Marketing Analytics description paper

BUAD-476 Marketing Analytics
Spring 2021
DIRECTIONS: Read the description and then thoroughly yet concisely address the following questions. Your submission should have two parts: a report, and an R appendix (R Script file). The report should be typewritten in standard formatting and be no longer than three single-spaced pages (not including a title page). The structure of the report is up to you, but should be written in paragraph form (do not list question numbers!) and will likely include at least three parts: an executive summary describing your objectives and main results; a methodology section describing your data, documenting your choice of methods, and justifying any changes to the data (e.g., transforming variables); and an analysis of findings in which you interpret your results. Your R Script should clearly indicate which lines of code are intended to answer which questions, and include only a list of commands (no Console output). I should be able to run your Script in its entirety. An R Script template is available to get you started.
Each question is worth four points. Two points are earned by appropriately selecting and applying R commands to generate output that can be used to answer the question. Two points are earned by interpreting that output in the report, using clear and concise language and structure, and formatting your results in an appealing and legible way (e.g., appropriate labels for graphs). There may be more than one way to answer each question: document and justify any choices you make. Use only methods we have discussed in class. Partial credit will be awarded.
DUE DATE: Thursday, April 22, 2021, 11:59pm
You have been hired as a marketing analyst for Kirin USA and are interested in repositioning the brand in the U.S. import beer market. The data file called kirindata.csv includes the following variables:
– brand: The brand of beer for which a respondent has rated his/her/their brand perceptions. Brands include Amstel Light, Bass, Becks, Corona, Dos Equis, Heineken, Kirin, Molson, Moosehead, Sapporo, and St. Pauli Girl.
– rich.full.taste: The brand has a “rich, full-bodied taste”.
– good.taste: The brand has “a good taste”.
– no.aftertaste: The brand leaves “no aftertaste”.
– refreshing: The brand is “refreshing”.
– light: The brand is “a light beer”.
– prestigious: The brand is “prestigious and growing in popularity”.
– masculine: The brand represents “a masculine beer”.
– brew.tradition: The brand is “from a country with a brewing tradition”.
– youthful: The brand is “for young people”.
– The brand “gives you a ‘buzz’”.
– drink.out: The brand is good “to drink at picnics and outings”.
– The brand is good “to drink at a bar”.
– drink.friends: The brand is good “to drink with close friends”.
– The brand is good “to treat yourself at home after work”.
– serve.guests: The brand is good “to serve to dinner guests at home”.
– lower.price: The brand is “lower than average price” for beers.
– good.value: The brand is “good value for the money”.
(Note: This is real ratings data from a brand perceptions survey given to customers and non-customers of Kirin USA. I uploaded a script file called Proj3Template.R to Canvas which contains some lines of code
that you should run prior to starting your analysis. It also contains a template for how to organize the code you write in order to answer the follow questions.)
As the analyst who obtained this data, you are interested in creating a perceptual map of Kirin USA in the U.S. import beer market to understand how to better position—or reposition—the Kirin brand in this market. The following questions guide you through the analysis.
1. Summarize the data. Should the ratings data be rescaled? Why? Rescale the data if you believe the data should be rescaled. Plot a correlation matrix for the perceptual adjective ratings—approximately how many factors does the correlation plot imply? Aggregate the average of each perceptual adjective rating by brand and then plot a heat map for the average perceptual adjective ratings by brand. What relationships do you observe in the heat map?
2. Extract the principal components in the ratings data using principal components analysis (PCA). How many components explain most of the variance in the data? Visualize. Generate a biplot of the brands for the first two principal components using the principal components from the average perceptual adjective ratings. Interpret the perpetual map.
3. Consider an exploratory factor analysis (EFA) for the perceptual adjective ratings. How many factors do you recommend extracting? Find the exploratory factor analysis solution for the ratings data with an appropriate number of factors and rotation; be sure to explain your selection of factor rotation. Draw a heat map of the rotation factor loadings and a path diagram for your preferred solution. Plot the average Bartlett factor scores by brand—to what extent does the Bartlett factor score heat map compare similarly to your results and interpretations from the previous question?
4. Plot a multidimensional scaling (MDS) map for the brands using the average perceptual adjective ratings. Which brands are most similar (different)? To what extent does your new perceptual map compare similarly to your results and interpretations from the previous two questions?
5. What is your preferred method for reducing complexity in the brand ratings? Why? What is your recommendation to Kirin USA regarding a repositioning strategy? In stating your managerial recommendation, be sure to include key shortcomings of the models you investigated. (Hint: Be specific about your repositioning recommendation—do you want to reposition Kirin closer to another import beer, do you want to move its brand toward a gap in the market, etc.? Be sure to show how you can achieve your repositioning strategy.)

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