Marketing

Master Thesis Topics

On this page you can find a list of possible research topics for a master thesis. This list is not exhaustive, but can be supplemented with your own topic suggestions or extensions to our proposed topics. For an insight into the expertise and research interests of our team members, also take a look at the personal pages in the "Team" section of our website and feel free to directly get in touch with the person that best fits your suggested topic or interests.

Available projects
 

Emotional Cues and Music Streaming

  • Public discussions about major sport events (e.g., Olympic games) often involve the idea that sports events provide emotional benefits to people, in particular if teams win that are supported by a given population. The question then arises of whether these emotional consequences can be measured. If so, one potential measurement could arise through the music that consumers listen to.

  • To address this question, this thesis will analyze music streaming behavior on Spotify around major sport events that happened in the last years.

  • This thesis requires knowledge of and some experience with R or Python.

  • Supervisor: Prof. Dr. Dominik Papies
     

How useful are Machine Learning techniques for marketing problems?

  • Recent years have seen a surge in the development and application of machine learning algorithms.

  • The goal of this thesis is to apply selected machine learning methods to marketing problems and compare it to traditional methods to assess the extent to which these new methods can improve marketing decision making. One domain where this can be useful is the detection of competitive relationships between a large number of brands in retailer settings.

  • This thesis is suitable for students who have experience with empirical work and good programming skills in R.

  • Supervisor: Prof. Dr. Dominik Papies


Job market requirements for business students

  • The job market for business school graduates has changed substantially in recent years. Digitization as well as the wide-spread availability of data in many work settings have likely altered the profile of skills that employers require. The extent, however, to which employers’ requirements have changed is unclear, as well as a structured assessment of what employers require today.

  • To address this void, this thesis will collect a large number of job ads (using an automated approach) and analyze the text contained in these job ads in a structured way, using state-of-the-art text mining techniques.

  • Based on the student’s preferences, the thesis may focus on a particular domain specialization, e.g., marketing or accounting.

  • This thesis requires knowledge of and good experience with R or Python.

  • Supervisor: Prof. Dr. Dominik Papies

 

Competition in the German gasoline market

  • The German gasoline market differs from other gasoline markets because it is characterized by a very high frequency of price changes, i.e., a typical gas station changes its prices multiple times a day. The question that arises is to what extent these price changes are predictable and whether dynamic cycles of price increases or decreases are always initiated by the same stations or brands, or whether these patterns are entirely unpredictable.

  • To address these questions, the thesis will analyze a large data set that contains all price changes of all stations in the German gasoline market over several years. 

  • This thesis requires knowledge of R and reasonable econometric knowledge.

  • Supervisor: Jun.-Prof. Dr. Wiebke Keller & Prof. Dr. Dominik Papies

 

Package size supply and socio-economic status

  • Anecdotic evidence suggests that some firms in regions with consumers of lower socio-economic status may offer products in larger package sizes compared to regions with consumers of higher socio-economic status to capitalize on the hypothesis that consumers with lower socio-economic status make potentially less controlled or educated choices.

  • The goal of this thesis is to test whether firms indeed offer large package sizes in these areas. The analysis will use a data set that will be provided to the candidate by the supervisor. This thesis is suitable for students who have some experience with empirical work and programming in R.

  • Supervisor: Prof. Dr. Dominik Papies
     

Analyzing firm-generated CSR reports 

  • Corporate social responsibility (CSR), i.e., the contribution of business resources to the improvement of societal well-being, plays an increasing role for customers and hence also for businesses. Firms have an intrinsic motivation to communicate their CSR engagement through press releases, social media, their annual reports, or through dedicated CSR reports. Unlike in financial reporting, there is currently no commonly agreed set of reporting standards in CSR reports, and, mostly, CSR reporting is voluntary. 
     
  • The goal of this thesis is to gather relevant data (e.g., CSR reports) and systematically analyze it. Using state-of-the-art text mining techniques, a thesis could, for example, analyze, what topics companies talk about in their CSR reports, and/or whether firms also mention CSR-related activities in their annual reports. 
     
  • This thesis is suitable for students with good programming skills in R or Python.
     
  • Supervisor: Prof. Dr. Dominik Papies with David Gremminger, M.Sc. 
     

New (machine learning) methods for causal inference  

  • In recent years, a multitude of new methods for causal inference from observational data have been developed by researchers from very different fields (econometrics, machine learning, computer science, epidemiology, ...). Examples include, among others, double machine learning, causal forests, deep instrumental variables, front-door adjustment, causal discovery, and targeted maximum likelihood. In many cases, these methods have laid the theoretical groundwork, but have not yet been applied widely to typical questions from business or economics. 
     
  • The goal of this thesis is to select from these methods and a) compare multiple methods from different disciplines that have the same goal, and/or b) assess the applicability of one of the new methods to typical research questions and datasets from business and economics, and/or c) explore how results from influential studies in business and economics (using traditional methods) would change, when one of the new methods is applied.
     
  • This thesis is suitable for students who have experience with empirical work, good programming skills in R or Python, and reasonable econometric knowledge.
     
  • Supervisor: Prof. Dr. Dominik Papies with Jonathan Fuhr, M.Sc.

Master Thesis with mine&make GmbH

  • Technical Components | Data Science | Tech-Company

  • Master-Thesis‚ Methodology to determine the market potential of a technical product by leveraging Data-Analytics’ - for further information please see here.

Ongoing Projects

 

Finished Projects