Instructor: Sungjin Kim (sungjin.kim@hawaii.edu)
Office Hours: Friday 3-4 PM or by appointment
BUS 312 Principles of Marketing
BUS 310 Statistical Analysis for Business Decisions
Please inform the instructor if you haven’t taken either course
This course delves into marketing analytics, emphasizing a systematic and analytical approach to decision-making. Using the free software R, students will gain hands-on experience applying data analytics techniques and case studies to scientifically address marketing challenges. The course explores the “first principles” of marketing strategy, serving as a guiding framework for organizations navigating analytics opportunities and challenges in the contemporary data era.
All customers are different
All customer change
All competitors react
All resources are limited
The course adopts the “Tell-Show-Do” sequence, offering a dynamic and hands-on approach to learning marketing analytics. Through a combination of lectures, practical demonstrations, and active engagement, students will acquire the necessary skills to apply scientific methodologies to marketing decision-making.
Lectures, supported by the textbook “Marketing Analytics Based on First Principles” by Robert W. Palmatier, J. Andrew Petersen, and Frank Germann, will explain key concepts and models essential for a scientific marketing approach. The theoretical foundation provided in lectures will serve as the “Tell” phase.
The “Show” phase involves the application of concepts and models using software tools integral to the course. R and RStudio, as free and powerful data analytics tools, will be employed to provide students with hands-on experience in resolving real-life marketing problems. These practical “Do” applications are designed to reinforce theoretical understanding and build analytical skills.
Required Text: Marketing Analytics Based on First Principles, by Robert W. Palmatier, J. Andrew Petersen, and Frank Germann
Software: R and Rstudio. R is a free data analytics software; RStudio is a free integrated development environment (IDE) that helps you be more productive with R; You can download them here: R and RStudio
Computer: Students are required to bring a laptop to the class. Any laptops with Windows (Windows 7 or higher)/Mac/Linux OS with a minimum of 4GB RAM are available to use R and RStudio. An iPad does not support R.
All students should read each assigned book chapter as well as the four cases. Please bring name cards to classes. I emphasize quality of participation much more than quantity. Your contributions will count for more if you build on the comments/insights offered by others (including me) in the class. The purpose of class participation is learning. So, do not attempt to dominate the conversation.
This course focuses on “learning by doing.” I will show you in class how to estimate the various models covered in the course using R. You will then be asked to replicate these models outside of class. The purpose of these replication assignments is to get first-hand experience with these models and build a marketing analytics toolkit. In the assignment, you should demonstrate (e.g., through screen capture) that you successfully replicate the class results. Beyond replicating the models and results discussed in class, you will also be asked to provide a brief (about half a page per assignment) summary of what the model does and when to use it.
We will be covering four cases in this course. You will work on these cases as part of your learning team, and each member of the team will get the same grade. Each case will focus on a different First Principle. I will provide specific questions for you to answer. Please see the due dates in the detailed schedule. I will briefly discuss the cases. Your case analyses should highlight your ability to communicate the results of the case in a managerially relevant and implementable manner.
The midterm and final exams will be a closed-book exam, and cover all content discussed in the course. The exams will include multiple-choice and true-false questions. Details will be discussed in class.
The grade components will be weighted as follows:
Class Participation: 10% + α
Replication Assignments: (10; 2% each) 20%
Cases (4; 5% each): 20%
Mid-term Exam: 15%
Final Exam: 35%
Any student who feels they may need an accommodation based on the impact of a disability is invited to contact me privately. I would be happy to work with you, and the KOKUA Program (Office for Students with Disabilities) to ensure reasonable accommodations in my course. KOKUA can be reached at (808) 956-7511 or (808) 956-7612 (voice/text) in room 013 of the Queen Liliʻuokalani Center for Student Services.
The University expects students to maintain standards of personal integrity that are in harmony with the educational goals of this institution; to respect the rights, privileges, and property of others; and to observe national, state, and local laws and University regulations. All work submitted in this class must be your own. Cheating, plagiarizing, gaining unfair advantages over others, or otherwise violating the University of Hawaii Student Conduct Code will not be tolerated. Any and all such violations will result, at minimum, in a failing grade for the assignment or exam. If you have questions about this policy or what constitutes proper conduct, please see me.
Note: This class schedule is tentative and may change as the term proceeds. All changes to the class schedule will be announced in class and posted on Laulima.
| Date | DoW | # | Subject | Reading | Homework Due |
|---|---|---|---|---|---|
| 1/10 | Wed | 1 | Introduction | Chapter 1 | |
| 1/12 | Fri | 2 | R Basics | Handout | |
| 1/17 | Wed | 3 | R Basics | Handout | |
| 1/19 | Fri | 4 | R Basics | Handout | |
| 1/24 | Wed | 5 | MP1: Consumer Heterogeneity | Chapter 2 | |
| 1/26 | Fri | 6 | Cluster Analysis for Segmentation | Chapter 3 | |
| 1/31 | Wed | 7 | Discriminant Analysis for Targeting | Chapter 4 | Replication: Cluster Analysis |
| 2/2 | Fri | 8 | Perceptual Map for Positioning | Chapter 5 | Replication: Discriminant Analysis |
| 2/7 | Wed | 9 | Case 1 Preparation | Handout | Replication: Perceptual Map |
| 2/9 | Fri | 10 | MP2: All Customers Change | Chapter 6 | |
| 2/14 | Wed | 11 | RFM Analysis | Chapter 7 | Case 1 Submission |
| 2/16 | Fri | 12 | Logistic Regression | Chapter 8 | Replication: RFM |
| 2/21 | Wed | 13 | Case 2 Preparation | Handout | Replication: Logistic Regression |
| 2/23 | Fri | Mid Term | |||
| 2/28 | Wed | 14 | MP3: All Competitors React | Chapter 10 | Case 2 Submission |
| 3/2 | Fri | 15 | Survey Design & Factor Analysis 1 | Chapter 11 | |
| 3/7 | Wed | 16 | Survey Design & Factor Analysis 2 | Chapter 11 | |
| 3/9 | Fri | 17 | Conjoint Analysis 1 | Chapter 12 | |
| 3/14 | Wed | 18 | Conjoint Analysis 2 | Chapter 12 | Replication: Factor Analysis |
| 3/16 | Fri | 19 | Bass Model | Chapter 13 | |
| 3/21 | Wed | Spring Break - No class | |||
| 3/23 | Fri | Spring Break - No class | |||
| 3/28 | Wed | 20 | Case 3 Preparation | Handout | Replication: Conjoint Analysis |
| 3/30 | Fri | Good Friday - No Class | |||
| 4/4 | Wed | 21 | MP4: All Resources Are Limited | Chapter 14 | Replication: Bass Model |
| 4/6 | Fri | 22 | Marketing Mix Model 1 | Chapter 15 | |
| 4/11 | Wed | 23 | Marketing Mix Model 2 | Chapter 15 | |
| 4/13 | Fri | 24 | Experiments 1 | Chapter 16 | Replication: Marketing Mix Model |
| 4/18 | Wed | 25 | Experiments 2 | Chapter 16 | |
| 4/20 | Fri | 26 | Case 4 Preparation | Handout | Replication: Experiments |
| 4/25 | Wed | 27 | Special Topics | Case 4 submission | |
| 4/27 | Fri | 28 | Special Topics | ||
| 5/2 | Wed | 29 | Wrap-up |