| Day | Date | Time | Room |
|---|---|---|---|
| Thursday | 10/02/25 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 10/09/25 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 10/16/25 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 10/23/25 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 11/06/25 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 11/13/25 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 11/20/25 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 11/27/25 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 12/04/25 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 12/11/25 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 12/18/25 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 01/08/26 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 01/15/26 | 03:00 PM - 06:30 PM | D2.0.030 |
| Thursday | 01/22/26 | 03:00 PM - 06:30 PM | D2.0.030 |
The course gives an introduction into Marketing Research and Analytics with an emphasis on problems in Retailing. All analyses will be done using R.
Topics:
1: Course overview
2: Self study units (Chapters: 1-4)
3: The Tidyverse & the Data Science Workflow
4: Comparing groups (5-6)
5: Linear models (7)
6: Non-Linear models and conjoint-analysis (13)
7: Text mining for marketing Applications 1
8: Text mining for marketing Applications 2
9: Segmentation (11)
10: Positioning
11: Reducing data complexity (8)
12: Presentations
After completing this course students will have a basic knowledge of fundamentals of Data Analysis for Marketing problems. They are able to describe and visualize data with R and apply models for advanced marketing applications. Students will learn about decision problems in retailing like pricing, assortment or advertising planning. Beside an understanding of the problem structure, students will learn to apply mathematical and statistical tools to support decision making. Apart from that, completing this course will contribute to the students’ ability to efficiently work and communicate in a team, work on solutions for complex practical problems by using modern statistical software.
According to the examination regulation full attendance is intended for a PI. Absence in one unit is tolerated if a proper reason is given.
The final grade of the course will depend on
• In-class coding quiz (2x15%) in unit 3 and unit 7
• Design of a research concept for a conjoint study (15%)
• Presentation of a research paper (20%) in unit 14
• Defense of STP-project (25%)
• Classroom participation (10%)
Grading scale:
(1) Excellent: 90% - 100%
(2) Good: 80% - <90%
(3) Satisfactory: 70% - <80%
(4) Sufficient: 60% - <70%
(5) Fail: <60%
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