This course covers the development of research projects in the area of AI in marketing. It also covers presentation and discussion of topics related to individual dissertations. Students are trained to present their work at scientific conferences. They receive feedback regarding their individual dissertation projects. In addition, journal articles in the area AI in Marketing and other topics of scientific practice are discussed. Specific topics are determined in the first session and will reflect the specific needs of participants.

This course focuses on research in the area of AI in marketing analytics, which is typically located at the interface of artificial intelligence, machine learning, and marketing analytics.

Upon completion of the course, participants will be able to:

1.     define a research problem in the area of AI in marketing analytics that is novel, non-trivial to solve, and relevant and outline the contribution of the paper;

2.     solve their research questions empirically using structured and unstructured data

3.     present results at scientific conferences;

4.     write-up and polish the paper;

5.     manage the review process.

You need to attend at least 80% of all classes to pass the course (no matter online or physical session). Note that this course benefits from the chance to engage with other PhD students and obtain additional perspectives.

The course uses a combination of student presentations of own dissertation projects, scientific papers in the area of AI in marketing, inputs on aspects of academic practice, and joint discussion of journal articles in the area of AI in marketing analytics.

1.     Preparation and active participation (10%);

2.     Quality of own presentations with regard to scientific quality and presentation skills (70%);

3.     Quality of contribution in reviewing scientific work (20%).

For this course we have the following scale:

·       < 60%                               fail (5)

·       60% bis 69,99%               sufficient (4)

·       70% bis 79,99%               satisfactory (3)

·       80% bis 89,99%               good (2)

·       >= 90%                             excellent (1)

Prerequisites for participation and waiting lists

·       Admission to doctoral or PhD program

·       Topical fit (if in doubt, please contact Prof. Dr. Siham El Kihal)

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