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The decision maker is in charge of procurement auctions at the department of transportation of Orangia (a fictitious U.S. state). Students are asked to assist him in estimating the winning bids in various auctions concerning highway repair jobs using data on past auctions. The decision maker is faced with various professional, statistical, and ethical dilemmas.

To analyze highway procurement auctions from the buyer-auctioneer perspective, establish basic facts regarding the project price-to-estimated cost ratio, set up and estimate a structural regression model to predict the winning bid, and compute the probability the winning price will be below estimated cost. Difficulties include heteroskedasticity, logarithmic specification, and omitted variable bias. Also to estimate a Logit regression and predict bidder collusion probability.

Supplements the (A) case.

Wall Street hedge fund manager Kim Meyer is considering investing in an SFA (slate financing arrangement) in Hollywood. Dave Griffith, a Hollywood producer, is pitching for the investment and has conducted a broad analysis of recent movie data to determine the important drivers of a movie’s success. In order to convince Meyer to invest in an SFA, Griffith must anticipate possible questions to maximize his persuasiveness.

Students will analyze the factors driving a movie’s revenue using various statistical methods, including calculating point estimates, computing confidence intervals, conducting hypothesis tests, and developing regression models (in which they must both choose the relevant set of independent variables as well as determine an appropriate functional form for the regression equation). The case also requires the interpretation of the quantitative findings in the context of the application.

EuroPet S.A. was a multinational company operating gas stations in many European countries. There was a growing propensity for supermarkets to attach gas stations to their retail operations, which was developing into a major threat to EuroPet. As a result, in the mid-1990s, the company began to develop and brand its own convenience stores co-located with its gas stations. However, the company was spending much more on advertising the convenience stores than its competitors did. Management now had to decide if the increase in sales attributed to advertising efforts justified the advertising spend by analyzing the market data from one large metropolitan area: Marseille, France.

Students will learn: how to use cross-tabs and other marketing research tools to identify segmentation descriptors; how to analyze data and interpret results; and how these research results could guide new product development and positioning strategies in order to effectively target relevant customer segments.

After working through the case, the students should be able to understand Indian political economy and the brand building process of NaMo; identify the media mix strategies used to build the brand NaMo in India; evaluate possible future growth strategies for brand NaMo; and compare and contrast brand NaMo with business brands.

Narendra Modi popularly called as NaMo was the current Prime Minister of India. He belonged to Bhartiya Janata Party (BJP) which won India’s general elections in two consecutive terms 2014 and 2019. NaMo was recognised worldwide for his prudence in leading the country to greater heights of achievement. NaMo started his political journey as the worker of BJP at a tender age. His rise in political career was akin to flagship brand overtaking the parent brand. All the steps taken in the past to position himself as a cult brand, will it fortify to NaMo’s victory in 2024 general elections? Business firms may follow NaMo’s strategies. What can the business brands emulate from NaMo to market and position themselves? Can political success be transpired to business success?

This case is designed for use in a graduate-/postgraduate-level marketing course in segments on brand management, brand expansion and the marketing strategies of a market leader. The case can also be used in a brand management course to discuss brand management models (e.g. Keller’s brand resonance pyramid and brand value chain). This case has particular application for classes that focus on brand equity, STP for any brand (segmentation, targeting and positioning) and brand value chain. The case looks in detail at the Indian political market and brand building process of NaMo and examines competitive moves since its inception. This case can be used in brand management, media management courses. The dilemma can be explained as part of a marketing course for postgraduate and executive programmes.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS 8: Marketing.

An asset management company must replace the manager of its two signature mutual funds, who is about to retire. Two candidates have been short-listed. The management team is divided and cannot decide which of the two candidates would make the better mutual fund manager. The retiring manager presents a linear regression model to examine success factors of mutual fund managers. This linear regression is the starting point for the subsequent analysis.

Application of linear regression analysis to analyze the performance of mutual fund managers.

This case introduces a framework for cost modeling. Two data sets (one for injection-molded plastic parts and another for compressors) allow students to apply the cost-driver framework in conjunction with basic spreadsheet and regression analyses. Although obviously applicable in a course on supply chain management, the case can also be used to teach competitive cost analysis for strategic decision making.

During one of the most nerve-wracking football matches of the 2012–2013 Bundesliga season, life-long friends Franz Dully and Max Vogel begin arguing about whether the wealth of a football club determines its success during the season. In order to disprove Vogel's claim that “money scores goals,” Dully must analyze the Bundesliga's current market values, points earned, and mid-season leader data.

After analyzing the case, students will be able to compute prediction intervals, develop regression models, and interpret data. The development of the regression models asks students to choose the relevant set of independent variables, as well as determine an appropriate functional form for the regression equation. The models derived have to be evaluated as well as compared to one another. Further, the students have to interpret the quantitative findings in the context of the application.

Fred Bocock was examining the performance of the Energy Hedge Fund and the Energy Portfolio, a hedge fund and a mutual fund respectively, which he manages. Bocock had become increasingly aware that absolute returns or relative returns (returns relative to a benchmark) may not adequately capture his performance and some measure of risk-adjusted performance was necessary. The Dynamis Energy Hedge Fund extends the discussion of performance evaluation into the hedge fund arena. (See “Zeus Asset Management,” UVA-F-1232, for an examination of performance evaluation techniques in the mutual funds arena.) More broadly, the case engages students in discussions on what hedge funds are, what investment strategies they use, and who their investors are. Since the portfolio manager of Dynamis manages both an oil sector equity mutual fund and an oil sector hedge fund, the case allows for a comparison between a hedge fund and a mutual fund. Students should consider the pros and cons of evaluating the performance of the oil stock mutual fund against a number of oil sector stock indices as well as against a number of generic indices, such as the S&P 500 Index. The use of futures, options, shorts, and leverage by hedge funds makes it a lot more difficult to measure their performance. The case comes with a spreadsheet that contains data on the energy mutual fund, the Dynamis hedge fund, and several relevant indices.