5039 Retail Marketing Analytics
Daria Yudaeva, MSc (WU)
Contact details
Weekly hours
Language of instruction
02/21/24 to 02/28/24
Registration via LPIS
Notes to the course
Day Date Time Room
Thursday 03/07/24 08:30 AM - 11:00 AM TC.-1.61
Thursday 03/14/24 08:30 AM - 11:30 AM TC.-1.61
Thursday 03/21/24 08:30 AM - 11:30 AM TC.-1.61
Thursday 04/11/24 08:30 AM - 11:30 AM TC.-1.61
Thursday 04/18/24 08:30 AM - 11:30 AM TC.-1.61
Thursday 04/25/24 08:30 AM - 11:30 AM TC.-1.61
Thursday 05/02/24 08:30 AM - 11:30 AM TC.-1.61
Thursday 05/16/24 09:00 AM - 11:00 AM Online-Einheit

Data-driven marketing has become increasingly important in recent years due to digitalization, (relatively) easy availability of large data sets, and wide opportunities for new data collection. In both offline and online retail, data can be collected and analyzed to improve marketing activities of all types. With the help of appropriate data analysis methods, the effectiveness of marketing measures can be quantified to generate meaningful recommendations for retail marketing managers. To utilize this potential, however, a sound understanding of modern analysis methods and their implementation using state-of-the-art software is required.

This course introduces the theories, processes, and analysis methods of a modern, data-driven marketing approach. Through practical applications of the methods in different contexts, a direct practical relevance is created to illustrate the relevance for various applications, such as marketing mix planning, market segmentation, brand positioning, and new product development.

Learning outcomes

After completing the course, students will be able to

  • derive relevant research questions concerning business objectives and assess them in terms of management relevance;
  • assess different research designs about their suitability in relation to the research objective (description, prediction, causal inference);
  • analyze relationships between variables based on observational data;
  • understand the basics of methods from the fields of supervised and unsupervised statistical learning (e.g. regression, classification, cluster analysis);
  • implement analysis methods using the state-of-the-art statistical software R;
  • interpret the results of statistical analyses and translate them into relevant implications as a decision-making aid for managers;
  • present the results of statistical analyses and communicate them to various stakeholders.
Attendance requirements

Attendance is required at least 80% of the time in the course units (this also applies to online units, should this be necessary).

Teaching/learning method(s)

The course integrates theoretical knowledge with practical applications through a combination of interactive lecture units with discussions, analysis of business cases, a group project, and home computer exercises. Please note that the teaching of the content is partly based on the flipped classroom principle, i.e., the content must be worked on as preparation for the units in self-study based on the materials provided. In particular, an online script with explanatory texts and videos is provided for this purpose. In order to be prepared for the weekly units, the content of the respective week must be worked through. During the on-campus units, we focus on problem-solving and practical applications. For the participation grade, it is important to actively participate in the units and the discussions and to ask questions about the material if necessary. 

The weekly materials are supplemented by case studies and computer exercises aimed at knowledge transfer. The home computer exercises must be submitted via the Canvas platform by the specified deadline. In addition, the course includes a group project in which students have to analyze a provided data set to draw managerial recommendations. There will be an online forum to facilitate interaction between students and to clarify questions.

The course includes practical applications of data analysis with the statistical software R. R is an open-source software and can therefore be installed and used free of charge. It is recommended to use R via the integrated development environment and graphical user interface RStudio:

Please also use the various free online materials (e.g., 


The assessment is based on the following components: 

  • Retail Marketing Project (group project: data analysis & presentation) - 30%
  • Computer exercises (statistical analysis of data sets) - 30%
  • Final exam (concepts & methods) - 30%
  • Participation (mostly measured in weekly tests) - 10%

In order to ensure a fair division of labor in the group project, a peer assessment is carried out among the group members at the end of the semester, which is taken into account when calculating the final grade. This means that each group member evaluates the other group members according to their contribution. 

To successfully complete the course, the final grade must be at least 60%.

Prerequisites for participation and waiting lists

It is recommended to review courses on statistics and marketing.


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Availability of lecturer(s)

I am happy to answer questions via the online discussions on Canvas or by email ( However, if you have specific methodological questions, please try to find answers first using the materials provided (e.g. the online tutorial).

Last edited: 2024-01-10