Andreas Hamann, Chair of Quantitative Marketing and Consumer Analytics: Digital Marketing and Research Data Platform with BERD@NFDI (August 2023)

Andreas Hamann obtained his bachelor’s and master’s degree in business administration with a focus on marketing and information systems at the Humboldt University of Berlin and the University of Mannheim.

Since September 2021, Andreas has been a doctoral candidate at the Chair of Quantitative Marketing and Consumer Analytics. As such he also joined the structured doctoral program offered by the Center for Doctoral Studies in Business at the University of Mannheim. His research interests center around digital marketing, with a particular focus on machine learning applications in marketing and the role of consumer online privacy.

What is your current research topic?

My research broadly covers topics in digital marketing. One project, for instance, deals with the interplay of corporate misbehavior and stakeholder reactions. Besides that, I am also involved in BERD@NFDI where we aim to create a wholistic research data platform, allowing users to discover, analyze, and learn about high-quality unstructured datasets in business, economics, and the social sciences.

For those who have not yet delved deeply into the topic of Data Science: How would you explain to a child what you are working on?

I am like a detective trying to see how people react and how they should react if something bad happens to their employer. But I am not just looking at a few cases – I am checking lots of them from all around the world. As I cannot do this on my own, I need a computer and some clever tools to figure out what happens.

Everyone talks about Data Science – how would you describe the importance of the topic for yourself in three words?

Empowering, challenging, crucial

What points of contact with Data Science does your work have? Which methods do you already use, and which would be interesting for you in the future?

I am working with large and partly unstructured datasets (e.g., one of them has almost 5M observations). Loading and pre-processing this data as well as running models and investigating their suitability are crucial tasks for me. I am drawing on a mix of techniques, like somewhat more complex panel data models and some machine learning for textual classification tasks.

How high is the value of Data Science for your work? Would your research even be possible without Data Science?

Without data science, my work would not be possible. My research relies on merging large amounts of data and understanding complex interdependencies among them. The size of data I work with makes it basically infeasible to manage without data science techniques.

What development opportunities do you see for the topic of Data Science in relation to your field?

In the field of quant marketing, working with unstructured data has become quite popular. I expect a drift from a current focus on textual data to even more advanced analyses of other unstructured data sources like images, videos, and audio data. This is all already possible and I expect to see more of this in the future. This is also why we are actively offering resources like these on BERD – our research data platform. Besides that, the integration of generative AI tools like ChatGPT into our research process obviously holds immense potential.