Case studies

The organizers of a music festival may use video from the Friday concert to create a DVD to sell to those who come to the Saturday concert. Attendance on Saturday is uncertain, as is the percentage of those who attend on Saturday who will buy the DVD. Is this a good project? If so, what number of DVDs should be burned early Saturday morning and offered for sale at that evening’s performance? By that time, Friday attendance is known, as well as whether it rained on Friday, and there is a forecast for whether it will rain on Saturday. Historical information on these variables may help us predict Saturday attendance using multiple regression; together with the results of a marketing survey, such analysis will help us make better purchasing decisions. This case series (see also the B case, UVA-QA-0708) can be used to illuminate a multitude of concepts that are covered in basic decision-analysis courses. The series starts by examining the role of uncertainty in decision-making, proceeds through the estimation of probability distributions from sample data with multiple regression, culminates in the development of a full decision model, and ends with a qualitative and quantitative analysis (with a tornado diagram) of how to add value and reduce risk. Key pitfalls for students are failing to recognize both limits on sales (supply and demand), incomplete reasoning in the determination of the attendance probability distribution, and oversimplifying the full forecast model.

Designed to teach conjoint analysis, this case challenges students to make tactical decisions based on marketing research. It should be paired with the technical note “A Practical Guide to Conjoint Analysis” (UVA-M-0675) and that note’s accompanying supplemental PowerPoint file (UVA-M-0675TNP). A National Basketball Association franchise is struggling to increase attendance. It contracts a marketing research firm to conduct a conjoint analysis focusing on several aspects of its season ticket offerings with the hope that it can profitably improve its attractiveness.

Business Administration (Marketing).

MBA.

Although it has become fashionable to talk about how things business are changing at a nanosecond pace owing to hyper-competition, disruptive technologies and empowered consumers; the real change has been based on digital revolution and management of information. Most of the new introductions are entering a phase of facelessness from being innovative within a year of their appearance; whereas, as per one estimate, the breakeven volume is achieved after three years. This puts insurmountable financial pressure on marketing companies. In order to remain ahead of competition, they are introducing more and more new products in growth areas. In this paradoxical, complex situation; a reputed marketer in the pharmaceutical arena like Artichem entered a maturing market of Omeprazole whereas growth areas like Lansoprazole, Pantoprazole and Esomeprazole were still open to them. Did they make a mistake? Was it a bad idea to embark upon? Should they go for introducing new molecules even after a successful launch in the same segment?

The student shall be able to: explain the term “positioning” and shall be able to explain why he should go ahead with introducing a brand in an existing and maturing product category; explain the term “product life-cycle” and shall be able to take rational decision in the midst of pressing circumstances to manage a new product in a likely to decline market; and explain the term “new product development” and shall be able to apply the theories of new product development for brand success.

Nil.

This case is intended to be part of a first-year MBA marketing course or a second-year elective in advertising, integrated marketing communications, market research, or marketing analytics. It provides students with two real advertising experiments and the challenges involved in executing them. It allows for discussion of the need for advertising experiments, and, at a more general level, the need to measure the return on marketing. Biases surrounding the field experiments provide an opportunity for discussion about the problems with establishing a causal relationship between advertising and sales.

In the first case of a two-part series, a strategic finance manager at Wells Fargo with experience installing solar panel systems on bank branches crunches the numbers for a similar project in the Los Angeles area given the uncertain future of a government rebate.

This is the first implementation of Revenue Management System in a major international hotel chain in India. The case describes the history of development, corporate story of overdrive for profit, system integration issues. It describes two components of a revenue management system, forecasting and optimization. It also raises several questions that need to be addressed before implementing a RMS.

The Global Networks Company (GNC), headquartered in Boston, Massachusetts, made its global footprint in India in 1994 by establishing a presence in Bangalore. Although mainly a sales support office, GNC grew name recognition from its contracts with India’s government to help build nationwide networks. Not quite 20 years later, GNC decided to further invest in India and tapped a manager from the Boston office, Jim Notrika, to establish and then manage GNC’s first global software center in Mumbai. Split between Mumbai and Boston, the project team successfully completed several minor projects, but only months into its first major project, the team was struggling to meet deadlines. Blame was being passed in both directions, and when three talented engineers in Mumbai quit, Notrika makes an emergency trip to Mumbai to better understand the problem.

This case describes three common cross-cultural communication obstacles in teams: a preference for direct versus indirect confrontation of problems; a clash of collectivist versus individualistic cultural values related to reporting bad news or giving negative feedback; and different expectations of team leaders based on power-distance values.

Two recently graduated MBA students are tasked with developing an ad-serving learning algorithm for a mobile ad-serving company. The case illustrates the way in which hypotheses can be tested in an A/B format or “horse race” in order to establish customer preferences and superior profitability. The case was written for a course elective covering hypothesis testing.

Hundreds of thousands of would-be hoteliers have been popping up all around the world, hoping to rent their own homes and apartments to complete strangers through a service called Airbnb. The goal of Airbnb’s aspiring hosts was to use the company’s website to attract guests who were willing to pay the highest rates to stay in their homes for a short time. For Airbnb, the goal was to improve customer review performance so it could, in turn, increase profits. How could the company achieve its goal? Enter text mining, a technique that allowed businesses to scour Internet pages, decipher the meaning of groups of words, and assign the words a sentiment proxy through the use of a software package.

In order for text mining to be useful for Airbnb, its marketing professionals first had to gain access to customer review data on the company’s own website. The team then had to analyze the data to find ways to improve property performance. Was the team going to be able to leverage this large amount of data to determine a strategy going forward?