1. This scenario shows how ethics can influence decision-making with forecasting. Put yourself in Katie’s position; how would you respond?

2. Select either the “tell the truth” or the “fix the data” position and defend your reasoning. Why do you believe that ethics does or does not factor into this dilemma? What would you say to someone who took the other position?

Katie is a brand new intern working for one of the biggest advertising firms in the Midwest, headquartered in Chicago. Getting this internship was a dream come true for her, as she grew up in the Chicago area and has always wanted to work in advertising. Additionally, the hiring rate for productive interns is very high, so this opportunity could lead to a full-time position. As a result, she is highly motivated to do a good job, get noticed, and, hopefully, receive a job offer.

Katie is the junior member of an advertising team that is meeting with a long-term client. The meeting is not going well; in fact, the client has serious reservations about continuing its relationship with the firm as a result of a recently failed advertising campaign the firm developed for the client. The client claims that the advertising was expensive and, in the words of its senior manager, “didn’t move the needle one bit. We are still at the same market share rate, so we’re out millions with nothing to show for it.” The meeting breaks for lunch, with the customer threatening to “end the relationship, unless you can give us something.” As the rest of the team leaves the room, Katie’s boss signals her to stay back.

Boss: “Katie, I hear that you’re pretty good with the statistical packages we use for customer data.”

Katie:“Yes, I’ve been using them for years now.”

Boss: “OK, I need you to run me some numbers to show that our ads are really working. Their flat sales could be a result of other issues, an industrywide pattern, or something else. Just find me something that I can use when they get back from lunch.”

Katie returns to her desk and works through her lunch break. The results generally are not good. To her best knowledge, the client’s sales really have not moved at all as a result of the advertising campaign. Nevertheless, she notices an interesting phenomenon. In the men’s age 18–35 demographic, she finds a big uptick in sales in the weeks right after the hockey playoff games were held in town. Although it is apparent to her that these sales increases are specifically related to those special events, she is pretty sure that she can smooth the data to show a general increase in sales. In fact, aggregating this data across all demographic groups could show some sales improvement that she could link to the advertising campaign, especially if no one on the client’s team is good with statistical models. Katie returns to the conference room 5 minutes before the client returns from lunch and meets up with her frantic boss.

Boss: “Well, what did you find for us?”

Katie: ….