Research Overview:

I study how modern statistical and computational methods can enhance the capacity of marketing managers to make data-driven decisions. I am particularly interested in topics in marketing analytics, customer relationship management, and preference measurement, with a focus on understanding dynamics in consumer purchasing patterns. I am also interested in incorporating visual and textual data into marketing models, with a specific interest in understanding the interplay between the stated and visual identities of brands, and in bringing big data to bear on firms' design decisions. Methodologically, I focus on techniques from machine learning, Bayesian nonparametrics, and Bayesian econometrics.

  • Bayesian Nonparametric Customer Base Analysis with Model-based Visualizations
    Ryan Dew and Asim Ansari
    Marketing Science, 2018
    [Show Abstract] [Paper] [Stan]

Working Papers:
  • Dynamic Preference Heterogeneity
    Ryan Dew, Yang Li, Asim Ansari
    Revision invited at Journal of Marketing Research.
    [Show Abstract] [Working Paper]

Research in Progress:
  • Letting Logos Speak: Deep, Probabilistic Models for Logo Design
    Ryan Dew, Asim Ansari, Olivier Toubia
    [Show Abstract]

  • Customer-Centric Data Fusion
    Ryan Dew and Oded Netzer
    [Show Abstract]

  • Scalable Decision Support Systems for Robust CRM
    Ryan Dew and Asim Ansari
    [Show Abstract]

  • Bayesian Optimization of Online Ad Bidding
    Ryan Dew and Kinshuk Jerath
    [Show Abstract]