Stephan Seiler Marketing Professor Imperial College London
Professor of Marketing
Imperial College Business School
South Kensington Campus
London SW7 2AZ
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).
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 ...
🚨 New Working Paper: 🚨
Causal Inference with Endogenous Price Response
(with Przemyslaw Jeziorski and Dingzhe Leng).
In marketing and economics, researchers are often interested in how a given treatment (advertising, word-of-mouth, product labels) shifts demand. When other marketing variables such as price adjust due to the treatment, researchers often control for such variables.
Using price as an example we show that including such endogenous controls creates bias even if treatment is randomly assigned. We sign and quantify the bias, derive testable condition for whether price adjustments occur, and show how to use instrument to avoid bias arising from endogenous controls.
Finally, we show that the bias from endogenous controls can be substantial when estimating the impact of feature advertising in multiple CPG categories.
🏆 🏆 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:
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.
How Much Influencer Marketing is Undisclosed? Evidence from Twitter
(forthcoming at Marketing Science)
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 👇👇👇