GRA 4158 Marketing and the Analysis of Experiments and Quasi-experiments
GRA 4158 Marketing and the Analysis of Experiments and Quasi-experiments
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.
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 experimental and quasi-experimental methods.
- Knowledge on how marketers can leverage experimental and non-experimental methods to make informed decision about the effectiveness of marketing activities.
By the end of the course, the student should be able to:
- Apply appropriate analysis techniques to experimental and quasi-experimental data.
- Reflect upon potential problems and challenges of these methods.
- Design and analyse simple experiments.
- Critically evaluate and communicate data-driven insights in marketing.
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.
- What is marketing?
- Why and when is data science applied in marketing?
- Experimental designs
- Field vs. lab experiments
- Randomized control trial (AB testing)
- Natural experiments
- Causal Inference
- Average treatment affect
- Heterogenous treatment affects
- Internal and external validity
- Quasi-experimental methods
- Regression discontinuity design
- Difference-in-difference design
- Instrumental variables (IVs)
- IV-free approaches to endogeneity correction (e.g., Gaussian copulas, latent IVs)
The course combines lectures, in-class discussions and presentations, and out-of-class exercises. The classes will be organized around first presenting the methodological foundations and then 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.
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.
This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course starts.
At re-sit all exam components must, as a main rule, be retaken during next scheduled 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.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 40 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: GRA 41581 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Activity Form of assessment: Presentation Weight: 30 Grouping: Group/Individual (1 - 3) Duration: 1 Semester(s) Exam code: GRA 41581 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 30 Grouping: Group (2 - 3) Duration: 3 Week(s) Exam code: GRA 41581 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Prepare for teaching | 24 Hour(s) | |
Student's own work with learning resources | 48 Hour(s) | |
Group work / Assignments | 49 Hour(s) | |
Examination | 3 Hour(s) |
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.