Case studies

In 2007, Best Buy was the leading electronics retailer in the United States with more than 941 stores, revenue totaling $31 billion, and a market cap of $21 billion. In 2005, Best Buy had adopted a new business model, culture, and customer-segmentation template called Customer Centricity. This move created volatility in the price of Best Buy stock because of the higher-than-expected employee costs that went with this new way of doing business and the difficulty of executing the old and the new business models simultaneously while the new model was rolled out. Best Buy responded to Wall Street's short-term focus in a myriad of ways. It first asked for investor patience, and stressed the strong operating results achieved in Best Buy stores operating under the new model. But in June 2007, after the stock dropped again, the CEO knew he had to decide whether to open more Best Buy stores, increase the company's dividend, or increase the stock-repurchase program.

This short case could be handed out at the end of class discussion on “J&L Railroad” [UVA-F-1053] in preparation for the following class, or if students are more experienced with hedging and option pricing, the instructor may choose to cover both cases in a single class period. It is the companion case to “J&L Railroad” [UVA-F-1053], and presents more technical issues regarding the hedging problem by requiring students to understand option-pricing principles. The board likes the CFO's hedging recommendations, but it wants a more careful analysis of the bank's prices for its risk-management products: the caps and floors. Besides demanding an understanding of option pricing, this case puts particular emphasis on the calculation and use of implied volatility.

The Home Depot case is a great story. It's about entrepreneurship, growth, CEO leadership, and the dramatic impact, good and bad, a CEO can have on a company's growth culture, strategy, and performance. Home Depot had faced market growth challenges for the last seven years as it tried in numerous ways to reignite its growth engine. The case explores the growth strategies of CEOs Bernie Marcus, Arthur Blank, and Blank's successor Bob Nardelli, a former GE executive. After examining Home Depot's growth history, the case challenges students to devise a growth strategy for the company under a new CEO.

Tiffany & Company was the leading U.S. luxury jewelry brand, generating more than $2.6 billion in revenue through 167 retail outlets globally and from catalogue and Internet sales. For nearly 170 years, Tiffany had managed its brand. In February 2007, a hedge fund, Trian Fund Management LP, announced that it had bought a 5.5% stake in Tiffany, and became its largest shareholder. Trian believed that Tiffany was undervalued and stated that it wanted to help the company “improve its earnings per share by addressing various operational and strategic issues.” In response, Tiffany began to consider different actions to increase shareholder value.

TourAmerica is negotiating a master contract with Voyager Inn International (Bethesda) for hotel rooms during the 1995 tourist season. Issues under consideration include number of rooms during peak, mid-, and off-periods, room rates, breakfast prices, and the cost of ancillary services. While the hotel manager is evaluated on the basis of several criteria, including adjusted daily rates, occupancy rates, and food and beverage profitability, and is also provided with a utility scheme to facilitate trade-offs among the criteria, TourAmerica uses an effective cost per registrant (adjusted for intangibles). These two approaches provide an opportunity to contrast measurement schemes and to justify the use of utility functions. This case is a role-play exercise and must be used in conjunction with “Voyager Inn International” (UVA-QA-0463).

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM).

The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]).

The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.

This case describes the creation and performance of the America's Cup team and the leadership of Dennis Conner.

Set in 1999, this case allows students to put themselves in the positions of both Airbus and Boeing as Boeing considered how to respond to Airbus's decision to announce its plans to proceed or not with the $10 billion development of the world's first commercial superjumbo jet, the Airbus A3XX. Boeing was considering a development effort to “stretch” its 747 jumbo jet into a larger superjumbo version, the 747-X. At the time, the two companies’ widely available 20-year forecasts for jumbo- and superjumbo-jet demand were particularly divergent. In light of this very public “agreement to disagree,” Boeing could pursue several alternatives, all of which were related to Airbus's decision about whether or not to develop the A3XX. This case presents an opportunity for students to make a real downstream decision. It was prepared as a final exam for an introductory decision analysis course involving subjective probability assessment, decision tree modeling, simulation, real options, and game theory. In the analysis of this case, a student is expected to utilize ideas from all five of these areas.

Madison Kryswada, director of alumni relationships at State University, assembled a 123-school data set to explore, in her words, “the drivers of alumni giving rate. ” The alumni giving rate contributed 5% to the increasingly important U.S. News & World Report rankings of U.S. colleges and universities. Wanting to understand the relationship between this variable and school characteristics, Kryswada gave her assistant a list of four well-formed questions to answer.

The business-travel department of American Express is facing rapid growth in demand but is plagued with overstaffing in some offices because of the broad distribution of client demand. Management's challenge is to reduce costs in local offices while maintaining a high level of service. One alternative under consideration is a centralized regional business-travel center to handle reservation functions for up to 20 other Amexco offices. This case gives students the opportunity to apply queuing theory to a practical situation. Normally, in order to facilitate the numerous calculations required, it is used with the UVA “QUEUE” program.