Case studies

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.

Uber China is a strategy pricing case that examines the role of customer acquisition tactics and brand positioning in entering the tantalizingly large Chinese market. The case adopts the perspective of an outside observer looking at Uber's efforts to compete in China from its entry in 2013 to its exit through its acquisition by Didi Chuxing, the highly dominant industry leader in China's ride-sharing market. After laying out the market opportunity, consumer and competitive landscape, and the various acquisition-related moves of Uber and the other major players, the case asks students to conduct a postmortem on Uber's failure in China. Specifically, they must consider what drew Uber to the opportunity in China and what it might have done differently in terms of positioning and customer acquisition to compete more effectively. First and foremost a pricing-related discussion, the case illustrates the relationship between pricing and acquisition tactics and brand positioning and the use of both in market entry and penetration.

This case features Lucas Lopez Lince, a rising leader in Colombian consumer goods company Grupo Familia. Lopez Lince had inherited a digital marketing program built around a newly identified customer persona–a young low-income mother. The program represented a significant shift in Familia's target customer as well as a shift in the way marketing funds were spent. The program appeared to be working, so he increased the digital spend even as he had to reduce his overall marketing budget in the face of challenging economic headwinds. Activity metrics such as page views, social media “likes,” and video views rose dramatically, and at the same time sales began to rise again. By the beginning of 2017, Lopez Lince faced a deeper set of questions. How could he be certain that the rising sales were due to the digital efforts? Would the existing digital programs and tactics continue to deliver against chosen metrics? And what could he do next in order to continue driving growth of revenue and margin through digital efforts? Students assume the role of Lopez Lince and are asked to apply concepts such as customer personas, micro-moments, and customer journey mapping to develop their own point of view on what they would do next.

Jennifer McDougall is considering investing in mutual funds for the first time, and has narrowed her options down to three: one that is domiciled in Germany, and two that are domiciled in Luxembourg. As a cautious and risk-averse investor, Jennifer has done extensive research on the three funds, and has come across a curious fact: the beta of the German fund is surprisingly low. After speaking to her financial planner, she learns there is no legal requirement in Germany for mutual funds to compute net asset values at a particular time of the day. If the German fund is closing its books in the middle of the day and its net asset values reflect its midday holdings, rather than end-of-day holdings, this could explain the low beta. Thus, the German fund might appear less risky, without actually being so. Jennifer needed to get a clearer picture of what was going on before making her decision.

Using the data provided with the case, students will determine the closing time of the three funds and how that affects the beta of each. Then they must make a recommendation about which fund would be the best investment for Jennifer.

In early December 2013, Roxann Biller, Associate at the Chicago-based private equity firm Delta Quantitative Real Estate Capital, was asked to assess the risk associated with the firm's first potential overseas investment. Haifu Sentā Gendaino (HSG) was a large multi-tenant logistics property located in the Gaikando area of Tokyo. High-quality tenants currently occupied the property, so at first glance the risks of investing in the property seemed minimal. However, Biller knew that she had to consider the potential drawbacks. This would mean gaining a better understanding of each tenant, trying to forecast the future condition of the Tokyo logistics market, and considering what new risks her firm would face because the property's cash flows were in a foreign currency.