Stephan Seiler Marketing Professor Imperial College London
Associate Professor of Marketing
Associate Professor of Economics (by courtesy)
CEPR Research Fellow
IFS Research Fellow
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
Seminars:
I co-organize the European Quant Marketing Seminar (eQMS).
Preference Heterogeneity, Consumer Search, & Targeted Marketing
(forthcoming in Marketing Science)
It is a well-known stylized fact in quantitative marketing that consumers are highly persistent in their product choices. Such persistence has typically been rationalized through strong heterogeneity in preferences across consumers. In a recent paper with Ilya Morozov, Xiaojing Dong, and Liwen Hou, we show that even mild preferences for specific products can lead to persistent choices if consumers only evaluate a limited set of options. We show that when data on search and purchase behavior is available, we can disentangle the role of preferences and search frictions and, taking search costs into account leads to smaller estimates of preference heterogeneity. As a consequence there is less scope for targeted marketing.
More details in this twitter thread: "Consumer Search & Heterogeneity"
Runner-up for Dick Wittink Best Paper Award
My paper “The Impact of Advertising Along the Conversion Funnel” with Song Yao was the runner-up for the Dick Wittink best paper award at the QME journal. In the paper, we analyze advertising effectiveness along the conversion funnel in brick-and-mortar supermarkets using novel “path-tracking” data on consumers’ movement in the store. The interview below provides a short summary of our findings.
Harnessing the Power of Social Media
Firms are increasingly trying to tab into social media and have users promote products on their behalf. But how effective is such a marketing strategy? In recent research of mine (with Song Yao and Wenbo Wang), we address this question. Stanford Business Insights provides a great summary of our findings (including a video interview, also posted below).
Optimal Price Targeting
In a new paper with Adam Smith and Ishant Aggarwal, 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.
Large-Scale Demand Estimation & Optimal Price Setting in Online Retail
Many online retailers sell hundreds of different products, which makes optimal price setting a difficult task that requires the retailer to understand substitution patterns between similar products in their assortment. In a recent working paper (joint with Tomomichi Amano and Andrew Rhodes) we propose a demand model that leverages information on which products consumers tend to search (i.e. browse) together before making a purchase. Such data on search behavior is often collected by online retailers, more abundant than purchase data, and highly informative about product similarity and hence substitutability.
The presentation posted above (prepared for the TSE Digital Economics conference) provides an overview of our approach. (Note: the version of this paper that is available on SSRN is slightly outdated, an updated version is coming soon. Stay tuned!)
The Impact of Soda Taxes
In our paper on soda taxes (joint with Anna Tuchman and Song Yao, recently published in the Journal of Marketing Research) we study how consumers reacted to the Philadelphia soda tax using detailed supermarket scanner data.
What do we find?
The tax leads to a 34% price increase and a 46% reduction in sales in Philadelphia. A large amount of cross-shopping to stores outside of Philadelphia offsets more than half of the reduction in sales in the city and decreases the net reduction in sales of taxed beverages to only 22%. We find no significant substitution to bottled water and modest substitution to untaxed natural juices.
What does this mean for tax policy design?
The current tax of 1.5 cents/oz is close to revenue-maximizing, but a slightly higher tax could lower sales of taxed beverages with only a small loss in revenue. A tax of 3 cents/oz (which was contemplated in Philadelphia) would have shrunk revenue by 70%. We also show that tax avoidance through cross-shopping severely constrains revenue generation and nutritional improvement. Widening geographic coverage will likely generate both more revenue and a larger decrease in the sales of taxed beverages.
The video below provides more details on our study ...