About Me:

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.


Interested in my research or in collaborating? Feel free to email me at ryandew@wharton.upenn.edu. You can also find me on LinkedIn and on Google Scholar.


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.

Research Overview:

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 which previously have been difficult to study from a data-driven perspective, like branding and design. 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 Frank M. Bass and Paul Green Awards.


Publications:
  • Bayesian Nonparametric Customer Base Analysis with Model-based Visualizations
    Ryan Dew and Asim Ansari
    Marketing Science, 2018
    This paper was a finalist for the 2019 Frank M. Bass Award.
    [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
    This paper was a finalist for the 2020 Paul Green Award.
    [Show Abstract] [Paper]

  • Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Logo Design
    Ryan Dew, Asim Ansari, Olivier Toubia
    Forthcoming, Marketing Science.
    [Show Abstract] [Paper] [Explore Our Data] [Personality-based Logo Generator]

Working Papers:
  • Detecting Routines in Ride-sharing: Implications For Customer Management
    Ryan Dew, Eva Ascarza, Oded Netzer, and Nachum Sicherman
  • [Show Abstract] [Working Paper]

  • A Gaussian Process Model of Cross-Category Dynamics in Brand Choice
    Ryan Dew, Yuhao Fan
  • [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]

Research in Progress:
  • Preference Measurement with Unstructured Data, with Applications to Adaptive Onboarding Surveys
    Ryan Dew
  • [Show Abstract]

  • Winning The Attention Race: Analyzing Video Popularity And Content Evolution On TikTok
    Zijun Tian, Ryan Dew, and Raghu Iyengar
  • [Show Abstract]