General Information

Prerequisites

Please inform the instructor if you haven’t taken either course.

Course Description

This course delves into marketing analysis, emphasizing a systematic and analytical approach to decision-making. Using the free software R, student 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

What Will Class be Like?

Teaching Methodology

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 Content

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.

Hands-On Application

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.

Texts and Course Materials

Texts and Course Materials

  • 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.

Assignments and Assessments

Class participation (Individual)

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.

Model replication assignments (Individual)

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.

Case Analyses (Team)

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.

Midterm/Final Exam (Individual)

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.

Assessment Components

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%

Important Course Policies

Students with Disabilities

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.

Academic Honesty

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.

Detailed Schedule

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(file.choose(),sheetIndex = 1,header = T)
nv1
##    Date DoW X.                             Subject    Reading
## 1  1/10 Wed  1                        Introduction  Chapter 1
## 2  1/12 Fri  2                            R Basics    Handout
## 3  1/17 Wed  3                            R Basics    Handout
## 4  1/19 Fri  4                            R Basics    Handout
## 5  1/24 Wed  5         MP1: Consumer Heterogeneity  Chapter 2
## 6  1/26 Fri  6   Cluster Analysis for Segmentation  Chapter 3
## 7  1/31 Wed  7 Discriminant Analysis for Targeting  Chapter 4
## 8   2/2 Fri  8      Perceptual Map for Positioning  Chapter 5
## 9   2/7 Wed  9                  Case 1 Preparation    Handout
## 10  2/9 Fri 10           MP2: All Customers Change  Chapter 6
## 11 2/14 Wed 11                        RFM Analysis  Chapter 7
## 12 2/16 Fri 12                 Logistic Regression  Chapter 8
## 13 2/21 Wed 13                  Case 2 Preparation    Handout
## 14 2/23 Fri NA                            Mid Term       <NA>
## 15 2/28 Wed 14          MP3: All Competitors React Chapter 10
## 16  3/1 Fri 15   Survey Design & Factor Analysis 1 Chapter 11
## 17  3/6 Wed 16   Survey Design & Factor Analysis 2 Chapter 11
## 18  3/8 Fri 17                 Conjoint Analysis 1 Chapter 12
## 19 3/13 Wed 18                 Conjoint Analysis 2 Chapter 12
## 20 3/15 Fri 19                          Bass Model Chapter 13
## 21 3/20 Wed NA             Spring Break - No class       <NA>
## 22 3/22 Fri NA             Spring Break - No class       <NA>
## 23 3/27 Wed 20                  Case 3 Preparation    Handout
## 24 3/29 Fri NA              Good Friday - No Class       <NA>
## 25  4/3 Wed 21      MP4: All Resources Are Limited Chapter 14
## 26  4/5 Fri 22               Marketing Mix Model 1 Chapter 15
## 27 4/10 Wed 23               Marketing Mix Model 2 Chapter 15
## 28 4/12 Fri 24                       Experiments 1 Chapter 16
## 29 4/17 Wed 25                       Experiments 2 Chapter 16
## 30 4/19 Fri 26                  Case 4 Preparation    Handout
## 31 4/24 Wed 27                      Special Topics       <NA>
## 32 4/26 Fri 28                      Special Topics       <NA>
## 33  5/1 Wed 29                             Wrap-up       <NA>
##                          Homework.Due NA. NA..1
## 1                                <NA>  NA  <NA>
## 2                                <NA>  NA  <NA>
## 3                                <NA>  NA  <NA>
## 4                                <NA>  NA  <NA>
## 5                                <NA>  NA  <NA>
## 6                                <NA>  NA  <NA>
## 7       Replication: Cluster Analysis  NA     '
## 8  Replication: Discriminant Analysis  NA  <NA>
## 9         Replication: Perceptual Map  NA  <NA>
## 10                               <NA>  NA  <NA>
## 11                  Case 1 Submission  NA  <NA>
## 12                   Replication: RFM  NA  <NA>
## 13   Replication: Logistic Regression  NA  <NA>
## 14                               <NA>  NA  <NA>
## 15                  Case 2 Submission  NA  <NA>
## 16                               <NA>  NA  <NA>
## 17                               <NA>  NA  <NA>
## 18                               <NA>  NA  <NA>
## 19       Replication: Factor Analysis  NA  <NA>
## 20                               <NA>  NA  <NA>
## 21                               <NA>  NA  <NA>
## 22                               <NA>  NA  <NA>
## 23     Replication: Conjoint Analysis  NA  <NA>
## 24                               <NA>  NA  <NA>
## 25            Replication: Bass Model  NA  <NA>
## 26                               <NA>  NA  <NA>
## 27                               <NA>  NA  <NA>
## 28   Replication: Marketing Mix Model  NA  <NA>
## 29                               <NA>  NA  <NA>
## 30           Replication: Experiments  NA  <NA>
## 31                  Case 4 submission  NA  <NA>
## 32                               <NA>  NA  <NA>
## 33                               <NA>  NA  <NA>