ensuring predictive analytics is in deed predicting the right habits and behaviors, retailers must also be aware of the challenge of identifying consumer trends and supporting them without making a consumer feel that their privacy has been breeched.[xv]<\/a><\/p>\n After minimizing the creep level of predictive analytics, Target found consumers were willing to use the coupons. Once the consumer entered the store, Target was able to cue up additional targeted rewards utilizing GEO locating technology both upon entering and when walking throughout the store.<\/p>\n <\/p>\n Conclusion<\/strong>:<\/p>\n As predictive analytics continues to define the retail space, retailers will need to further develop means to differentiate their products and value to the customer. The ability to provide value through a targeted coupon at the right time will no longer be the differentiating factor as all retailers become more highly educated on their use of predictive analytics.<\/p>\n The ability to be the first to predict trends and execute on them seamlessly will become table stakes.<\/p>\n For now though, Pole shared with NY Times, \u201cJust wait. We\u2019ll be sending you coupons for things you want before you even know you want them.\u201d[xvi]<\/a><\/p>\n <\/p>\n <\/a>_____________________________________________________________________<\/p>\n [i]<\/a> http:\/\/www.nytimes.com\/2012\/02\/19\/magazine\/shopping-habits.html?pagewanted=1&_r=1&hp<\/p>\n [ii]<\/a> http:\/\/www.nytimes.com\/2012\/02\/19\/magazine\/shopping-habits.html?pagewanted=1&_r=1&hp<\/p>\n [iii]<\/a> http:\/\/funginstitute.berkeley.edu\/news\/avoiding-the-traps-of-big-data\/<\/p>\n [iv]<\/a> http:\/\/www.nytimes.com\/2012\/02\/19\/magazine\/shopping-habits.html?pagewanted=1&_r=1&hp<\/p>\n [v]<\/a> https:\/\/www.datanami.com\/2016\/07\/20\/9-ways-retailers-using-big-data-hadoop\/<\/p>\n [vi]<\/a> http:\/\/www.nytimes.com\/2012\/02\/19\/magazine\/shopping-habits.html?pagewanted=1&_r=1&hp<\/p>\n [vii]<\/a> https:\/\/www.datanami.com\/2016\/07\/20\/9-ways-retailers-using-big-data-hadoop\/<\/p>\n [viii]<\/a> http:\/\/www.ingrammicroadvisor.com\/data-center\/four-big-data-examples-in-the-retail-market<\/p>\n [ix]<\/a> http:\/\/www.nytimes.com\/2012\/02\/19\/magazine\/shopping-habits.html?pagewanted=1&_r=1&hp<\/p>\n [x]<\/a> https:\/\/www.thepennyhoarder.com\/smart-money\/look-at-all-the-free-stuff-i-got-for-creating-a-baby-registry-at-target\/<\/p>\n [xi]<\/a> http:\/\/www.ingrammicroadvisor.com\/data-center\/four-big-data-examples-in-the-retail-market<\/p>\n [xii]<\/a> http:\/\/dealmama.com\/2014\/07\/target-baby-coupon-20-100-baby-purchase\/<\/p>\n [xiii]<\/a> http:\/\/www.nytimes.com\/2012\/02\/19\/magazine\/shopping-habits.html?pagewanted=1&_r=1&hp<\/p>\n [xiv]<\/a> http:\/\/www.nytimes.com\/2012\/02\/19\/magazine\/shopping-habits.html?pagewanted=1&_r=1&hp<\/p>\n![\"\"](\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/04\/Target-Business-Model-300x218.png\")
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