MENY
This is the study programme for 2019/2020. It is subject to change.


The course teaches students how to apply various analytical tools while exploring real world marketing challenges. Students will also be exposed to software R to run simple descriptive summaries to marketing analytic models.

Learning outcome

Knowledge:
Upon completion of this course, students should be familiar with:
  • fundamental data analysis techniques, such as various regression models and segmentation tools
  • writing a simple R code for fundamental statistical analysis
  • different types of research designs
  • different sampling methods, data collection methods and measurement issues

Skills:
Upon completion of this course, students will be able to:
  • obtain and visualize basic descriptive features of data
  • identify and carry out the appropriate analytical method for a given problem
  • draw scientifically sound conclusions from analytical results
  • write and execute simple R programs for fundamental statistical analyses
  • independently develop and carry out their own research projects and communicate results from such projects
  • understand, evaluate and utilize the results from other research studies

General competence:
Upon completion of this course, students will have acquired and developed an understanding of the fundamentals thinking around marketing analytics, and how to gain marketing insights using data and statistical tools.

Contents

1. Introduction to Marketing Analytics
  • A brief statistical review
  • A brief principles of consumer behavior and marketing strategy
  • What is an insight?
  • Introduction to R

2. Dependent variable techniques
  • What drives demand?
  • Regression analysis (ordinary least squares, logistic regression, count data regression, panel regression)

3. Inter-relationship techniques
  • What does my customer market look like?
  • Segmentation analysis (clustering, latent class analysis, random forest)

4. Big data and big data analysis
  • What is big data?
  • How to analyze big data?

5. Final Project
  • Research design
  • Data collection
  • Reporting results

Required prerequisite knowledge

None.

Recommended previous knowledge

BØK104 Statistics and social science methodology

Exam

Term paper in group and oral exam
Weight Duration Marks Aid
Term paper in group1/21 A - FAll.
Oral exam1/2 A - F
The term paper: work in group of max. 5 students. Due at the end of the instruction.
The oral exam is graded individually. There is no possibility of repeat exam for the group project.
The term paper and the oral exam will be given and responded to in English.

Coursework requirements

Assignments, Attendance
The assignments are completed in groups of up to 5 students. Groups are assigned by the instructor (3 to 4 assignments in total). All assignments are mandatory, and students need to "pass" all the assignments in order to write the term paper.
Participation is an important aspect of this course. Thus, students are required to attend all lectures and sessions during the semester. Those who cannot attend lectures should not take this course.

Course teacher(s)

Course coordinator
Yuko Onozaka

Method of work

The course will include a combination of weekly lectures, weekly data lab sessions, (approximately) bi-weekly mandatory group assignments, and individual study (reading). Students are expected to prepare for the lectures by reading the part of the curriculum that will be covered in each particular lecture.
The total work load in this course is estimated to be 250-300 hours.

Open to

Business Administration - Bachelor's Degree Programme
Business Administration - Master of Science (5 years)
Exchange programmes at UIS Business School

Course assessment

Student evaluation will be carried out in accordance with the UiS Business School's evaluation system.

Literature

Required: "Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques" by Mike Brisgy. 2nd Edition. Kogan Page Publishers, 2018.
Optional (supplementary): "R for Marketing Research and Analytics" by Chris Chapman and Elea McDonnell Feit. Springer, 2015.


This is the study programme for 2019/2020. It is subject to change.

Sist oppdatert: 17.06.2019