MANAGEMENT DECISION CASE:

Using Information to Target Customers Who Do Not Know They Are Being Targeted

In late 2013, Target Corporation, one of the biggest retail organizations in America, announced one of the largest data security breaches in history, with tens of millions of credit cards compromised by system hack- ers. This case focuses on another interesting but less publicized event from the same time frame.

“If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know, can you do that?” This question is emblematic of similar questions that are asked in companies all over the world in an effort to more specifically identify customers for increased sales opportunities. In this case, the Target brass were hoping to identify buying patterns of their female cus- tomers that would allow for effective promotion of specific products such as maternity and baby items. As Target has learned, customer buying patterns are rep- resentative of their life stage and to the extent that an organization can identify those life stages through data mining and information analysis, greater sales opportu- nities are possible.

Target is one of many companies that have discovered the benefits of “big data” and “business analytics.” As individuals buy items, whether it is online or in a physical store location, they leave a data trail that can be collected, analyzed, and acted on by marketers in ways that were unheard of in the recent past. In Target’s case, when a customer pays for purchases with a debit or credit card, that customer is assigned a unique internal customer identification number. Monitoring those purchases over time allows company personnel to collect information and make inferences about the shopper’s lifestyle and family situation. For example, buying a small baseball glove, some animal crackers, and a DVD of the latest Pixar movie provides clues that a young boy may live in that household. With such information Target can provide coupons for age-appropriate clothing items, electronics, sporting goods, toys, and school sup- plies, thus increasing their chances to make more sales to that customer.

So, to answer the question posed in the opening of the case, it turns out that Target can identify its pregnant customers before those customers may want anyone to know they are pregnant! This point is illustrated best by a man who walked into a Target store clutching a coupon booklet demanding to see the store manager. That coupon booklet was addressed to the man’s daughter, who was in high school at the time, and it contained coupons for things like cribs and baby clothes. Upon being asked by the upset customer if Target was encouraging his daughter to get pregnant, the store manager apologized profusely for Target’s home office’s action. A few days later the store manager phoned the customer to apologize again and learned the customer’s daughter was actually pregnant, a fact that was revealed after the father returned from the store and spoke with her.

As enhanced computer processing power becomes more prevalent and more people are trained in the science and art of business analytics, there is no question that companies will utilize these data to increase company performance. In fact, Tar- get Corporation’s total revenues rose from $44 bil- lion in 2002 to over $70 billion in 2013, an increase attributed to Target’s “heightened focus on items and categories that appeal to specific segments such as mom and baby.” However, the ability for companies to analyze big data with sophisticated techniques is not without some risk, and given Target’s highly pub- licized security breach in late 2013 Target must be highly sensitive to the issue. The key is to balance the need ensure security and at the same time use data to enhance organizational success.

Question:

1. A good portion of Target’s success appears to be attributable to its internal market information system. What types of information from external sources might Target gather to augment its customer information and enhance analytical capability?

2. Aside from the downsides to using big data dis- cussed in the case, what other possible issues exist for companies implementing market research/ customer insight technology in this manner? As younger consumers in general continue to share more of their life online, how might companies of different types utilize that information to their advantage?

3. What “dark sides” of big data and business analytics should Target be paying attention to? What might happen to Target if they are insensitive to the potential for such processes to offend some customers?