Case studies

In 2008, Starbucks was in crisis as a result of undisciplined growth and loss of focus, and its stock declined almost 70%. In August of that year, Howard Schultz, the founder of Starbucks, came out of retirement to take over as the CEO. The company regained its footing by refocusing on its core and driving strong organic growth. By 2014, the stock price had reached $40, an all-time high. To prevent history from repeating itself, Schultz wanted to ensure that Starbucks' growth strategies not only addressed market opportunities, but also were aligned with the company's brand image, assets, and capabilities.

Starbucks announced a five-year growth plan in December 2014 with ambitious goals that included nearly doubling its revenues from $16 billion to $30 billion, doubling operating income, and expanding its footprint to more than 30,000 stores globally by 2019. The growth plan consisted of seven specific growth strategies, one of which was the New Occasions strategy. The objective of New Occasions was to drive growth by diversifying Starbucks' revenues beyond breakfast to the lunch, afternoon, and evening dayparts. Starbucks created specific offerings for each daypart, called the Lunch, Sunset, and Evenings programs. The case focuses on evaluating these three occasions-based growth opportunities and identifying the best path forward.

Polaris Battery Labs was an Oregon-based startup that provided innovation services to companies in the lithium ion battery industry. Its operating philosophy and expertise in this fast-growing industry enabled it to provide great value to its clients, but as a startup that was seeking growth the company was subject to multiple risks.

For Polaris, taking clients, developing new manufacturing capabilities to meet unproven battery technologies, and even extending credit to its clients posed real risk. Many of its clients were startups themselves and had a significant probability of failure. Others were established firms testing new and unproven battery technologies, many of which were unlikely to gain traction in the market.

The case examines how a technology-driven firm managed the risk of working with startups, claiming appropriate intellectual property, and developing a sustainable portfolio of clients.

Dr. Phillip Hoopes, a LASIK surgeon, is considering how to respond to discounting by his competitors. He had built a high-end practice with costly advanced technology, but discounters had recently entered the market and other high-end practices in the area were starting to offer deep promotions in an effort to compete.

In this case, students step into the shoes of a brand manager at UK-based online peer-to-peer lending company Zopa as she decides whether or not to recommend that the company undertake a brand redesign. Students are provided a brand design worksheet to help them understand the component parts of brand strategy and brand design and to help structure their assessment of Zopa's current brand. They are then challenged to consider if, to what extent, and how Zopa's brand design should change as a function of the company's brand strategy. Finally, they will learn a set of best practices, or guidelines, for approaching brand redesigns.

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.