The case study will consist of 2-3 pages written in APA format, double-spaced with no more than 12 Font Times New Roman. Additional references can be used, but the text must be included as the primary reference. Please cite each source inside the writing to give credit to the author (In-Text Citation).
Case Study Analysis
Before you begin writing, follow these guidelines to help you prepare and understand the case study:
Read and examine the case thoroughly
Take notes, highlight relevant facts, underline key problems.
Focus your analysis
Identify two to five key problems
Why do they exist?
How do they impact the organization?
Who is responsible for them?
Uncover possible solutions
Review course readings, discussions, outside research, your experience.
Select the best solution
Consider strong supporting evidence, pros, and cons: is this solution realistic?
Drafting the Case
Once you have gathered the necessary information, a draft of your analysis should include these sections:
Introduction
Identify the key problems and issues in the case study.
Formulate and include a thesis statement, summarizing the outcome of your analysis in 1–2 sentences.
Background
Set the scene: background information, relevant facts, and the most important issues.
Demonstrate that you have researched the problems in this case study.
Alternatives
Outline possible alternatives (not necessarily all of them)
Explain why alternatives were rejected
Constraints/reasons
Why are alternatives not possible at this time?
Proposed Solution
Provide one specific and realistic solution
Explain why this solution was chosen
Support this solution with solid evidence
Concepts from class (text readings, discussions, lectures)
Outside research
Personal experience (anecdotes)
Recommendations
Determine and discuss specific strategies for accomplishing the proposed solution.
If applicable, recommend further action to resolve some of the issues
What should be done and who should do it?
Finalizing the Case
After you have composed the first draft of your case study analysis, read through it to check for any gaps or inconsistencies in content or structure: Is your thesis statement clear and direct? Have you provided solid evidence? Is any component from the analysis missing?
BMW’s Road to Higher Customer Satisfaction: Just Tell Me What You Think!
Customer satisfaction and loyalty are critical elements in any successful marketing strategy, but they are essential when the product is purchased only once every few years. It was, therefore, of great concern to luxury automakers like Acura, Audi, and BMW when in 2014 their customer satisfaction scores all dropped below the average for the entire automobile industry.34 For years these companies had been gathering and using external primary data from their own surveys, and also relying on secondary data from sources like the American Consumer Satisfaction Index, yet satisfaction was falling. Assuming the luxury automakers were listening and responding to the data they received from existing research, it appeared the old modes of research were missing key insights. Management had a new research deliverable—learn how we can improve customer satisfaction—and a new research question—what specific product attributes or service components are not satisfying our customers? In situations like this, you don’t know what you don’t know. Were less-expensive competitors adding features that satisfied customers at a lower price? Were service amenities like free loaner cars not valued by the customers? What were the features or service components that were failing, missing, or just not valued? Finding a method to ask questions that exposed areas of dissatisfaction (or missed opportunities to satisfy), and ask these questions in a way that was representative of the target customers, became the market research problem. One of these companies—BMW—learned how to listen and greatly improved its customer satisfaction scores. It now ranks above the industry average for customer satisfaction and higher in satisfaction than its luxury automobile competitors.35 BMW’s focus on understanding its customers has helped it reach the top of its market, but its path was only possible with an understanding of important trade-offs different types of marketing research require. Through a worldwide dealer network, BMW makes 10 promises to its customers and then measures how well the company (and dealers) keep those promises. Walk into (or visit on the web) nearly any BWM dealership, from Idaho Falls, Idaho, to Hastings, New Zealand, and you will see a display of the 10 promises of service quality.36 What you don’t see is how difficult it is to measure and make sense of hundreds, thousands, or, at a country level, hundreds of thousands of opinions about customers’ satisfaction with activities such as response timeliness, explaining the bill, or offering a test drive. In an effort to gather better satisfaction information, BMW UK set about to solve two problems at once. Like most companies, it had a difficult time getting customers to respond to its surveys.37 And, with 10 promises to measure, the surveys were fairly long and time-consuming to complete. This led to a falloff in customer response rates. The second problem was making sense of the incoming survey data. To make surveys easier for respondents and for later analysis, market researchers use closed-ended quantitative questions, typically with a one-to-five- or one-to-seven-point scale of very unsatisfied to very satisfied. Customers who responded answered by bubbling in the appropriate number on their survey. But, without context as to why they were very satisfied (or not), it was difficult for BMW to know exactly what to keep doing and what to fix. More context meant more questions, and more questions meant fewer responses. To address this problem and get deeper context, market researchers often seek to gather deeper, qualitative information through in-depth interviews, which are often one on one and quite expensive to administer, or by inviting a small number of customers (usually around a dozen) to a focus group, where a professional moderator asks open-ended qualitative questions, digging out nuances of why customers are expressing a given level of satisfaction. One downside of both in-depth interviews and focus groups is that they are not a statistically valid representation of the population of interest (for example, a target segment such as Lexus buyers). BMW recognized that focus groups enable the company to gain deeper context and actionable insights through qualitative data but do not allow for generalizing the findings to a large group, so they only provide guidance on what should be focused on in a broader, statistically valid study. BMW faced what is, for all market researchers, a series of trade-offs between different research methods. Qualitative methods, such as focus groups, brought deeper insights, but at the cost of being able to apply the findings to a more general customer target. Other methods, such as quantitative surveys, brought speed, generalizability, and a higher level of numerical precision and statistical validity, but lacked an ability to delve deeper and understand the heart of what customers value. And, in both cases, the more information BMW sought, the fewer customers responded. Realizing a need for richer and more representative information, BMW set out to find a way to analyze representative Page 112 samples of large amounts of open-ended qualitative customer responses.
