Dr. S (Sebastian) Gabel
I am an assistant professor at the Rotterdam School of Management. My research is located at the intersection of quantitative marketing, machine learning and econometrics. I develop and validate new machine learning methods for modeling the behavior of individual customers. My current projects focus on methodological research in Deep Learning that can be applied to promotion personalization, recommender systems, pricing, and assortment optimization in large-scale retailing settings. In my industry work, I collaborate with leading marketing solution providers and grocery retailers to design and implement advanced machine learning systems for scalable and automated marketing personalization. These marketing solutions have helped retailers to increase their revenue, return on advertising spend, and conversion rates.
For more information, please visit www.sebastiangabel.com.
Publications
Article (5)
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Academic (4)
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Gabel, S., Molitor, D., & Spann, M. (Accepted/In press). The Effect of an Ad Ban on Retailer Sales: Insights from a Natural Experiment. Marketing Science. https://doi.org/10.1287/mksc.2023.0019
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Gabel, S., & Guhl, D. (2022). Comparing the Effectiveness of Rewards and Individually Targeted Coupons in Loyalty Programs. Journal of Retailing, 98(3), 395-411. https://doi.org//10.1016/j.jretai.2021.08.001
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Gabel, S., & Timoshenko, A. (2022). Product Choice with Large Assortments: A Scalable Deep-Learning Model. Management Science, 68(3), 1808-1827. https://doi.org/10.1287/mnsc.2021.3969
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Gabel, S., Guhl, D., & Klapper, D. (2019). P2V-MAP: Mapping Market Structures for Large Retail Assortments. Journal of Marketing Research, 56(4), 557-580. https://doi.org/10.1177/0022243719833631
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Professional (1)
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Gabel, S., Simester, D., & Timoshenko, A. (2024). How Retailers Became Ad Platforms. Harvard Business Review (digital). https://hbr.org/2024/06/how-retailers-became-ad-platforms
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Conference proceeding (1)
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Academic (1)
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Schrage, R., Kenning, P., Guhl, D., & Gabel, S. (2021). Price Personalisation Technology in Retail Stores: Examining the Role of Users' Trust. In International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive: Blending the Local and the Global, Hyderabad, India, December 13-16, 2020 Article 1900 Association for Information Systems. https://aisel.aisnet.org/icis2020/implement_adopt/implement_adopt/7
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PhD Vacancy (1)
The Marketing group at Rotterdam School of Management, Erasmus University seeks a highly motivated PhD student with strong quantitative skills to study the problem of algorithmic biases in marketing.
As machines are trained to analyse complex problems, many tasks that previously required humans are now guided by Artificial Intelligence. Marketing is no exception in this domain. Increasingly, companies use algorithms to design targeted marketing campaigns. Causal Machine Learning is an emerging research field that can learn the causal effect of an intervention and how it varies within a population based on a large set of potential moderating variables. Its use in marketing has been rapidly growing over the last years (Lemmens and Gupta 2020; Esterzon, Lemmens, Van den Bergh 2023).
Unfortunately, algorithms can be discriminatory. The number of cases reporting biases in algorithms has exploded. Algorithms reproduce and amplify biases present in human decisions. They may even inadvertently create new discriminatory outcomes.
This PhD project ambitions to tackle this crucial managerial and societal challenge. The goal will be to better understand the problem of algorithmic biases in the context of targeting marketing campaigns and to develop a novel methodological framework to design effective and fair personalized policies. The project will include large-scale field experiments in collaboration with company partners.
Strong applicants typically have backgrounds in computer science, statistics or econometrics but should have an intrinsic interest for marketing problems. The PhD will be supervised by Prof. Dr. Aurélie Lemmens and funded by a VICI NWO grant.
Awards (4)
Address
Burgemeester Oudlaan 50
3062 PA Rotterdam
Postbus 1738
3000 DR Rotterdam
Netherlands
