One of the first e-tailers to offer sophisticated online shopping targeted to specific customers was Amazon.com. It seemed to know more things about you than you knew about yourself. Amazon truly was the trailblazer for putting web analytics into practice, having collected information longer and used it more proactively than any other e-tailer before (Associated Press, 2005).
Today, many e-tailers are mirroring Amazon by offering customized online shopping experiences by merging web analytics data with data acquired from other sources. Almost every major retailer, from grocery chains to investment banks to the U.S. Postal Service, has a predictive analytics department devoted to understanding not just consumers’ shopping habits but also their personal habits (Duhigg, 2012).
One such company that uses its customer data well is Target. For decades, Target has collected vast amounts of data on every customer who shops at it stores or buys something online. Whenever possible, Target assigns each customer a unique code — known internally as the Guest ID number — that keeps tabs on everything he/she buys (Duhigg, 2012). The Guest ID becomes a bucket that stores a history of everything customers buy and any demographic information Target has collected or bought from other sources (Hill, 2012). For example, if you shopped at Target, Target would know if you use a credit card or a coupon, fill out a survey, mail in a refund, contact the customer help line, visit the website, or open an e-mail Target sent you. All of those actions are recorded in your Guest ID (Duhigg, 2012). Also linked to your Guest ID is demographic information such as your age, whether you are married and have kids, which part of town you live in, how long it takes you to drive to the store, your estimated salary, whether you’ve moved recently, what credit cards you carry in your wallet and what websites you visit. Target also buys data about your ethnicity, job history, the magazines you read, if you’ve ever declared bankruptcy or got divorced, the year you bought (or lost) your house, where you went to college, what kinds of topics you talk about online, whether you prefer certain brands of products, your political leanings, reading habits, charitable giving and the number of cars you own (Duhigg, 2012).
Target uses this massive collection of data to provide more targeted marketing to its customers. Its marketing is so “on target” that Target recently came under-fire when it started sending coupons for baby products to a pregnant teenager before her father even knew she was pregnant (Hill, 2012). As it turns out, Target developed a very sophisticated predicative analysis program to determine when women who shop at Target become pregnant. The program allowed Target to reach pregnant women at a pivotal time in their lives before any other retailers know a baby is on the way (Duhigg, 2012).
To implement the program, Target analyzed the data it already had in it’s a baby-shower registry and observed how shopping habits changed as a woman approached her due date, which women on the registry had willingly disclosed (Duhigg, 2012). Next, they analyzed the data and some useful patterns emerged. It was observed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester. It was also noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. They also suddenly started to buy lots of scent-free soap, cotton balls, sanitizers and washcloths (Hill, 2012). These findings lead Target to assign each shopper a “pregnancy prediction” score based on if they had purchased some of the 25 products flagged. More importantly, Target could estimate her due date to within a small window, so coupons could be sent that were timed to very specific stages of her pregnancy (Duhigg, 2012; Hill, 2012).
Target has clearly demonstrated an ability to use its extensive collection of data to predict buyer behavior. It has a solid understanding that all data collected, no matter the source, needs to go into one bucket – in Target’s case, the Gust ID number. According to a web analytics expert, predicting behavior based on web data alone does not work because data collected on the web is mostly anonymous, usually incomplete, and is really unstructured (Kaushik, 2007). It is important for web data to be supplemented and merged with other data across multiple channels. Knowing all of the customer touch points both online and offline makes it easier to understand current behavior and predict future behavior (Kaushik, 2007).
According to the company website, Target (2012) is already using cookies, web beacons, and other technologies to receive and store certain types of information whenever customers interact with its website. Target uses this information, which includes the pages visited, which web address the customer came from, and the type of browser they are using, to customize website experiences for its customers (Target.com, 2012). However, Target could do a better job of customizing its online shopping experience by employing a strategy similar to Amazon whereby Target suggests products to customers that it thinks they will like.
References
Associated Press. (2005). Amazon knows who you are. Wired.com. [Web log post]. Retrieved July 3, 2012, from http://www.wired.com/techbiz/media/news/2005/03/67034
Duhigg, C. (2012, February 16). How companies learn your secrets. http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?_r=2&pagewanted=1&hp
Hill, K. (2012, February 28). How Target figured out a teen girl was pregnant before her father did. Forbes Magazine. Retrieved July 3, 2012, from http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/
Kaushik, A. (2007, September 11). Data Mining And Predictive Analytics On Web Data Works? Nyet! [Web log post].Retrieved July 4, 2012, from http://www.kaushik.net/avinash/data-mining-and-predictive-analytics-on-web-data-works-nyet/
Target.com. (2012, May 3). Privacy Policy. Retrieved July 4, 2012, from http://sites.target.com/site/en/spot/page.jsp?title=privacy_policy#collectedonline
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