Senior Researcher
Holger Dietrich
Modelling Consumer Preferences / Advanced Statistics and Method Development
One thing that Holger Dietrich really enjoys about his work is seeing science applied to real life. Especially if it is possible to apply new findings from various disciplines to improve markets. As a trained statistician, Holger considers all kinds of models and methods to generate new insights out of data. He works on research projects together with academic and practice partners and did many projects modeling the consumer behavior based on different kinds of data and different practice areas, e.g., developing a new conjoint method or making marketing mix models. After studying statistics at LMU Munich, he started his career in Method and Product Development at GfK AG, was Head of Fundamental Research at GfK Verein and is now working in the research group Future & Trends at the Nuremberg Institute for Market Decisions.
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Current Topics of interest are
- Quantum probability
- Optimizing decision processes
- Virtual reality
- Augmented reality
Past projects: e.g.,
Professional history
- Nuremberg Institute for Market Decisions, Data Science and Future & Trends
- GfK Verein, Fundamental Research
- GfK AG, Method and product development
- Studied statistics at the LMU Munich
Selected publications
- Buder, F., Hesel, N. & Dietrich, H. (2023, July). Marketing in the metaverse – opportunities and barriers for the creation of marketing value. In Proceedings 11th International Conference on Contemporary Marketing Issues (ICCMI) (pp. 362-363).
- Buder, F., Dieckmann, A., Manewitsch, V., Dietrich, H., Wiertz, C., Banerjee, A., Acar, O. A., & Ghosh, A. (2020). Adoption Rates for Contact Tracing App Configurations in Germany. NIM Research Report.
- Meißner, M., Pfeiffer, J., Peukert, C., Dietrich, H., & Pfeiffer, T. (2020). How virtual reality affects consumer choice. Journal of Business Research, 117, 219–231.
- Hahne, M., Dietrich, H., Dieckmann, A., Gaspar, C., Hofmann, J., Wildner, R., & Brand, M. (2012). Buying decisions: How does objective consumer expertise influence the use of recommendations and product attributes as decision support? In A. Bröder, E. Erdfelder, B. E. Hilbig, T. Meiser, R. F. Pohl, & D. Stahlberg (Hrsg.), TeaP 2012 Abstracts (S. 282). Lengerich: Pabst.
- Dieckmann, A., Dippold, K., & Dietrich, H. Compensatory versus noncompensatory models for predicting consumer preferences. Judgment and Decision Making 4.3 (2009): 200–213.
- Wildner, R., Dietrich, H., & Hölscher, A. HILCA: A NEW CONJOINT PROCEDURE FOR AN IMPROVED PORTRAYAL OF PURCHASE DECISIONS ON COMPLEX PRODUCTS Yearbook of Marketing and Consumer Research, 2007 S. 5–20.