Stephan Seiler Marketing Professor Imperial College London
Associate Professor of Marketing
Associate Professor of Economics (by courtesy)
CEPR Research Fellow
CESifo Research Network Fellow
IFS Research Fellow
Imperial College Business School
South Kensington Campus
London SW7 2AZ
Email: Stephan.a.seiler@gmail.com
I use data to understand how consumers make choices in settings
ranging from laundry detergent discounts to choosing a hospital for
a bypass operation. I am particularly interested in how consumers gather
information before making a purchase and what we can learn from data on
consumer search behavior.
I am an Associate Editor at Marketing Science, Quantitative Marketing and Economics,
and the Journal of Industrial Economics. I co-organize the European Quant Marketing Seminar (eQMS),
and I am a Public Editor at @QME_Journal.
Google Scholar profile
SSRN profile
LinkedIn profile
Twitter: https://twitter.com/SeilerStephan
Recent Research Highlights:
Finalist for 🏆 Best Paper Award 🏆
Our work on soda taxes (joint work with Anna Tuchman and Song Yao) was a finalist for the Paul E. Green award for the best paper published in the Journal of Marketing Research in 2022.
State Dependence in Brand Choice
In a new paper with Julia Levine we analyze whether consumers that start purchasing new brands persist in their choices. We leverage stock-outs due to hurricanes that force to consumers to switch to a different brand because their preferred product is not available. Interestingly, we find that switches due to hurricane stock-outs do not exhibit any persistence and consumers switch back to their pre-hurricane purchases immediately. Therefore, we conclude that consumers do not exhibit structural state dependence in their brand choices. We rule out that our null result is driven by unusual purchases during a hurricane or context-specific purchase behavior when preparing for a hurricane.
Optimal Price Targeting
In a new paper with Adam Smith and Ishant Aggarwal (forthcoming in Marketing Science), we compare different approaches to estimating demand with consumer panel data with the goal of deriving personalized pricing policies. We provide a unified framework that lets us compare different modeling approaches with different data inputs in terms of the profits they generate when a targeted pricing policy is derived from each model.
We find that purchase histories are useful inputs in any model of demand, whereas demographics information has a small impact on the profitability of pricing policies. Model performance is variable with a Bayesian hierarchical model generating the largest profit gain followed by regularized regression and a neural network. We also find that measures of model fit are almost uncorrelated with predicted profits from a targeted pricing policy and hence statistical fit does not provide useful guidance to model selection.
More detail are in this twitter thread about the paper.
Find out more about my recent research projects on my Blog.
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