Ryan Dew

Ryan Dew
  • Assistant Professor of Marketing
  • Govil Family Faculty Scholar

Contact Information

  • office Address:

    755 Jon M. Huntsman Hall
    3730 Walnut Street
    University of Pennsylvania
    Philadelphia, PA 19104

Research Interests: marketing analytics, data-driven design, decision support, preference measurement

Links: Personal Website

Overview

Ryan Dew is an Assistant Professor of Marketing and the Govil Family Faculty Scholar at the Wharton School of the University of Pennsylvania. His research explores how machine learning and Bayesian statistical methodologies can solve applied marketing problems and enhance the capacity of marketers to make data-driven decisions. Methodologically, he uses techniques from machine learning, Bayesian nonparametrics, and Bayesian econometrics.

For up-to-date information about Professor Dew and his research, please visit his website: www.rtdew.com

 

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Research

Teaching

Past Courses

  • ECON4999 - Independent Study

    Individual study and research under the direction of a member of the Economics Department faculty. At a minimum, the student must write a major paper summarizing, unifying, and interpreting the results of the study. This is a one semester, one c.u. course.

  • MKTG2120 - Data & Anlz For Mktg Dec

    This course introduces students to the fundamentals of data-driven marketing, including topics from marketing research and analytics. It examines the many different sources of data available to marketers, including data from customer transactions, surveys, pricing, advertising, and A/B testing, and how to use those data to guide decision-making. Through real-world applications from various industries, including hands-on analyses using modern data analysis tools, students will learn how to formulate marketing problems as testable hypotheses, systematically gather data, and apply statistical tools to yield actionable marketing insights.

  • MKTG3990 - Independent Study

  • MKTG7120 - Data & Anlz For Mktg Dec

    This course introduces students to the fundamentals of data-driven marketing, including topics from marketing research and analytics. It examines the many different sources of data available to marketers, including data from customer transactions, surveys, pricing, advertising, and A/B testing, and how to use those data to guide decision-making. Through real-world applications from various industries, including hands-on analyses using modern data analysis tools, students will learn how to formulate marketing problems as testable hypotheses, systematically gather data, and apply statistical tools to yield actionable marketing insights.

  • MKTG8990 - Independent Study

    A student contemplating an independent study project must first find a faculty member who agrees to supervise and approve the student's written proposal as an independent study (MKTG 899). If a student wishes the proposed work to be used to meet the ASP requirement, he/she should then submit the approved proposal to the MBA adviser who will determine if it is an appropriate substitute. Such substitutions will only be approved prior to the beginning of the semester.

  • MKTG9570 - Empirical Models Mktg B

    This course is designed to generate awareness and appreciation of the way several substantive topics in marketing have been studied empirically using quantitative models. This seminar reviews empirical models of marketing phenomena including consumer choice, adoption of new products, sales response to marketing mix elements, and competitive interaction. Applies methods and concepts developed in econometrics and statistics but focuses on substantive issues of model structure and interpretation, rather than on estimation techniques. Ultimately, the goals are a) to prepare students to read and understand the literature and b) to stimulate new research interests. By the end of the course, students should be familiar with the key issues and approaches in empirical marketing modeling.

  • MKTG9950 - Dissertation

    Dissertation

  • MKTG9999 - Independent Study

    Requires written permission of instructor and the department graduate adviser.

In the News

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Activity

Latest Research

Zijun Tian, Ryan Dew, Raghuram Iyengar (2024), Mega or Micro? Optimal Influencer Selection by Follower Elasticity, Journal of Marketing Research.
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In the News

Markets Brace for Tariffs and Look to AI for Relief

Jeremy Siegel examines how investors are responding to geopolitical uncertainty, looming tariffs, and the promise of AI-driven productivity.…Read More

Knowledge @ Wharton - 2025/06/27
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