MA Fall 2021 Final Project
Marketing Analytics Fall 2021 Final Project
Writing is an important part of any analysis work. Learning to convey your findings – both visually
(e.g., graphs) and with words – is important.
The final project requires each student to perform data analysis tasks/research of a marketing
data set selected by the student using SAS Studio tool. Each student will produce a Project
Proposal, a Project Presentation, and a Final Project Paper (which is a written report detailing the
analysis techniques and the findings of the project) with a SAS syntax file.
Data Set: The data set may be your own or may be obtained from an external source. The data set
must be related to marketing (e.g. consumers, profits, sales, costs, etc.) and there must be enough
data to perform necessary analyses. Here are some links where you may find good datasets:
Analyses: You must use SAS Studio that we learn and practice in this course to conduct all your
analyses. In your analyses, you should cover at least three of the analyses topics we talk about in
this course. However, covering 4 to 5 topics is recommended in order to gain excellent points in
Completeness and Thoroughness (Please see GRADING at the last page). They include:
• Data preprocessing: how to clean, aggregate, match the raw data sets and how to
transform, clean different variables
• Descriptive analysis: summary/descriptive statistics for your data
• Data visualization: different tables, charts, graphs to help audience better understand your
analyses and your findings
• Statistical analysis: hypothesis testing with assumptions and limitations, testing for
differences between groups and for predictive relationships
• Predictive analysis: predictive models (such as linear regression) with their assumptions and
Paper: The paper should not be too lengthy but needs to be long enough so that I understand what
the data and analyses are, the conclusions you make, and know how you arrived at the conclusions.
6 to 12 pages (DOUBLE spaced, font size 12, excluding visualizations, dataset, code/spreadsheets
and other attachments or reference material) should be enough.
SAS syntax file: When you submit your final project paper, you should also submit a SAS syntax file
called “YourLastName_sassyntax” separately, which include the codes/syntax you generate/use to
conduct your project. If you fail to submit this file or the syntax could not reflect the work you have
done for your project paper, you may receive a failing score for your project.
Citation format: You must include proper citation in both final project presentation and paper. You
are allowed to use the citation formatting that you prefer for this project. Your paper will be
checked by NYU Turnitin tool for plagiarism. If you fail to use proper citation, or the paper you
submit contain more than 30% exact wording from other sources, you may receive a failing score
for your project.
MA Fall 2021 Final Project
1. Final Project Proposal – Due Date: Wednesday, 11/10/2020, 6pm
You will need to turn in a 1-page paper (SINGLE spaced) proposal (Word/PDF) called
“YourLastName_proposal”. The proposal should have the following 5 headers:
• Background: 1 paragraph giving the overall problem
• Purpose: 1 paragraph that begins: “In this paper, …” (Provide the goals of what you will
accomplish in the paper and how.)
• Data: 1 paragraph telling the data source, and important features of the data (links to the
data set, sample size, who was sampled, year collected, covariates you will use)
• Analysis plan: 1-2 paragraphs with your analysis plan.
• Discussion: 1 paragraph on how your results will help answer the question/problem you
posed in “purpose.”
2. Final Project Presentation – Due/Present Date: Wednesday, 12/8/2020, 6pm
1) Slides: You will need to put together slides for your presentation and submit it on the
presentation date by 6 pm (called it “YourLastName_presentation”). You can use
whatever program you prefer (e.g., PowerPoint, Latex), but your focus should be on
clarity and being concise, not on presenting everything.
2) Presentation: You will present your slides to the class. You will need to maintain eye
contact with those in the audience, describe verbally what you did well, come across
professionally, and answer questions appropriately. The presentation should be no
shorter than 5 minutes and no longer than 15 minutes.
3. Final Project Paper + SAS syntax file – Due Date: Thursday, 12/16/2020, 11:59pm
You will need to turn in your paper (DOUBLE spaced, font size 12, Word/PDF) called
“YourLastName_paper”. I have offered one way to organize your paper below, but feel free to do
what works best for you. You need to make sure that the organization is easy to follow.
• Introduction/background—A brief (approximately one page) general description of the
o Why the problem is of interest—you might refer to previous studies using this, or similar
o A brief summary of the methods utilized—tell me what methods you have utilized, e.g.,
“I perform a significance hypothesis testing to examine the differences of cereals across
the shelf on which they are displayed.” Detailed information about the methods should
be left for a later section.
o The main results of your analysis.
MA Fall 2021 Final Project
• Data/Data Preprocessing:
o Information about the data set (e.g, the source of the data, how many observations,
number of variables, etc.)
o If you make any change to the raw data set, you should describe how you change it and
why you change it as part of data preprocessing (e.g. you delete the observations with
missing values, you select random samples in the data set, etc.)
• Exploratory analyses —This section should provide the reader (me) with graphical and
numerical summaries and results of the data, paying special attention to summaries that
provide evidence for the results you’ve mentioned in the introduction.
o Descriptive statistics of the variables you are interested in
o Data visualizations of the variables you are interested in
o You should explain both the processes and the meanings of conducting the descriptive
statistics and visualizations
• Methods —The methods section should expand the description of the methods used. Topics
that should be covered in this section include:
o If appropriate, explicitly define any tests/models used in your analysis (e.g., significance
tests, linear regression, etc.). Make sure to state why you think the method(s) is (are)
appropriate for the data.
o Discuss and evaluate the assumptions of the method(s) used. If you’re data does not
quite conform to the assumptions, make note of it, and discuss the implications.
o State the hypotheses you are testing and also state which testing procedure(s) you are
using. For example, “I perform a hypothesis test to determine whether the mean
response vector varies by display shelf, with the p-value statistic.”
• Detailed Results—This section expands the explanation of the results, and includes, where
appropriate, tables and figures providing evidence for the conclusions you’ve stated. You can
also report any secondary results you’ve found.
• Discussion—Summarize the findings one last time, paying close attention to the limitations of
the analysis. You can share thoughts with the reader about how you might expand the study,
improve on the model you’ve used, and what are the long-term implications of the findings.
MA Fall 2021 Final Project
1. Final Project Proposal (1% of course grade – 100-points scale in Discussion Section 8%)
• Total 5 headers, 20 points of each header’s contents
2. Final Project Presentation (10% of course grade – 100-points scale)
1) Slides, 50 points:
• Clarity and conciseness
• Completeness: do the slides reflect the whole story for the project
• Proper Citation and Reference
2) Presentation, 50 points:
• Interaction: eye contact
• Clarity of delivery: easiness to follow, sounds, speed
3. Final Project Paper + SAS syntax file (20% of course grade – 100-points scale)
1) Clarity, 15 points. If I scratch my head and ask myself, “what the heck are they trying to
say?” several times when reading the paper, then its probably not very clear. Delete
long sentences with complex structure in favor of ones that are relatively short and easy
2) Appropriateness of the data and analyses, 30 points. This is the most important piece
of the project. When evaluating this portion of the project I will be asking myself, “Is this
what I would have done?” “Is there a better way to perform this analysis or data
3) Ability to explain the results and draw the correct conclusions, 25 points. So you’ve
used the right method(s) for data analysis. Did you explain the results well and clearly?
Did you use that method correctly to draw conclusions?
4) Completeness and Thoroughness, 25 points. The project should include at least three
(4-5 topics preferably) of the analyses topics we talk about in this course. Also, the
paper should have proper citation inside the texts, appendix, and references. Did you
perform a complete analysis? Was an adequate exploratory data analysis performed? Is
there something in the data that you failed to discuss? Were all of the model
assumptions discussed and evaluated? Were limitations of the method(s) discussed?
5) The wow factor, 5 points. Extremely well-written papers will be rewarded. Also, if you
use SAS Studio to conduct any task/analysis/research beyond what we learned in the
class, you will be rewarded. For example, you find some new SAS codes we did not talk
in class to conduct additional related analysis, etc. Did the student go beyond the call of
duty in the analysis? Is the paper extremely well-written? Did the student suggest ways
to extend the work or how the analysis could be improved?