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 was designed to facilitate discussion of how a cyberattack was remediated by a major public university. Students are challenged to think through how to best manage the remediation project, including the application of best practices such as risk management, stakeholder management, communication plans, outsourcing/procurement management, and cyberattack remediation. The Phoenix Project was a success from multiple perspectives, and as such provides a useful example of how to manage an unplanned, mission-critical project well.

Faced with declining market share and sales, Natura, Brazil’s second-largest brand in the cosmetics, fragrances, and toiletries market, expanded its customer reach by moving from a direct-sales company to a multichannel company. In 2014, Natura added online catalogs, physical stores, and drugstores to its well-established direct-selling model, but the results were disappointing. Between 2014 and 2016, three different Natura CEOs attempted to lead the company in the strategic transition to focus less on the direct sales consultants and more on reaching the end consumers directly with multiple channels and touchpoints. In October 2016, the company’s board appointed its former commercial vice president, João Paulo Ferreira, as the most recent CEO. Ferreira’s challenge was to find the right balance between the direct-selling and other channel formats to market Natura, thus enabling it to thrive in the face of intense competition in the beauty and personal care market in Brazil.

At three o'clock in the morning on September 10, 2001, Thierry Hautillac, a risk arbitrageur, learns of the final agreement between Pinault-Printemps-Redoute SA (“PPR”) and LVMH Moët Hennessy Louis Vuitton SA (“LVMH”). After a contest for control of Gucci lasting over two years, PPR has emerged as the winner. PPR and LVMH have agreed for PPR to buy about half of LVMH's stock in Gucci for $94 per share, for Gucci to pay an extraordinary dividend of $7 per share, and for PPR to give a two and a half year put option with a strike price of $101.50 to the public shareholders in Gucci. The primary task for the student in this case is to recommend a course of action for Hautillac: should he sell his 2% holding of Gucci shares when the market opens, continue to hold his shares, or buy more shares? The student must estimate the risky arbitrage returns from each of these choices. As a basis for this decision, the student must value the terms of payment and consider what the Gucci stock price will do upon the market's open. The student must determine the intrinsic value of Gucci using a DCF model as well as information on peer firms and transactions. The student must consider potential synergies between Gucci and PPR and between Gucci and LVMH. The student must assess the likelihood of a higher bid, using analysis of price changes at earlier events in the contest for clues.

In early 2012, an equity analyst, was examining the jet fuel hedging strategy of JetBlue Airways for the coming year. Because airlines cross-hedged their jet fuel price risk using derivatives contracts on other oil products such as WTI and Brent crude oil, they were exposed to basis risk. In 2011, dislocations in the oil market led to a Brent-WTI premium wherein jet fuel started to move with Brent instead of WTI, as it traditionally did. Faced with hedging losses, several U.S. airlines started to change their hedging strategies, moving away from WTI. But others worried that the Brent-WTI premium might be a temporary phenomenon. For 2012, would JetBlue continue using WTI for its hedges, or would it switch to an alternative such as Brent?