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Professor of Marketing 

Imperial College Business School

South Kensington Campus                               

London SW7 2AZ

Faculty Profile Page

Email: Stephan.a.seiler@gmail.com

Google Scholar profile

SSRN profile

LinkedIn profile  

Twitter: https://twitter.com/SeilerStephan

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 a Co-Editor at Quantitative Marketing and Economics and an Associate Editor at

Management Science, the Journal of Marketing Research, and the Journal of Industrial

Economics.  I also co-organize the European Quant Marketing Seminar (eQMS).

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Recent Research:

Updated Working Paper: Demand Estimation with Text and Image Data 

New machine learning tools allow researchers to process unstructured data from text and images more easily. In this new paper with Giovanni Compiani and Ilya Morozov, we extract product features from unstructured images and text data and then feed them into a mixed logit demand model. We show that this approach outperforms characteristics-based models in predicting second choice in an online choice experiment and consistently predicts substitution patterns in real-world data across 40 product categories. 

We also also make code for our "DeepLogit" approach available here:

 https://github.com/deep-logit-demand/deeplogit

Find out more about my recent research projects on my Blog.

... or follow me on Twitter ... 

🏆  🏆 My selection of the best quant marketing papers of 2024 🏆  🏆

I started putting together an annual list of my favorite marketing papers in a twitter thread since 2020. Older lists can be found here:

Best-of-2020, Best-of-2021, Best-of-2022, Best-of-2023

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Paper on Soda Taxes featured on the "How I Wrote This" podcast.

Our paper on soda taxes (with Anna Tuchman and Song Yao was covered on the “How I Wrote This” podcast.  We discuss what motivated us to pursue this research,  the various  decisions that we took when working  on the paper, and how we navigated the review process. 

🚨 New Paper (forthcoming at Marketing Science) 🚨
How Much Influencer Marketing is Undisclosed? Evidence from Twitter

In this new paper with Daniel Ershov and Yanting He, we develop a new method to detect undisclosed sponsored content on Twitter.  We gather a novel data set of over 100 million posts across 268 brands from 2014 to 2021 and find that 96% (!!!) of sponsored content

is undisclosed. Despite tightening regulation, the share of undisclosed content decreases only slightly over time. Undisclosed content is more likely to originate from younger brands with a large Twitter following, suggesting that disclosure might remain low in the future.

Check out the video below for more details 👇👇👇

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