GRA 6437 Marketing Research

GRA 6437 Marketing Research

Course code: 
GRA 6437
Course coordinator: 
Auke Hunneman
Course name in Norwegian: 
Marketing Research
Product category: 
MSc in Strategic Marketing Management
2025 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

Due to technological developments and the proliferation of high-quality data, marketing is becoming an increasingly quantitative profession. This means that marketing professionals should not only be creative, they also must have a solid background in marketing analytical tools in order to make sense of all the available data. In this course, you will learn how to analyze secondary data using multivariate techniques with the purpose of enhancing marketing decisions.

The focus in the course is to be able to determine which marketing problem requires which particular analytical approach. The students will solve real-life marketing problems through the analysis of secondary data. They will have to come up with managerial recommendations based on their findings and they will learn how to communicate these findings effectively to a management audience with the use of Powerpoint presentations and by means of a written report. Hence, this course is not just a statistics course; the emphasis is on the managerial aspects of the statistical tools.

Given the rapidly changing technological environment, as indicated by the buzzwords "Big Data", marketing accountability, data science, etc., this course will summarize the most recent developments in marketing research in general and introduce the students to state-of-the-art analytical methods in particular.

Students should have working knowledge of SPSS before the course starts.

Learning outcomes - Knowledge

The overall learning goal is to be able to see the benefits of analytical decisions in marketing, namely how it can lead to better decisions contributing to the firm's goals. Closely related to this ability is the ability to discern and choose between possible techniques as well as the ability to apply this technique appropriately, given a particular marketing problem. Finally, the aim is to convincingly communicate the findings to the firm's decision makers in an understandable, non-technical language.

To achieve the learning outcomes, the students must be able to:

1) Explain the differences and similarities between key techniques.
2) Understand each technique in terms of:

  • Its data requirements
  • The type of problems it can be used for
  • The underlying statistics
  • The assumptions/limitations
  • Its relationship with other techniques

3) Interpret the outputs (results) and derive managerial implications
4) Oral and written communication of the results

Learning outcomes - Skills

Students should be able to:

  • Effectively communicate the outcomes of their statistical analyses to decision makers.
  • Utilize statistical packages like SPSS and Excel to analyze secondary data.
  • Choose the appropriate statistical technique for the marketing research problem at hand.
  • Decide which data / variables may give answers to relevant marketing research problems.
General Competence

Students should be able to:

  • Understand and respect the privacy of the subjects of the analyses.
  • Be fully transparent with regard to how data are collected and analyzed.
  • Be aware of the limitations of statistical technique / multivariate method.
Course content
  1. A refresher in inferential statistics
  2. Analysis of Variance (ANOVA) and related methods
  3. (Logistic) Regression analysis
  4. Principal components analysis
  5. Cluster analysis
  6. Conjoint analysis
  7. Presentation and write up of findings

This course outline may be subject to changes.

Teaching and learning activities

The learning process in this course takes place through 1) lectures about different multivariate techniques and how they can be employed to solve marketing problems, and 2) lab sessions in which the students practice the application of these techniques to real-life marketing problems using secondary data. Class attendance is strongly recommended but not required; participation in lab sessions however is mandatory.

Software tools
Additional information

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.

All parts of the assessment must be passed in order to receive a final grade in the course.


All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.


Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Exam category: 
Form of assessment: 
Submission PDF
Exam/hand-in semester: 
First Semester
Group (2 - 3)
2 Week(s)
Assignment (hand out in the beginning of the semester)
Exam code: 
GRA 64372
Grading scale: 
Examination when next scheduled course
Exam category: 
School Exam
Form of assessment: 
Written School Exam - digital
Exam/hand-in semester: 
First Semester
Support materials: 
  • Bilingual dictionary
3 Hour(s)
Written examination under supervision.
Exam code: 
GRA 64373
Grading scale: 
Examination when next scheduled course
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
Student workload
36 Hour(s)
Group work / Assignments
30 Hour(s)
Student's own work with learning resources
60 Hour(s)
Prepare for teaching
34 Hour(s)
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.