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
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
Recent Research Highlights:
Paper on Soda Taxes is named a "Distinguished Winner"
for the AMA-EBSCO-RRBM Award for Responsible Research in Marketing
Together with Anna Tuchman and Song Yao, we study the implementation of a local soda tax in Philadelphia.
We find that many consumers avoid the tax by driving outside of the taxed area which reduces the impact of the
tax both in terms of generating revenue and in terms of improving consumers' nutritional intake.
The video posted on the right provides a more detailed summary of our findings.
Machine Learning and Targeted Marketing
Machine learning methods are increasingly used to design personalized marketing strategies such as targeted
coupons and personalized advertising messages. In a recent talk at the Katia Campo symposium, I talked about
what I see as three important ingredients to designing targeting strategies:
(1) the importance of incremental targeting (i.e. based on treatment effect heterogeneity)
(2) how to derive policies from machine learning estimates by using a decision-theoretic framework
(3) how to compare the performance of ML methods based on the profits that policies derived from a given model
Slide are available here 👉 Katia Campo Price Targeting Slide-deck.
The Sequential Search Model: A Framework for Empirical Research (with Accompanying Code)
The sequential search model (Weitzman, 1979) has emerged as the workhorse model for research based on consumer search data. Papers in this growing literature adopt different specifications of utility, estimate the model using a variety of approaches, and discuss identification in relation to a specific setting. Our aim in this paper (joint work with Elisabeth Honka and Raluca Ursu) is to provide a unified treatment of the aspects of the sequential model that are relevant to empirical work in order to consolidate knowledge and to provide a comprehensive introduction on the use of the sequential model, especially for researchers that are new to working with search data and models.
We also provide a comprehensive code base that covers different methods for computing reservation utilities and 4 different estimation approaches. Click here for access to the code.
Find out more about my recent research projects on my Blog.
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