Target: The Challenge of Data Mining

Andrew Pole began working for Target as a statistician in 2002. His job was to improve effectiveness of Target's promotions by statistically analyzing information on customer purchasing patterns and demographic characteristics. The technique was often referred to as data mining. One of his tasks was predicting whether a woman was pregnant through her purchasing patterns and demographic profile. Marketing could then target these women with information on products for prenatal care and infant needs. All was going well until an irate father asked to see manager in a Target store outside of Minneapolis. Waiving a group of coupons for clothes and cribs that had been sent to his high school age daughter, father fumed, Are you trying to encourage her to get pregnant?(Hill, 2012). Was Target's utilization of customer data in this ethical, were mistakes more damaging than value of successes, and was this an invasion of privacy? These were questions that Target management had to resolve. Target From a single store in Roseville, MN, Target grew to over 1763 Target and Super Target stores by 2011. Target was second largest discount retailer in US and saw both sales and net earnings grow between 2006 and 2011. (Target, 2012). Target catered to a similar moneysaving market as Wal-Mart, but offered a very different value proposition. Target focused on different capabilities and a different product portfolio, including: * Target's way to emphasized design-forward apparel and home decor for image-conscious consumers. Store layout and advertising focused on an eye for style. * Its capabilities system supported this to play with image advertising, mass prestige sourcing (with use of private brand and exclusive offerings), pricing, and management of urban locations. * Target satisfied needs of its younger, image-conscious shoppers by stocking more furniture, clothing and exclusive designer merchandise than Wal-Mart. Gregg Steinhafel, Target's president, boasted to investors that heightened focus on items and categories that appeal to specific guest segments such as mom and baby (Target, 2012) were responsible for these successes. The segment focus relied heavily upon ability to determine customer purchasing patterns through prior purchase behaviors and other demographic data. Target was one of first major retailers to use predictive modeling (sophisticated data mining techniques) to identify customer segments and differentially market to those segments. Consumer Behavior: Why Does Data Mining Work It has long been a working assumption in psychology that one of tendencies of human behavior was habituation (Crossley, 2001). One of founding fathers of psychology, William James, described habit as sequences of behaviors, usually simple.... that have become virtually automatic (James, 1890). With automaticity at its core, habituation was ideal in creating repetition of useful behaviors that ultimately required less mental exertion or effort to maintain. In fact, James suggested the more of details of our daily life we can hand over to.... automatism, more our mind will be set free ... (p.122). Habits were acquired through gradual strengthening of a learned association between a situation (cue) and a routine action in a consistent context. In formation of a habit, control of behavior transfer to cues in environment. This transfer of behavioral control to environmental cues increase automaticity with which behavior was performed when situation was encountered again (Verplanken, 2006; Wood & Neal, 2007). From behavioral perspective, habit strength was considered to be a function of repetition only when rewards were received for performing behavior upon encountering a cue (Hull, 1943; 1951). Identifying three-part process (cue, routine, reward) of shopping habits of consumers allowed for retailers to market and exploit habitual purchasing behavior of its consumers, all seemingly without consumers' knowledge. …

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