Instructor: Sungjin Kim ( sungjin.kim@hawaii.edu)
Office Hour: Friday 3-4 PM or by appointment
BUS 312 Principles of Marketing
BUS 310 Statistical Analysis for Business Decsions
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 tecchniques 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 customers change
All competitors react
All resourses 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.
Lecture, supported by the text book “Marketing Analytics Based on First Priciples” by Robert W. Palmatiter, 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” applicationare designed to reinforce theoretical understanding and build analytical skills
Required Text: Marketing Anlytics Based on Frist Principles, by Robert W. Palmatier, J. Andrew Peteren, and Frank Germann
Software: R and RStudio. R is free integrated development enviroment (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 ( Windowns 7 or higher)/Mac/Linux OS with a mininum of 4GB RAM are avialable 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 replicate assignment is to get first-hand experiencewith these models and buil 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 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 implamentable manner.
The midterm and final exams will be closed-book exam, and cover all content discussed in the course. The exams will include multiple-choice and true-false questions. Detail 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 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 Lili’uokalani 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; 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.
library(xlsx)
nv1 <- read.xlsx("D:/AnhThu/nv1.xlsx", sheetIndex = 1, header = T)
nv1## Date X. DoW Subject Reading
## 1 2024-01-10 1 Wed Introduction Chapter 1
## 2 2024-01-11 2 Fri R Basics Handout
## 3 2024-01-12 3 Wed R Basics Handout
## 4 2024-01-13 4 Fri R Basics Handout
## 5 2024-01-14 5 Wed MP1: Consumer Heterogeneity Chapter 2
## 6 2024-01-15 6 Fri Cluster Analysis for Segmentation Chapter 3
## 7 2024-01-16 7 Wed Discriminant Analysis for Targeting Chapter 4
## 8 2024-01-17 8 Fri Perceptual Map for Positioning Chapter 5
## 9 2024-01-18 9 Wed Case 1 Preparation Handout
## 10 2024-01-19 10 Fri MP2: All Customers Change Chapter 6
## 11 2024-01-20 11 Wed RFM Analysis Chapter 7
## 12 2024-01-21 12 Fri Logistic Regression Chapter 8
## 13 2024-01-22 13 Wed Case 2 Preparation Handout
## 14 2024-01-23 14 Fri Mid Term <NA>
## 15 2024-01-24 15 Wed MP3: All Competitors React Chapter 10
## 16 2024-01-25 16 Fri Survey Design & Factor Analysis 1 Chapter 11
## 17 2024-01-26 17 Wed Survey Design & Factor Analysis 2 Chapter 11
## 18 2024-01-27 18 Fri Conjoint Analysis 1 Chapter 12
## 19 2024-01-28 19 Wed Conjoint Analysis 2 Chapter 12
## 20 2024-01-29 NA Fri Bass Model Chapter 13
## 21 2024-01-30 NA Wed Spring Break - No class <NA>
## 22 2024-01-31 NA Fri Spring Break - No class <NA>
## 23 2024-02-01 NA Wed Case 3 Preparation Handout
## 24 2024-02-02 NA Fri Good Friday - No Class <NA>
## 25 2024-02-03 NA Wed MP4: All Resources Are Limited Chapter 14
## 26 2024-02-04 NA Fri Marketing Mix Model 1 Chapter 15
## 27 2024-02-05 NA Wed Marketing Mix Model 2 Chapter 15
## 28 2024-02-06 NA Fri Experiments 1 Chapter 16
## 29 2024-02-07 NA Wed Experiments 2 Chapter 16
## 30 2024-02-08 NA Fri Case 4 Preparation Handout
## 31 2024-02-09 NA Wed Special Topics <NA>
## 32 2024-02-10 NA Fri Special Topics <NA>
## 33 2024-02-11 NA Wed Wrap-up <NA>
## 34 <NA> NA <NA> <NA> <NA>
## Homework.Due
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## 7 Replication: Cluster Analysis
## 8 Replication: Discriminant Analysis
## 9 Replication: Perceptual Map
## 10 <NA>
## 11 Case 1 Submission
## 12 Replication: RFM
## 13 Replication: Logistic Regression
## 14 <NA>
## 15 Case 2 Submission
## 16 <NA>
## 17 <NA>
## 18 <NA>
## 19 Replication: Factor Analysis
## 20 <NA>
## 21 <NA>
## 22 <NA>
## 23 Replication: Conjoint Analysis
## 24 <NA>
## 25 Replication: Bass Model
## 26 <NA>
## 27 <NA>
## 28 Replication: Marketing Mix Model
## 29 <NA>
## 30 Replication: Experiments
## 31 Case 4 submission
## 32 <NA>
## 33 <NA>
## 34 <NA>