Our Courses for Master Students

• Semester hours: 4
• Event type: Lecture + Exercise
• Offered in: Winter semester
• Course Language: German
• Credits: 6 Credits
• Exam date: refer to Campus 
• Exam type: written exam
• Lecture documents: refer to ILIAS 

Learning objectives

Knowledge of the potential insights to be gained from empirical research in business administration as well as its limits; an overview of the general approach to experiments, surveys and studies on the basis of secondary data. In addition, students can gage the strengths and weaknesses of different forms of data collection and are able to use multivariate methods of data analysis as well as interpret their results.

Content

Basics of empirical research; methods of data collection (surveys, experimental research, secondary data research, qualitative research); (multivariate) data analysis and evaluation, e.g., analysis of variance, regression analysis, structural equation modeling, cluster analysis; guest lectures by industry professionals.

Literature

A reference list will be indicated in the course.

• Semester hours: 4
• Event type: Lecture + Exercise
• Offered in: Summer semester
• Course Language: English 
• Credits: 6 Credits
• Exam date: refer to Campus 
• Exam type: written exam
• Lecture documents: refer to ILIAS 

Learning objectives

• Professional skills: Students are able to understand and critically evaluate the complex interplay between influencing factors and current developments in product and price management.
• Methodological skills: Students transfer and apply theoretical knowledge to practical questions
• Social skills: Students are able to work in teams and to communicate in English

Content

• Product Management: basics of product management, innovation management, brand management, management of established products.
• Price Management: basics of price management, basics of classical price theory, basics of behavioral science, pricing theory
• Guest lectures

Literature

A reference list will be indicated in the course.

General Information:

• Choice of 2 Courses

• 1. Digital Marketing Strategies & Business Models
  2. Applied Marketing Research
  3. Basics of Machine Learning in Marketing

• The two courses must be completed within the same semester
• The curriculum will likely be expanded in the winter semester 2022/23. 
• Course Language: German
• 3 Credits for each course

1. Digital Marketing Strategies and Business Models

• Content: The course provides insights into digital marketing strategies as well as the effects and potentials of different marketing channels (e.g., SEO, SEA, Social Media). It examines key elements of a digital strategy - relevance of devices (mobile, tablet), website, concepts of usability, apps, market resarch and content management. The following topics are discussed during the course:
  • overview of Key Performance Indicators (KPIs)
  • interaction of KPIs
  • elaboration of a KPI dashboard
  • combination of digital and non-digital measures in order to calculate return on investment (RoI)
• Learning objectives: 
  • digital strategy  - overview
  • digitization of business models
  • digital effects on B2B and B2C business models
  • digital revenue added value (incl. business case)
• Format: interactive lecture with case studies
• Exam type: case study presentation & term paper

2. Applied Marketing Research

• Content: The course aim is to prepare master sudents and doctoral candidates for their thesis in the field of marketing. It provides guidance through the discussion of papers as well as students' own empirical practice in order to help students make the "right" decisions during different phases of a research project. These phases include:
  • definition of the problem,
  • conceptual framework,
  • study design,
  • data collection and analysis using software (e.g. R Studio, EViews),
  • documentation
• Learning objectives:
  
  • evaluation of existing studies in marketing research
  • definition of scope and design of marketing research
  • learn quantitative (and qualitative) methods of marketing research
  • write marketing research studies
• Format: interactive lecture incl. software
• Exam type: written review of a scientific paper

3. Basics of Machine Learning in Marketing

• Content: It is important for master students and doctoral candidates in the field of marketing, other fields of business administration or social studies to know the basics of machine learning and its numerous potential applications, because machine learning contributes majorly to the rapid development of artificial intelligence. The course meets this need and discusses the following topics from a marketing perspective: 
  • basics of machine learning
  • selected methods of supervised learning
  • introduction to artificial neural networks
  • basics of the programming language Python
• Learning objectives:
  • understanding basic concepts and methods of machine learning as well as their application within marketing
  • training of students' own models in Python based on a compact, practical introduction to programming in Python 
• Format: interactive Lecture using examples of machine learning applications

• Exam type: term paper & presentation of results

   

Please find an overview of the interdisciplinary qualifications (FÜSQ) offered by the Department of Business Administration and Marketing here: