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. I am also a faculty affiliate of the Penn Eidos LGBTQ+ Health Initiative.


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 probabilistic 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 classic data-driven marketing contexts, like preference measurement, and in 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 2022 Frank M. Bass Award, the 2018 INFORMS Society for Marketing Science Doctoral Dissertation Award, and the 2018 Marketing Section of the American Statistical Association's Doctoral Research Award. My papers have also been finalists for the John D.C. Little Award and Paul Green Awards. In recognition of my research, I was named a 2023 MSI Young Scholar.


Publications:
  • Bayesian Nonparametric Customer Base Analysis with Model-based Visualizations
    Ryan Dew and Asim Ansari
    Marketing Science, 2018
    • Finalist, 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
    • Finalist, 2020 Paul Green Award
    [ Show Abstract ] [ Paper (Open Access) ]

  • Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Logo Design
    Ryan Dew, Asim Ansari, Olivier Toubia
    Marketing Science, 2022
    • Winner, 2022 Frank M. Bass Award
    • Finalist, 2022 John D.C. Little Award
    [ Show Abstract ] [ Paper ] [ Explore Our Data ] [ Personality-based Logo Generator ]

  • Detecting Routines: Applications to Ridesharing CRM
    Ryan Dew, Eva Ascarza, Oded Netzer, and Nachum Sicherman
    Journal of Marketing Research, 2024
    [ Show Abstract ] [ Paper (Open Access) ] [ Code ]

  • Mega or Micro? Influencer Selection Using Follower Elasticity
    Zijun Tian, Ryan Dew, and Raghu Iyengar
  • Journal of Marketing Research, 2024
    [ Show Abstract ] [ Paper ] [ Web Appendix ] [ Journal ]
    [ Knowledge@Wharton ] [ YouTube ]

  • Adaptive Preference Measurement with Unstructured Data
    Ryan Dew
    Forthcoming, Management Science
  • [ Show Abstract ] [ Paper ] [ Web Appendix ] [ Code ]

Working Papers:
  • Correlated Dynamics in Marketing Sensitivities
    Ryan Dew, Yuhao Fan
    Last updated: March 2024
  • [ Show Abstract ] [ Working Paper ]

  • Probabilistic Machine Learning: New Frontiers for Modeling Consumers and their Choices
    Ryan Dew, Nicolas Padilla, Lan E. Luo, Shin Oblander, Asim Ansari, Khaled Boughanmi, Michael Braun, Fred Feinberg, Jia Liu, Thomas Otter, Longxiu Tian, Yixin Wang, and Mingzhang Yin
    Last updated: April 2024
  • [ Show Abstract ] [ Working Paper ] [ Code Companion ]

  • Your MMM Is Broken: Identification of Nonlinear and Dynamic Effects in Marketing Mix Models
    Ryan Dew, Nicolas Padilla, Anya Shchetkina
    Authors contributed equally. Last updated: June 2024
  • [ Show Abstract ] [ Available by Request ]

Selected Research in Progress:
  • Unified Marketing Measurement and Optimal Test Timing
    with Nicolas Padilla

  • Graph Representation Learning for Inferring Market Structure
    with Mingyung Kim

  • How Do Influencers Learn From Feedback?
    with Zijun Tian and Raghu Iyengar

  • Bayesian Analysis of A/B Tests with Partially Observed Assignment: An Application to Free Cancellation Programs
    Yuhao Fan, Ryan Dew, Eric T. Bradlow, Peter Fader