MKTG 352: Marketing Analytics Master Syllabus
Representative Syllabus
Students are responsible for being familiar with and following the instructions and policies in this document.
Course Instructor:
Office Number:
Office Hours:
Email:
Course Meeting Times:
Course Overview
As the marketing discipline evolves, firms are becoming overwhelmed with an increasing number of analysis tools, processes, and research techniques for evaluating business phenomena and implementing new marketing strategies. However, this evolution offers little overall guidance on when to use various frameworks, how they work, which ones are the most valuable, or how they all fit together. Managers are looking for talent who have taken relevant courses (e.g., Marketing Analytics) and can use the right analysis tools to solve business problems. There has been significant development in terms of data and tools. However, making meaningful inferences from data depends not only on the tools themselves but also on using them in an appropriate context.
In today’s data-rich environment, every firm must know what drives its success. Additionally, a firm should identify who its customers are, how to reach them, what their expectations are, and how to keep them satisfied. A firm should also track its competitors and develop strategies that help improve its performance. Finally, a firm has only limited cash flow, human resources, knowledge, etc. When designing and implementing marketing strategies, it should also consider which approaches are worth the firm’s time and investment to implement. These considerations lead to four underlying “problems” or “complexities” that managers and marketers should focus on:
(1) All customers differ.
(2) All customers change.
(3) All competitors react.
(4) All resources are limited.
We refer to these as the “First Principles” of marketing strategy. A careful understanding of data and proper inference from it can help a firm achieve these objectives. To understand data and draw inferences from it, one should know concepts related to data, have exposure to the context, and be able to use various statistical tools. In this course, we will discuss several critical concepts of marketing, learn how to use data and analytical tools to make inferences, and develop effective marketing strategies. You will learn how to measure customer preferences, recognize different ways to segment markets, identify attractive customers to target, determine the best marketing mix, and develop new products that add value.
Course Objectives
This course is about data-driven marketing strategy, data handling and management techniques, and the use of statistical software to estimate marketing models. It is also a project-based course focused on marketing decision-making. You will be introduced to real and masked marketing problems that firms face. You will then be exposed to the context, available tools, and data needed to solve these problems. Finally, you will learn how to make inferences from data for effective marketing decision-making. We will use the “problem-concept-data-tools-inference-strategies” sequence for each class and topic, and you will repeat the “data-tools-inference-strategies” sequence in classroom exercises for better understanding.
The class will follow a comprehensive approach that includes lectures, course materials, reference materials, and hands-on practices. We will use Excel and R (RStudio) software for all our analyses. R is a programming language for statistical computing. It is also a powerful tool for collecting, cleaning, and analyzing data. The R software is opensource and free, attracting significant attention in the industry in recent years. RStudio integrates with R as an IDE (Integrated Development Environment) to provide further functionality. You can download both from their respective websites (both are free). You can find step-by-step instructions at the links below:
By completing this course, you will learn to describe data, explore relationships between marketing variables, understand what makes consumers prefer certain options over others, segment customers based on observed information, target customers, develop marketing mix strategies, and recommend how a firm can improve its performance. From a learning perspective, the course will emphasize:
- Understanding marketing issues faced by firms.
- Using and executing data analytic tools and techniques and making meaningful inferences for effective decision making.
- Using statistical software to estimate various marketing models.
- Applying your learning through a marketing analytics project.
Course Materials
There is no required textbook for this course. I will be providing various reference materials for a better understanding of marketing concept and R coding. However, the following books may be helpful to enhance your understanding:
- Palmatier, Robert W. and Shrihari Sridhar (2017). “Marketing Strategy: Based on First Principles and Data Analytics”, Palgrave MacMillan.
- Chapman, Chris and Elea McDonnell Feit (2015) “R for marketing research and analytics”, Springer
Other readings may be assigned throughout the semester and posted on Canvas.
Students Software Download Information
Students may download free Windows software for classes OR may use the free Citrix Virtual lab to access Microsoft Access, Project, Visio, and Visual Studio as well as Visual Studio for Mac. The latter is a good option for students who want quick access without downloads or installations. Please use the Citrix Lab if you are experiencing installation problems.
- **Easiest Option for All Users**:
- Citrix Virtual Lab (Virtual Desktop) - No Registration or Downloads Needed. Tutorial Video Below.
Virtual Computing Services
How to Launch a Virtual Session in Patriot Virtual Computing & Labs
How to Use the Citrix Virtual Lab (Video): GMU Citrix Virtual Lab Tutorial.mp4
- Citrix Virtual Lab (Virtual Desktop) - No Registration or Downloads Needed. Tutorial Video Below.
- All Windows Users & Mac Users Who Installed VMWare:
- Azure for Windows/Windows applications: ***NOTE ABOUT SOFTWARE KEYS: After selecting the software for download, see “View Key” button to the right. Double-click to see Product Key***
- Mac Users Only. Ensure Your Mac Can Support This Option First. Use the Citrix Virtual Lab If Not:
- VMWare is required to create a virtual Windows machine and download Windows software for class. You can download VMware for free with this link. A license is no longer required.
Email Communications
Course Communication
All course communication will take place through Canvas or George Mason email addresses. Any emails from me about this course will either be sent through Canvas or from my George Mason email address.
Announcements/Reminders
Announcements regarding this course will be posted on and/or emailed via Canvas. It is your responsibility to check Canvas and your George Mason email regularly to stay updated with this course.
Email Correspondence - George Mason Email Addresses Only
E-mail is the preferred method of contact regarding any questions about this course. When you email: You MUST use your George Mason e-mail address. Federal privacy laws state that I am not allowed to provide confidential information to any private non-George Mason e-mail addresses. I will not respond to messages sent from or send messages to a non-George Mason email address.
E-mail Correspondence – “MKTG 352 – Section 001/002” in the Subject Line
In order to prioritize responding to e-mails sent regarding this class, please use “MKTG 352 – Section 001 (or 002)” as part of the subject line of any emails you send to me.
Estimated E-mail Reply Times
Under normal circumstances, you should receive a reply to your e-mail within 24 hours during the week (Monday-Friday) and up to 48 hours on weekends (Saturday/Sunday). An email sent 5PM or later will not be seen until the following day/next weekday. If you do not receive a response within 48 hours, check for any technical issues and follow-up with me. Please plan for enough time when emailing to receive a response.
Course Grading Policy & Assignments
Grading
Your grade will be determined by the total points that you earn on each of the graded assignments in this course. You may earn up to 1000 points. To determine your final course letter grade, compare the total points you earned on your graded assignments with the scale below.
Final course letter grades will be assigned based on the following scale:
| Grades will be assigned as follows | |
|---|---|
| Letter Grades | Points Earned |
| A+ | 97.00-100.00 |
| A | 94.00-96.99 |
| A- | 90.00-93.99 |
| B+ | 87.00-89.99 |
| B | 83.00-86.99 |
| B- | 80.00-82.99 |
| C+ | 77.00-79.99 |
| C | 70.00-76.99 |
| D | 60.00-69.99 |
| F | 00.00-59.99 |
This course requires a minimum grade of a C to satisfy Costello College of Business degree requirements. Students will not be permitted to make more than three attempts to achieve a C or higher in this course. If you have questions about this policy, please talk with an academic advisor.
Note on Final Course Grades: Final grades are not negotiated. No adjustments will be made to final course grades on an individual basis. Do not ask for your individual final grade to be “rounded up”.
Course Assignments
The graded course assignments in the class will have the following point values:
| Course Assignments | |
|---|---|
| Exams | |
| Midterm Exam 1 | 20 Points |
| Assignments | |
| Individual Assignment 1 | 10 Points |
| Individual Assignment 2 | 10 Points |
| Group Assignment 3 | 10 Points |
| Group Assignment 4 | 10 Points |
| Course Project | |
| Final Project | 25 Points |
| Case Spotlight | |
| Mini Case Presentation & Discussion Lead | 5 Points |
| Participation | |
| General Class Participation | 10 Points |
| Extra Credit | 5 Points |
| Total (Maximum) | 100 Points |
Details on Course Assignments
A. Mid-Term Exam
There will be a mid-term exam given during scheduled class times. The exam will involve short answer questions, data analyses, and write-up about specific tools/concepts discussed in the class. The exam format will be discussed in class (there will be an exam review session).
Make-up Exam Policy: Make-up exams will only be given if a student has a university-validated excuse. Requests for a make-up exam must be timely (e.g., before the exam if issue is known or as soon as possible [within a day] of the exam if sudden). Without exception, students who request a make-up exam will be asked to provide appropriate documentation before a make-up exam is scheduled. Failure to provide official documentation will result in a score of zero for the exam. The instructor reserves the right to determine the nature and date of the make-up exam.
B. 4 Assignments (2 Individual and 2 Group)
You will have four assignments throughout the semester. Each assignment will be associated with a dataset and a set of questions. You will need to use Excel/R (RStudio) and the relevant models to answer these questions. You will have one week to submit each assignment from the day it is posted. Assignments will be graded based on 1) accuracy, 2) strength of analysis, and 3) strength of inferences. For group assignments, you will need to form your own group (4 or 5 students per group). Please note that if you do not have a group, you should contact me as soon as possible so that I can connect you with others. The group you form should continue for the two group assignments and the final project. Please ensure that you have a group to work with by the second week of the class.
- You will need to submit the assignment electronically. For group assignments, only one member submits the report with the group members’ name in the first page.
- Answers: Provide a specific answer to each question. Show how you have arrived at those answers (inferences).
- Attached the Excel/R code you used under the heading “Excel/R procedure”
- Write a concluding paragraph based on your analysis and inference.
C. Final Project (Group)
The final project is an expanded and comprehensive version of an individual assignment, designed to showcase your ability to apply the concepts and skills learned throughout the course. The final project will be evaluated based on both the presentation and the final project report. I will provide a dataset, which may require cleaning and preprocessing—an essential skill we will cover in class. Alternatively, you are encouraged to seek out and utilize a unique dataset that aligns with your interests.
You will be tasked with answering a set of predefined questions related to the dataset. Beyond this, you must also identify and propose a specific research question that would be of interest to managers or researchers. This question should reflect a relevant business challenge or opportunity, and you will need to select an appropriate analytical tool or framework to address it.
Each group will present their findings during the last session of the course. The order of presentations will be determined by a random draw. Your presentation should be delivered using PowerPoint and will be evaluated on the following criteria: 1) accuracy, 2) strength of analysis, 3) strength of inferences, 4) depth of strategic insights provided, 5) importance of proposed questions, and 6) presentation quality.
After your presentation, each group must submit a comprehensive final project write-up. This document should detail your analysis, including data cleaning, methodology, results, and strategic insights. Additionally, it must address any questions or challenges raised during your presentation, providing necessary clarifications. This write-up will serve as the formal record of your project, showcasing your ability to communicate findings and respond to feedback.
D. Class Participation
Attendance is mandatory, and students must notify the instructor in advance if they are unable to attend a session. Grading class participation is necessarily subjective. Some of my criteria for evaluating effective class participation include: 1. What is the quality of the comment? 2. Is the student willing to learn? Is the participant prepared? Do comments add to our understanding of the situation? Do comments show an understanding of theories, concepts, and analytical tools presented in-class lectures or reading materials? 3. Is the participant a good listener? Are the points made relevant to the discussion? Are they linked to the comments of others? Is the participant willing to interact with other class members? 4. Are concepts presented concisely and convincingly? I emphasize the quality of participation much more than quantity.
The more you engage, the more likely you are to earn participation points and potentially extra credit.
E. Extra Credit
The criteria will be discussed in the class.
F. Assignments Submission Policies
Canvas Submissions
All course assignments must be submitted on Canvas. Only assignments that have been successfully submitted on Canvas will be graded. Attempts that have been started but not submitted are not visible to the instructor and, therefore, cannot be graded. You are responsible for ensuring that all of your assignments are submitted properly.
Deadlines
Students are expected to complete and submit all course assignments by the stated deadline. All deadlines are based on the Eastern Time Zone where George Mason University – Fairfax is located. For each submission on Canvas, a time stamp is recorded. The exact time stamp for the submission on Canvas will determine if the assignment is on-time or late.
- To illustrate: If an assignment that is due by 11:59PM has a time stamp of 12:00AM -- or even 11:59:01PM --it will be considered late.
- To ensure that your submission is on-time, give yourself plenty of time to upload the assignment.
Late Submission Policy
All deadlines are final. If the instructor decides to extend a deadline, the extension will apply to all students in the class.
The late submission policy is as follows:
- No late submissions for exams will be accepted. A make-up exam may be given for those that qualify according to the Make-Up Exam Policy.
- Late submissions will be accepted. However, late submissions for these assignments are subject to the penalties detailed in the table below.
| Late Period | Automatic Deduction |
|---|---|
| Late within 1 hour: | 10% off total points possible |
| Late more than 1 hours but less than 48 hours: | 40% off total points possible |
| Late more than 48 hours: | Not accepted |
Course Schedule*
| Weeks | Topic | Notes |
|---|---|---|
| Week 1 |
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Bring your laptop to install R/R studio |
| Week 2 |
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| Week 3 |
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Sign up for a Group Assignment 1 (individual) will be posted |
| Week 4 |
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Assignment 1 Due |
| Week 5 |
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Assignment 2 (Group) will be posted |
| Week 6 |
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Assignment 2 Due |
| Week 7 |
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| Week 8 |
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| Week 9 |
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Assignment 3 (individual) will be posted |
| Week 10 |
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Assignment 3 Due Assignment 4 (Group) will be posted One-page final project proposal due and discuss your initial final project plan with me |
| Week 11 |
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| Week 12 |
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Assignment 4 Due |
| Week 13 |
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| Week 14 |
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| Week 15 |
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25 min presentation 5 min Q&A |
| Exam Period |
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Final project report due by Dec 10, 11:59 PM (ET). |
* Calendar may be subject to change due to unforeseen circumstances. If adjustments need to be made, notice will be given to students on Canvas.
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