| Table 2: Group Project Grading Components | |
| Percentage | |
|---|---|
| Research Brief | 10% |
| Qualitative Findings | 30% |
| Final Report | 30% |
| In-Person Presentation | 10% |
| Managerial Review | 15% |
| Peer Evaluation | 5% |
| Total | 100% |
MKT 512
Customer Insights & Analysis
Syllabus
Course Description
This course will introduce you to various approaches to understand customers and their contribution through data. It includes qualitative and quantitative methods to help you analyze data and develop insights that lead to more profitable and equitable business outcomes. These approaches are essential for developing a successful career in marketing or in any management discipline that relies upon modeling and predicting customer behavior.
Learning Objectives
- Develop customer-centric research programs that address specific business questions about customer segments and buying journey behaviors.
- Implement primary research studies including qualitative interviews and quantitative experiments to gather actionable customer insights.
- Apply statistical analysis techniques to customer data to test hypotheses and identify behavioral patterns.
- Analyze customer relationships across buying journey stages to determine value perception and decision drivers.
- Synthesize research findings into compelling narratives that support strategic customer management decisions.
Materials
Required Texts
Course Reader for assigned articles and cases available on the Harvard Business School Publishing website. The assignments are posted on Brightspace. The custom cases I authored are provided for download on Brightspace.
Optional Texts
The textbooks listed below are entirely optional. They provide greater depth and context on marketing research and marketing, in general.
Marketing Research by David Aaker is the gold standard for textbooks on marketing research. It includes numerous chapters that delve deep into qualitative methods, survey research, and emerging analytical approaches. If you wish to further explore the material we cover in the class, this is a book that will age well on your bookshelf.
Marketing Management by Philip Kotler and Kevin Keller is the most widely cited textbook for the foundations of marketing. If you are new to marketing or have limited experience with marketing activities, this book is the essential reference. We use it here at USC in many of our core marketing courses. Again, it is another text book that deserves a permanent home on your business bookshelf.
Slides
I upload most of the slides that I present in class to Brightspace. I have also compiled an easy to reference repository for the class at mkt512.profvincent.com. Some slides that I present in class may be omitted due to confidentiality or for intellectual property rights purposes. I use slides as visual exhibits to illustrate specific points. They are not a complete record of my lectures or our discussions, and they are not a substitute for notes you should take on your own.
All of my slides are created in Quarto, an open-source scientific and technical publishing system that makes it easy for data scientists to create beautiful documents. Quarto slide presentations are actually mini-websites created with reveal.js, an open source HTML presentation framework. Quarto reveal.js presentations are best viewed on a Chrome browser. Instructions for how to make PDFs of these slides (and print, if you like) can be found here
Though not a requirement of the course, you may want to explore Quarto while you’re here. It is becoming a gold standard for reproducible research and has been growing in popularity and usage in big companies like Netflix and Airbnb, as well as many consulting firms. Knowing how to use it may give you an advantage when seeking internships or jobs in data-driven roles. The beauty of Quarto is that it is language agnostic. You can use it with R, Python and many other data science-based programming languages. You analyze the data once, then enjoy the power to output your findings in multiple formats without having to reformat.
Required Resources
You will need software to perform the statistical analyses covered in the course. Most of these analyses can be done in Excel with add-ins. I will be using R for my analyses and I will share my code throughout the semester, but you are not required to use R.
That said, use of free, open source statistical platforms like R and Python are proliferating in the marketing analytics world and marketers who possess the skills to leverage these tools are often attractive to employers. You may wish to experiment with them as you explore course content. Due to time constraints, I am unable to provide in-depth tutoring for R beginners. For those interested in learning to use R I have included a list of books and resources on R in Appendix I. Some students prefer to use Radiant, a menu-driven app built on R. Most AI platforms can now help students write code in Python or R.
For the team research project, you will conduct a survey to gather primary data for analysis. Qualtrics survey software is available free to the Marshall community. You can register using your USC email account. You may also use other data collection platforms such as SurveyMonkey, PollFish, or Google Forms. You are not required to pay participants for your study, but many students choose to recruit respondents from panel providers, which have additional costs that your team may incur. A list of sample providers is provided in Appendix II.
Optional Materials and Resources
I produced a six-episode podcast called The Customer Class for the course. Many students have said they found it useful. It features interviews with senior marketing leaders. You can access on Apple, Spotify, or listen directly on the show’s website.
For students who have little to no experience with marketing or with marketing research, please reach out to the professor and I will suggest additional reading material.
Grading
I use a two-step grading process for this course to ensure fair and consistent grading across different cohorts. The first step is related to the points you will accumulate throughout the semester by completing various assessments. 100 points are possible (see Table 1 below). These points serve as a raw score that measures your individual performance.
The second step involves standardizing your scores by creating a z-score1. Standardized scores are then mapped to letter grades according to grading guidelines for graduate electives provided by the Marshall School, as well as my own professional judgment.2
1 A z-score is a statistical process in which the mean grade for all students is set to zero and the standard deviation is one.
2 Due to this standardization process, your final letter grade may not directly correspond to your raw point total. For example, an 88-point total might not automatically translate to a B+ grade, as the final grade depends on the overall distribution of scores in the class.
| Table 1: Grading Components | ||
| % of Grade | Points | |
|---|---|---|
| Class Participation | 15% | 15 |
| Class Polls | 10% | 10 |
| Individual Assignment #1 (IA1) | 10% | 10 |
| Individual Assignment #2 (IA2) | 10% | 10 |
| Learning Checks (2 of 3) | 20% | 20 |
| Group Project | 35% | 35 |
| Total | 100% | 100 |
Grading for the group project is detailed below
I grade the course this way because…
- It ensures consistency with USC Marshall’s standards for grade distributions.
- It accounts for variations in difficulty across different assignments and assessment types.
- It provides a fair comparison of performance within your specific cohort.
I will provide regular updates on your point accumulation throughout the semester on Brightspace. If you have questions about where you stand or how the standardization process might affect your grade, please don’t hesitate to ask.
Course Format
I teach MKT 512 as a hands-on course. Theory and critical concepts are integrated into a participatory, discussion-format curriculum. The course relies on four components:
Discussion–Throughout the course we will have many discussions. I prefer interaction with students, rather than straight lecturing. Cases will often anchor our discussions. Cases provide the best way to explore real-world scenarios and view research challenges from a managerial perspective.
Individual Assignments–On an individual level you will have an opportunity to apply the concepts we learn through two individual assignments.
Learning Checks–Rather than rely on a midterm and final exam for assessing comprehension of core concepts, I prefer to do learning checks that give both of us a chance to evaluate your progress. We’ll have three of these learning checks during the semester. Only the two best scores you achieve will count toward your course grade (one learning check score is dropped).
Group Project–You and your teammates will complete a research project over the semester. This project integrates all of our lessons, discussions, and casework. It is designed to provide you with hands-on, practical experience that you can use in your careers. In fact, many previous students of this course used their team research project as a calling card for interviews with prospective employers.
Prerequisites
This is a hands-on marketing course. Students enrolled in the course should have (a) a basic understanding of foundational marketing frameworks such as the 5Cs and the marketing mix (4Ps); and, (b) functional experience doing basic statistical analysis. At a minimum, you should know how to calculate the following using the statistical tools of your choice:
- Basic descriptive statistics (counts, proportions, crosstabs, means, standard deviations, etc.)
- Core statistical tests (chi-square, t-tests, ANOVA, correlation analysis, etc.)
- Simple and multivariate linear regression
If you have no experience with statistical analysis, this course may be challenging for you. While I do everything I can to make myself available to students to help them master course concepts, I have limited capacity to tutor students on basic marketing and data science fundamentals.
Attendance
You are expected to attend and be prepared for all class sessions in-person unless you are experiencing an illness. There is no Zoom option for this class.
While attendance is not directly graded, consistent participation in class sessions is essential for your success in this course.
There are no makeup sessions or alternative assignments for missed classes. Out of fairness to all students, and in adherence with university policy, I do not offer extra credit assignments for missed work or to accumulate participation points due to absence.
Preparation and Participation
Participation enriches the quality of the classroom and the student learning experience. It also constitutes 15% of your course grade. The primary way you earn participation points is through your active participation in class discussions, though I may provide other asynchronous opportunities to participate during the semester.
Read the cases. Do the analysis. Marketing is a complex and nuanced subject matter. It is both an art and a science. The way in which it is practiced varies greatly between companies, industries, and geographies. Cases provide the best platform for you to grasp the challenges and to experiment with approaches in a context that reflects diverse, real-world scenarios. Case discussions are to business school students as flight simulators are to aspiring pilots. They provide a safe space to apply critical concepts and knowledge that will prepare you for managing in the real world.
Expected preparation includes conducting any analysis on the data presented in the case before class meetings. I post study questions on Brightspace to help you prepare for in-class discussion. You should pay particular attention to the exhibits at the back of the case and the accompanying datasets (when applicable).
Sometimes I cold call3 on students, particularly when volunteers are scarce. It is never punitive. My goal is to encourage active participation and to gain multiple perspectives and points of view. This creates a richer learning experience and makes the grading of participation more fair.
3 A cold call is an unprompted call by the instructor on a student without warning. I also do warm calls, which give the student advanced notice that they will be asked to answer a question.
Quality participation means asking questions about key concepts in the material, sharing points of view on issues and decision points in the cases, relating relevant personal experience, contributing to class debates, or building upon points raised by others during the discussion. The best rule of thumb is to act like you are a stakeholder in the business issue we’re discussing. Imagine that you are sitting in a conference room at company headquarters. How would you engage if these decisions affected your livelihood? That’s the right level of engagement for the classroom.
Speak Up! Anonymous Feedback
Your voice matters in shaping our classroom experience. Throughout the semester, you have access to a feedback tool that I call “Speak Up!” It’s available on Brightspace and it is designed to ensure everyone feels heard and included.
This platform serves multiple purposes. You can…
- Share concerns if you feel overlooked in discussions
- Highlight positive moments you’ve observed
- Suggest improvements to make our classroom more inclusive
- Share what’s on your mind about the course
The tool is anonymous by default, but you have the option to identify yourself if you’d like me to follow up with you directly.
If you’re finding it difficult to participate in class discussions, “Speak Up!” offers a practical solution: You can request to be added to my priority call list for an upcoming class. I do my best to notice raised hands and call on students who haven’t participated recently, but I sometimes miss students who are trying to get in.
I regularly review these submissions and use your insights to make real-time adjustments to enhance our learning environment. Whether you want to share feedback about a specific class session, point out ongoing patterns you’ve noticed, or celebrate something that’s working well, this tool provides a private, direct line of communication to me that respects your comfort level.
Your honest feedback helps create a classroom where everyone can thrive. It also helps me become a better instructor.
Individual Assignments
Each student will complete two (2) graded assignments. Collectively, the individual assignments count toward 20% of your course grade (10pts each). These assignments are posted on Brightspace and are due on the dates listed in the course timeline below. Please note that you should upload your work as one PDF and not as separate files. Failure to submit in the proper format may cost you grade points. Late submissions will not be accepted. Additional information about each assignment is provided in the briefing documents uploaded to Brightspace.
Learning Checks
There will be three (3) learning checks–short assessments in a multiple-choice quiz format–over the course of the semester. Each learning check is worth 10 points and each is designed to be completed within about 30 minutes. Your lowest learning check score will be dropped4.
4 For the final course grade, only your two highest learning check scores will be used. If you miss a learning check, the other two scores will be used.
Group Research Project
During the term of the course, you will join a team and complete a group research project (GRP). This project is a capstone of the course and provides the best opportunity for you to apply what you will learn in discussions, readings, and lecture content.
Three companies have been pre-selected for the class to explore this semester:
- Disney+
- Snap
- SoulCycle
In the first few weeks of class, we will divide into “Boards” for each company. These boards will have oversight of the research to be conducted. Meanwhile, you will form into teams of about 5 students. Each team will have an opportunity to decide which company research it would like to explore through a ranking activity. While I will do my best to ensure that each team gets its top pick, we will need a good distribution of team assignments for the final weeks of the class to work.
Group Project Grading
Research Brief (First Deliverable)
Each team will submit 1-2 page Research Brief, which is much like a real-world scope-of-work. This brief will be evaluated by the instructor and the internal student board associated with the target company. We will have a discussion on “Pitch Day” to discuss any suggested changes to the plan and/or issues to be resolved.
The Research Brief serves as a blueprint and workplan for your research activities. It must have the following components:
- Research Objectives
Clearly state the primary research question(s) your project will address. These are typically tied to a pressing business challenge or opportunity related to the business’s customers (current or potential).
- Initial Hypotheses
Your brief should include some discussion of what you are expecting to find and validate in your research. It’s ok if your initial hypotheses are rough or if you are uncertain about what you will find. However, you should make clear why you think this research question is significant enough that the company would spend time and money to explore it. In other words, what value will the research create?
- Experimental Methodology
Your final study is required to incorporate an experimental (or causal) design. This means that you will be creating an experiment to quantify the effects of your hypothesis. For example, suppose you hypothesize that an AI chatbot with a British accent will generate more premium sales for Amazon than one with an American accent. To prove this hypothesis, you might design an experiment in which a control group hears a voice with an American accent, while a test group hears the exact same copy read with a British accent (this is called the manipulation or treatment).
- Data Sources
Identify the people you plan to interview and survey (i.e. specific types of customers, prospects, etc.) At a minimum, your qualitative research must include at least 10 customers. Your quantitative field work must include a survey of at least 100 respondents.
- Roles
Your brief should designate the roles each team member plans to play in the project. The most important role is the Communications Lead–this is the person the instructor or others can contact if they have questions or need to coordinate activities with your group. Other roles you may want to consider (but not at all required) include…
- Qualitative interviewer(s)–team member(s) who will moderate interviews or focus groups
- Survey designer/programmer–someone who will distill the group’s design into a questionnaire and/or program the survey instrument
- Reporting coordinator (someone who will own one or more of the final documents)
- Data analyst(s) (someone who will coordinate and/or conduct statistical analysis)
Again, these are not required and you can change your mind after submitting the brief. I have found that the teams who define individual roles early and clearly tend to have better success.
Qualitative Research
The second phase of the GRP is to conduct exploratory research. The objective of this phase is to deepen your understanding of the problem and develop qualitative insights that will inform the design of your quantitative research. In addition to the qualitative component, this phase can also be used to work with secondary data you may find about your topic (published research, datasets found on the internet, etc).
You will need to interview 10 customers and/or prospective customers. These can be done as 1:1 depth interviews or in a focus group setting. Depth interviews should be about 45 minutes in length; focus groups usually require at least an hour, and may require 90 minutes, depending upon the depth of your discussion guide. All interviews should be structured around a discussion guide that must be submitted as an appendix to your qualitative findings report.
The findings report should lead with the key insights that were learned as a result of the customer interviews and how these insights influence your thinking about the problem. The report should also identify or refine the key phenomenon you wish to test in your quantitative research.
Your findings report should be submitted through the appropriate link in the Group Project folder on Brightspace no later than the beginning of the designated class period. Please make sure that you clearly designate the names of every member of the team on the cover page of your submission.5 The page limit for your qualitative report is 10 annotated slides6, excluding appendices and exhibits. Your submission should be in the form of a single-file PDF. Be sure to reference your sources for secondary research in footnotes at the bottom of each slide. I encourage you to make good use of the Gaughan & Tiberti Library. The excellent staff there can answer many of your questions and assist you in finding useful information for your exploratory analysis.
5 Teams frequently forget to list the team number and group members on the report. Don’t let this happen to you!
6 Annotated slides are presentation slides that combine the visual content (charts, bullet points, images) with written explanations or commentary directly on the slide. The annotation provides the narrative–the “what this means” or “why this matters”–so that someone could understand the argument or flow of the presentation without hearing you speak. In many cases, this takes the form of a “keyline” or short block of text beneath the visual that explains the point being made.
Quantitative Research
Your final report will be delivered in two parts—(1) an integrated findings report that includes the results of your quantitative analysis along with relevant references to the findings from your qualitative research, and (2) a presentation by your team, summarizing your insights and any recommendations. The final report should be submitted via the link provided on Brightspace no later than the beginning of class on the day it is due.
To complete the assignment, your group will conduct survey research to test the hypotheses developed in your exploratory research. This survey research should be of an experimental design—meaning that you should have one or more dependent (or response) variables that are predicted or explained by one or more independent (or predictor) variables. For example, if you were exploring the impact of offering a subscription-based offering for a service that is currently a la carte, you might wish to study the impact on retention. Your dependent variable might be a question asked of respondents measuring their likelihood to subscribe for six months vs. a year, based on differing pricing options. You could use this construct along with other metrics gathered from the respondent (past purchase behavior, lifestyle, etc.) to build a model that proves or disproves your hypothesis.
Your study must adhere to these requirements:
- You need at least 100 respondents.
- You need approximately 30 respondents in each experimental condition (out of the minimum 100 total). Keep this in mind if your study will have multiple conditions or branches.
- A copy of your survey questionnaire must be included as an appendix to your final report.
- Your sample should represent a close facsimile of the population you wish to study. So, if you wish to study subscribers of Disney+ who have purchased premium offerings on top of their base subscription, you will need to recruit a sample that matches this profile. Be mindful of demographic characteristics. Your exploratory research should inform the quotas you may need to accurately measure your target population.
- It is ok to survey college students provided that they are a relevant and meaningful sample of the customer population you are studying.7
7 I chose the three companies this semester because I believe students should be able to find customers within the USC student population.
When you have completed your research, you will need to use appropriate statistical models and tools introduced in this class to analyze data and draw insights and conclusions. Be prepared to justify your analytical approach.
The final report should be about 15-20 annotated slides, excluding necessary appendices. Your submission should be in the form of a single-file PDF.
Each team will also deliver a 10-minute presentation that summarizes the key findings and recommendations of the Final Report. It is not necessary for every team member to speak/present. In fact, given the short time period allowed, I encourage you to limit the presenters to no more than two. Speaking or not speaking will have no impact on your grade, but the clarity and persuasiveness of the group presentation overall will be graded. Group presentations will be divided into three separate sessions, by company. The order of presentation will be determined at random later in the semester.
Managerial Review
Your group will be assigned to review the work of another group in the class. As a group, you are to review their research as though you were an executive committee of the board at the company in question. Your team will provide a brief, 1-page constructive evaluation. A rubric for your evaluation will be provided about one week prior to the due date. You will submit your group’s evaluation via the link on Brightspace.
The Managerial Review is part of the evaluating team’s grade on the GRP. It does not affect the grade of the team that is evaluated. To be clear: another team’s evaluation of your GRP has no affect on your team’s grade.
Peer Evaluations
You and every member of your team will also be required to submit a peer evaluation. The link to the evaluation form is provided on Brightspace. I use peer evaluations to ensure that the grade the group achieves for the project is fairly distributed to the individuals based on their individual performance.
Course Evaluations
Towards the end of the course, you will be asked to complete a course evaluation. Your evaluations are extremely valuable to me. I am continuously revising and developing this course based on our classroom experiences together and your feedback. Please take the time to complete the course evaluation. Additionally, I may ask for periodic feedback to help guide the pace and flow of content during the semester. Your timely responses are greatly appreciated.
Course Policies
Emergency Preparedness
In case of a declared emergency if travel to campus is not feasible, the USC Emergency Information website will provide safety an other information, including electronic means by which instructors will conduct class using a combination of USC’s Brightspace learning management system, teleconferencing, and other technologies.
Use of Recordings
Pursuant to the USC Student Handbook (Part B, 11 & 12), students may not record a university class without the express permission of the instructor and announcement to the class. In addition, students may not distribute or use notes or recordings based on University classes or lectures without the express permission of the instructor for purposes other than personal or class-related group study by students registered for the class. This restriction on unauthorized use applies to all information that is distributed or displayed for use in relationship to the class.
All in-person sessions are recorded on the Panopto in-class video capture. I post the recording from most classes on Brightspace. There will be at least one remote session while the instructor is traveling with the Executive MBA program. That session will be facilitated on Zoom and the session will be recorded.
Lecture Notes and Distributed Materials
Notes made by students based on a university class or lecture may only be made for purposes of individual or group study, or for other usual non-commercial purposes that reasonably arise from the student’s membership in the class or attendance at the university. This restriction also applies to any information distributed, disseminated or in any way displayed for use in relationship to the class, whether obtained in class, via email or otherwise on the internet, or via any other medium. Actions in violation of this policy constitute a violation of the Student Conduct Code and may subject an individual or entity to university discipline and/or legal proceedings.
Academic Integrity
The University of Southern California is foremost a learning community committed to fostering successful scholars and researchers dedicated to the pursuit of knowledge and the transmission of ideas. Academic misconduct is contrary to this fundamental mission and includes any act of dishonesty in the submission of academic work (either in draft or final form), as well as cheating, plagiarism, fabrication (e.g., falsifying data), knowingly assisting others in acts of academic dishonesty, and any act that gains or is intended to gain an unfair academic advantage. Students are expected to uphold the highest standards of academic integrity in all coursework.
This course follows the expectations for academic integrity as stated in the USC Student Handbook. All students are expected to submit assignments that are original work and prepared specifically for the course/section in this academic term. Students may not submit work written by others or “recycle” work prepared for other courses without obtaining written permission from the instructor(s). Students suspected of academic misconduct will be reported to the Office of Academic Integrity.
Academic dishonesty has a far-reaching impact and is considered a serious offense against the university. Violations will result in a grade penalty, such as a failing grade on the assignment or in the course, and disciplinary action from the university, such as suspension or expulsion.
For more information about academic integrity see the Student Handbook, the Office of Academic Integrity’s website, and university policies on Research and Scholarship Misconduct.
Please ask your instructor if you are unsure what constitutes unauthorized assistance on an exam or assignment or what information requires citation and/or attribution.
Artificial Intelligence
I expect you to use AI (e.g., ChatGPT, Gemini, Anthropic’s Claude, Midjourney, Dall-E, etc.) in this class. Learning to use AI is an emerging skill, and I welcome the opportunity to explore with you over the semester. Keep the following in mind:
- AI tools are permitted to help you brainstorm topics or revise work you have already written. For example, some students have used AI to brainstorm survey questionnaires. The AI in programs like ChatGPT can write R and Python code that can be particularly helpful when conducting your analyses. Keep in mind that what AI produces tends to be generic and is rarely sufficient on its own.
- If you provide minimum-effort prompts, you will get low-quality results. You will need to refine your prompts to get good outcomes. This will take work.
- Proceed with caution when using AI tools and do not assume the information provided is accurate or trustworthy If it gives you a number or fact, assume it is incorrect unless you either know the correct answer or can verify its accuracy with another source. You will be responsible for any errors or omissions provided by the tool. It works best for topics you understand. This is also true for code that it generates.
- Generative AI—especially large language models (LLMs)—is prone to cognitive biases such as confirmation bias and acquiescence bias. In practice, this means the model may present information that aligns with your assumptions or simply tells you what it “thinks” you want to hear. When applying AI to the design or analysis of marketing research, this can lead to false confidence in flawed ideas. Always verify AI-generated information with credible sources, and validate any theories or concepts before acting on them.
- AI is a permitted tool in this course, but its use must be acknowledged. When you use AI on an assignment, briefly explain how and why you used it, and describe the role it played in your work. A concise, high-level overview is sufficient—you do not need to submit every prompt or a full transcript. Failure to disclose AI use when required will be treated as a violation of academic integrity.
Marketing research has recently been dubbed the managerial function that is “the one that’s most disrupted by generative AI.”8 During our time together we will frequently discuss its increasing role in the field–from coding surveys to serving as the source of public opinion. I encourage you to explore it often and with care.
8 “How Gen AI Is Transforming Market Research” in Harvard Business Review, 2025
Technology Policy
Because this is a technical course, laptops are occasionally allowed in class. It is acceptable for students to use laptops or other electronic devices to take notes, review material being presented in class or to conduct analysis and/or calculations. It is not permissible to use laptops or devices for unrelated purposes. During discussions, I may ask you to put laptops and devices away so that you can be fully present. Students who are distracted by devices during discussions may lose participation points or be asked to leave the classroom.
Open Expression and Respect for All
An important goal of the educational experience at USC Marshall is to be exposed to and discuss diverse, thought-provoking, and sometimes controversial ideas that challenge one’s beliefs. In this course we will support the values articulated in the USC Marshall “Open Expression Statement.”
Academic Conduct and Support Systems
Accommodations
USC welcomes students with disabilities into all of the University’s educational programs. The Office of Student Accessibility Services (OSAS) is responsible for the determination of appropriate accommodations for students who encounter disability-related barriers. Once a student has completed the OSAS process (registration, initial appointment, and submitted documentation) and accommodations are determined to be reasonable and appropriate, a Letter of Accommodation (LOA) will be available to generate for each course. The LOA must be given to each course instructor by the student and followed up with a discussion. This should be done as early in the semester as possible as accommodations are not retroactive. More information can be found at osas.usc.edu. You may contact OSAS at (213) 740-0776 or via email at osasfrontdesk@usc.edu.
Support Systems
Counseling and Mental Health
(213) 740-9355 (24/7 on call)
health.usc.edu/counseling
Free and confidential mental health treatment for students, including short-term psychotherapy, group counseling, stress fitness workshops, and crisis intervention.
988 Suicide and Crisis Lifeline
988 (24/7 on call – phone or text)
The 988 Suicide and Crisis Lifeline (formerly known as the National Suicide Prevention Lifeline) provides free and confidential emotional support to people in suicidal crisis or emotional distress 24 hours a day, 7 days a week, across the United States. The Lifeline is comprised of a national network of over 200 local crisis centers, combining custom local care and resources with national standards and best practices. The new, shorter phone number makes it easier for people to remember and access mental health crisis services (though the previous 1 (800) 273-8255 number will continue to function indefinitely) and represents a continued commitment to those in crisis.
Relationship and Sexual Violence Prevention Services (RSVP)
(213) 740-9355(WELL) press “0” after hours (24/7 on call)
studenthealth.usc.edu/sexual-assault
Free and confidential therapy services, workshops, and training for situations related to gender- and power-based harm (including sexual assault, intimate partner violence, and stalking).
Office for Equity, Equal Opportunity, and Title IX (EEO-TIX)
(213) 740-5086
eeotix.usc.edu
Information about how to get help or help someone affected by harassment or discrimination, rights of protected classes, reporting options, and additional resources for students, faculty, staff, visitors, and applicants.
Reporting Incidents of Bias or Harassment
(213) 740-5086 or (213) 821-8298
usc-advocate.symplicity.com/care_report
Avenue to report incidents of bias, hate crimes, and microaggressions to the Office for Equity, Equal Opportunity, and Title for appropriate investigation, supportive measures, and response.
The Office of Student Accessibility Services (OSAS)
(213) 740-0776
osas.usc.edu
OSAS ensures equal access for students with disabilities through providing academic accommodations and auxiliary aids in accordance with federal laws and university policy.
USC Campus Support and Intervention
(213) 821-4710
campussupport.usc.edu
Assists students and families in resolving complex personal, financial, and academic issues adversely affecting their success as a student.
Diversity, Equity, and Inclusion
(213) 740-2101
diversity.usc.edu
Information on events, programs and training, the Provost’s Diversity and Inclusion Council, Diversity Liaisons for each academic school, chronology, participation, and various resources for students.
USC Emergency
(213) 740-4321 (UPC) 24/7 on call
(323) 442-1000 (HSC) 24/7 on call
dps.usc.edu, emergency.usc.edu
Emergency assistance and avenue to report a crime. Latest updates regarding safety, including ways in which instruction will be continued if an officially declared emergency makes travel to campus unfeasible.
USC Department of Public Safety
UPC: (213) 740-6000 24/7 on call)
HSC: (323) 442-1200 24/7 on call)
dps.usc.edu
Non-emergency assistance or information
Office of the Ombuds
(213) 821-9556 (UPC)
(323-442-0382 (HSC)
ombuds.usc.edu
A safe and confidential place to share your USC-related issues with a University Ombuds who will work with you to explore options or paths to manage your concern.
Occupational Therapy Faculty Practice
(323) 442-3340
otfp@med.usc.edu chan.usc.edu/otfp
Confidential Lifestyle Redesign services for USC students to support health promoting habits and routines that enhance quality of life and academic performance.
Course Outline and Assignments
| Module 1: Outcomes | |||
|---|---|---|---|
| 1 | Tue, Aug-26 | Course introduction | Syllabus The Marketing Process (Optional) |
| 2 | Thu, Aug-28 | Customer-centricity | Marketing Reading: Customer Centricity (pp 1-15) Where Net Promoter Score Goes Wrong |
| 3 | Tue, Sep-02 | CLV | Marketing Reading: Customer Centricity (pp 22-29) Customer Lifetime Value CLV Practice Problems |
| 4 | Thu, Sep-04 | Research Planning | The Metrics Marketers Muddle Marketing Reading: Marketing Intelligence (pp 1-15) |
| 5 | Tue, Sep-09 | Case: Hecuba (A) | Hecuba (A) {Provided on Brightspace} Poll: Hecuba (A) |
| 6 | Thu, Sep-11 | Pitch Day | Learning Check 1 |
| Module 2: Qualitative | |||
| 7 | Tue, Sep-16 | Introduction to qualitative research | Qualitative Customer Research GRP: Research Brief |
| 8 | Thu, Sep-18 | Guest Speaker: Hanna Stein | |
| 9 | Tue, Sep-23 | Data collection and interviewing methods | Watch Videos on Brightspace for Session
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| 10 | Thu, Sep-25 | Case: Hecuba (B) | Hecuba (B) {Provided on Brightspace} Poll: Hecuba (B) |
| 11 | Tue, Sep-30 | Coding and theory building | Putting the Relationship Back in CRM Learning Check 2 |
| 12 | Thu, Oct-02 | IA1 Read-Out | IA2 |
| Module 3: Quantitative | |||
| 13 | Tue, Oct-07 | Introduction to survey research | Research Methods in Marketing: Survey Research
|
| Thu, Oct-09 | Fall Recess | ||
| 14 | Tue, Oct-14 | Survey design principles | Dare to Experiment: The Scientific Approach to Consumer Behavior Algorithmic Bias in Marketing GRP: Qualitative |
| 15 | Thu, Oct-16 | Scales and latent variables | |
| 16 | Tue, Oct-21 | Remote Session: Guest Speaker: Jeremy Korst |
|
| 17 | Thu, Oct-23 | Asynchronous Session: Using First-Party Behavioral Data (RFM analysis) |
The Flaw in Customer Lifetime Value The Perfect Message at the Perfect Moment |
| 18 | Tue, Oct-28 | Clustering for segmentation | Cluster Analysis for Segmentation
|
| 19 | Thu, Oct-30 | Case: Hecuba (C) | Hecuba (C) {Provided on Brightspace} Poll: Hecuba (C) |
| 20 | Tue, Nov-04 | Case: Aspen (A) Experiments with numeric DVs |
Regression: Forecasting Using Explanatory Factors Aspen Homegoods (A) {Provided on Brightspace} Poll: Aspen (A) |
| 21 | Thu, Nov-06 | Case: Aspen (B) Experiments with binary DVs |
Note on Logistic Regression - The Binomial Case Aspen Homegoods (B) {Provided on Brightspace} Poll: Aspen (B) |
| Tue, Nov-11 | Veteran’s Day | ||
| 22 | Thu, Nov-13 | Case: Crew’s Cup | Crew’s Cup {Provided in IA2 Brief} IA2 |
| Module 4: Synthesis | |||
| 23 | Tue, Nov-18 | Group Presentations #1 (TBA) | GRP: Quantitative |
| 24 | Thu, Nov-20 | Group Presentations #2 (TBA) | |
| 25 | Tue, Nov-25 | Group Presentations #3 (TBA) | |
| Thu, Nov-27 | Thanksgiving Recess | ||
| 26 | Tue, Dec-02 | Guest Speaker: Joe Kessler | |
| 27 | Thu, Dec-04 | Course Wrap-Up | Learning Check 3 |
| 28 | Tue, Dec-16 | Final Exam Day (No Class Meeting - Work with Group on Managerial Review–Due by 4pm PST) | GRP: Managerial Review |
Blue shaded rows are remote or asynchronous sessions
Appendix I: R Resources
Knowledge of R is not required for this course. You are free to conduct your analysis on any software you choose, including Microsoft Excel. However, as I do all of my analysis in R, many students are curious to experiment with the platform during the course. If you are new to R, the resources below may be helpful.
Many students who have previously taken the course used Radiant for analysis and had very positive reviews. Radiant is built on R but it features a menu-driven windows interface that lessens the need to learn any R code. You can learn how to install it here. Unfortunately, I cannot help you install Radiant on your local computer. If you need assistance, you might check with our excellent IT support team. Some DSO courses also use Radiant, so you may find additional resources there.
If you are brand new to R, the best starter book is R for Data Science by Garrett Grolemund and Hadley Wickham. The book is available free online.
The Big Book of R is an compendium of many free online books about R and its many custom packages.
RStudio is the most popular IDE (Individual Development Environment) for coding in R. It’s open source and free. It’s parent company, Posit, just launched an innovative new IDE called Positron The company is also home to a brilliant engineering and data science team that produces many free R packages. The most famous of these is the Tidyverse which I use liberally in the code examples I post.
Our former dean, Gareth James, co-authored an excellent book on statistics and data science that uses R in all of its examples. The book is titled An Introduction to Statistical Learning with Applications in R. It is available at most sellers of technical books and textbooks, including Amazon. The code in this book is all in “base R”, rather than using the Tidyverse. I rely more on Tidyverse code when I teach because students tend to find it more user-friendly, but if you plan on doing more work in R then I think it’s good to know the core language, which this book uses entirely. A new version of this book has been published using Python, as well.
R for Marketing Research and Analytics is an excellent book by Chris Chapman and Elea McDonnell Feit. Chapman is a data scientist at Google and Heit was formerly at GM and at several analytics-based research companies. You can purchase the book from most online re-sellers, including Amazon.
R is prolific. It has exploded in popularity over the last decade. A simple Google search will reveal many online tutorials. There is also a very active community online that is friendly and happy to answer questions in forums like Stack Overflow. I think this guide on Medium has a good list of tutorials and learning resources. You may also wish to connect with students or faculty in the DSO department, as many use R in their work.
Appendix II: Sample Providers
The course includes a survey research component. Each student team will need to gather a sample of people who closely represent the population relevant to your study. Some student teams leverage their own personal networks (Facebook, LinkedIn, etc) to recruit respondents. This is perfectly acceptable provided the samples respondents reflect the study’s target population. Many student teams recruit respondents from third-party sample providers. The list below is by no means comprehensive; however, these suppliers often have competitive rates and are easy to work with.