GRA 4158 Marketing and the Analysis of Experiments and Quasi-experiments

GRA 4158 Marketing and the Analysis of Experiments and Quasi-experiments

Course code: 
GRA 4158
Course coordinator: 
Jan-Michael Becker
Course name in Norwegian: 
Marketing and the Analysis of Experiments and Quasi-experiments
Product category: 
MSc in Data Science for Business
2024 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

Marketing is about understanding consumer preferences and behaviour, predicting future needs, and testing the effectiveness of different marketing activities. As such, it is a discipline that affects most industries, and with the advent of digital technologies and vast amounts of both aggregated and individual-level data it is more data-driven than ever before.

In this course you will be given a brief overview of what defines marketing as a discipline and learn about the marketing process from a data-driven decision perspective. We will then focus on the use of causal inference methods using experimental and quasi-experimental data to study marketing phenomena. You will learn how to plan and conduct experiments efficiently and effectively (e.g., AB testing), and you will be exposed to statistical methods allowing us to derive causal relationships from quasi-experimental (or observational) data. While these methods are widely applied in many different disciplines (e.g., economics, political science, sociology), we will use applications from the field of marketing research to illustrate the principles, challenges, and opportunities of these methods, as well as how to derive managerial recommendations from this type of analysis.

Learning outcomes - Knowledge

By the end of the course, the student should have:

  • Basic knowledge on what defines marketing as a discipline.
  • An overview of why and when data science is important for marketing decision-making.
  • A solid understanding of important experimental and quasi-experimental methods.
  • Knowledge on how marketers can leverage experimental and non-experimental methods to make informed decision about marketing activities.
Learning outcomes - Skills

By the end of the course, the student should be able to:

  • Design and analyse simple experiments.
  • Apply appropriate analysis techniques to experimental and quasi-experimental data.
  • Reflect upon potential problems and challenges of these methods.
  • Critically evaluate and communicate data-driven insights in marketing.
General Competence

Upon completion of this course, students should be able to:

  • Assess alternative solutions and derive strategic implications.
  • Develop a mindset for data-driven marketing decision-making.
Course content

The course will rougly center around the following core topics. Details are examplary and may be subject to change.

  • What is marketing?
  • Why and when is data science applied in marketing?
  • Experimental designs
    • Field vs. lab experiments
    • Natural experiments
  • Causal Inference
    • Average treatment affect
    • Heterogenous treatment affects
    • Internal and external validity
  • Quasi-experimental and observational methods for causal inference
    • Concept of endogeneity
    • Difference-in-difference design
    • Propensity score matching
    • Instrumental variables (IVs)
    • IV-free approaches to endogeneity correction (e.g., Gaussian copulas)
Teaching and learning activities

This course is designed using a variation of learning activities that support students in reaching the learning outcomes stated for this course. The course combines lectures, in-class discussions, group collaboration, and out-of-class learning activities. The classes will be organized around becoming familiar with the core concepts and methodological foundations and discussing selected topics in marketing to illustrate state-of-the-art empirical marketing research and practical business cases that apply these methods to derive managerial insight. The students are expected to be well prepared and highly involved in the discussions. In addition, students will apply some of the methods to analyse relevant datasets.

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 get a grade in the course.

Starting academic year 2023/2024, the weighting of the exams of this course have been changed It is not possible to retake the old version of the exam. Please note new exam codes in the Exam section of the course description. 


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: 
Written submission
Support materials: 
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
3 Hour(s)
Exam code: 
GRA 41582
Grading scale: 
Examination when next scheduled course
Exam category: 
Form of assessment: 
Written submission
Group/Individual (1 - 3)
1 Semester(s)
This exam is organized as a portfolio assessment where students are given several smaller assignments during the course. They will get feedback along the way.
Exam code: 
GRA 41583
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
30 Hour(s)
Prepare for teaching
36 Hour(s)
Student's own work with learning resources
46 Hour(s)
Group work / Assignments
45 Hour(s)
3 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.