I am an Assistant Professor of Marketing at the Wharton School of the University of Pennsylvania, where I am affiliated with Analytics at Wharton and the AI for Business initiative. My research explores the intersection of marketing, data science, and machine learning. At Wharton, I teach the class Data and Analysis for Marketing Decisions, for which I've been awarded the Wharton Teaching Excellence award.
For more about my background, check out my CV.
Interested in probablistic machine learning, Bayesian computation, and marketing? I'm also the co-organizer of an interschool virtual reading group on these topics for junior faculty and Ph.D. students, which you can read more about here.
I am an applied methodologist, interested in the intersection of modern probabilistic machine learning and marketing. I focus on problems in customer analytics, preference measurement, and design, with an eye to developing and applying flexible, interpretable, computational tools to drive insights in these domains. I also study how rich, unstructured data like text and images can be used in classical marketing contexts (like preference measurement), and in new contexts (like visual branding and product design) which previously have been difficult to study from a data-driven perspective. Methodologically, I am interested in Bayesian nonparametrics, Bayesian computation, deep generative models, and representation learning. I'm honored to have received several awards for my research, including the 2018 INFORMS Society for Marketing Science Doctoral Dissertation Award, and the 2018 Marketing Section of the American Statistical Association's Doctoral Research Award, as well as to have been a finalist for the 2019 Frank M. Bass award.
- Bayesian Nonparametric Customer Base Analysis with Model-based Visualizations
Ryan Dew and Asim Ansari
Marketing Science, 2018
[Show Abstract] [Paper] [Code Notebook] [Replication Data] [Stan Code]
- Modeling Dynamic Heterogeneity using Gaussian Processes
Ryan Dew, Asim Ansari, Yang Li
Journal of Marketing Research, 2020
[Show Abstract] [Paper]
- Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Logo Design
Ryan Dew, Asim Ansari, Olivier Toubia
Revision invited at Marketing Science.
[Show Abstract] [Working Paper] [Explore Our Data] [Personality-based Logo Generator]
- Detecting Routines in Ride-sharing: Implications For Customer Management
Ryan Dew, Eva Ascarza, Oded Netzer, and Nachum Sicherman
[Show Abstract] [Working Paper]
- Decomposing The Impact of a Free Cancellation Program on Customer Booking Behavior
Yuhao Fan, Ryan Dew, Eric T. Bradlow, Peter Fader
[Show Abstract] [Request Paper]
- Preference Measurement with Unstructured Data, with Applications to Adaptive Onboarding Surveys
- A General, Kernel-based Framework for Capturing Cross-Category Choice Dynamics
Ryan Dew and Yuhao Fan
- An Investigation into Video, Hashtag, and Challenge Success on TikTok
Zijun Tian, Ryan Dew, and Raghu Iyengar