Target's approach is everyday, ordinary statistics. They have a list of products that are purchased by expectant mothers and know the significance of each "factor" (ie product purchase) towards indicating pregnancy. These are used to calculate a percent chance that the buyer is pregnant. Once that percentage exceeds a human-chosen threshold, they start sending them coupons and advertisements for baby stuff.
The products which expectant mothers tend to buy are unscented lotions, soaps, sanitizers, washcloths, cotton balls and containers (like extra large purses). If someone starts buying these things for the first time or in increasing amounts, each instance is counted as a factor.
Answer from Tyler Durden on Stack ExchangeTarget's approach is everyday, ordinary statistics. They have a list of products that are purchased by expectant mothers and know the significance of each "factor" (ie product purchase) towards indicating pregnancy. These are used to calculate a percent chance that the buyer is pregnant. Once that percentage exceeds a human-chosen threshold, they start sending them coupons and advertisements for baby stuff.
The products which expectant mothers tend to buy are unscented lotions, soaps, sanitizers, washcloths, cotton balls and containers (like extra large purses). If someone starts buying these things for the first time or in increasing amounts, each instance is counted as a factor.
Target Inc's data science team developed a customer loyalty system using a machine learning technique called Bayesian classification network that represent conditional dependencies against sets of random variables. In this case Target developed a strategy to understand customer behaviour and shopping habits and encourage customers to try products similar to their usual brand purchases. Target's CRM data warehouse contain customer engagement key figures with a brand dimension are fed to Target's Customer Loyalty system to answer for the best method to communicate with customers for brand experiences. Target's CL system was able to mine the teenager's shopping baskets and segmented her as a potential pregnant mother, therefore sending her baby coupons which is her preferred communication method. This ML technique is also used in Google Mail to filter out spam mail.