Brett R. Gordon is a Professor of Marketing at Kellogg School of Management. His research interests are in pricing, advertising, experimentation, promotion, retailing, and competitive strategy. Professor Gordon studies these topics by drawing on methods from empirical industrial organization, econometrics, and machine learning. His articles have appeared in scholarly journals such as American Economic Review, Journal of Political Economy, Marketing Science, Journal of Marketing Research, Quantitative Marketing and Economics, and Journal of Marketing. Four of his papers have received special academic recognition. He twice won the John D. C. Little Award at Marketing Science for best paper and was a Finalist for a third article. Among the two papers that won the Little Award, one paper won the Robert D. Buzzell Best Paper Award from the Marketing Science Institute for its contributions to marketing practice, and the other paper was twice a Finalist for the Long-Term Impact Award at Marketing Science. A fourth paper was a Runner-Up for the Dick Wittink Prize for the best paper published at Quantitative Marketing and Economics. In April 2023, Professor Gordon was appointed a Co-Editor at the Journal of Marketing Research. Previously, he served as an Associate Editor at Management Science and the Journal of Marketing Research and on the Editorial Boards of Marketing Science and Quantitative Marketing and Economics. He is a co-founder of the Quantitative Marketing and Structural Econometrics Workshop, which helps educate graduate students on state-of-the-art empirical techniques.

At Kellogg, he teaches the MBA course on Retail Analytics and Pricing and a Ph.D. course on Structural Models for Quantitative Marketing.

Previously, he was the Class of 1967 Associate Professor of Business at Columbia Business School, which he joined in 2007. He has held visiting positions at University of Chicago's Booth School of Business and at Stanford GSB. He earned both his Ph.D. in Economics and Masters in Information Systems from Carnegie Mellon University.

Pricing, advertising, digital marketing, promotions, innovation, new products, marketing analytics, causal inference, empirical industrial organization, and technology markets

• Ph.D, 2007, Economics, Carnegie Mellon University
• M.S., 2004, Economics, Carnegie Mellon University
• Masters, 2002, Information Systems Management, Carnegie Mellon University
• B.S. (Honors), 2002, Information Systems and Economics, Carnegie Mellon University

• Professor of Marketing, Northwestern University, Kellogg School of Management
• Visiting Scholar, Graduate School of Business, Stanford University, 12/1/2012
• Class of 1967 Associate Professor, Business, Columbia Business School, Columbia University, 6/1/2014
• Associate Professor, Business, Columbia Business School, Columbia University, 8/1/2011
• Assistant Professor, Business, Columbia Business School, Columbia University, 7/1/2011
• Visiting Scholar, Marketing, Graduate School of Business, Stanford University, 3/16/2018
• Associate Professor (with tenure), Marketing, Kellogg School of Management, Northwestern University, 8/31/2021
• Visiting Associate Professor, Marketing, Booth School of Business, University of Chicago, 6/1/2013

• Ned Smith Research Mentorship Award
• Sidney J. Levy Teaching Award
• Winner, Robert D. Buzzell Best Paper, Marketing Science Institute (MSI), 2020
• Winner, John D.C. Little Best Paper Award, INFORMS Society for Marketing Science, 2019
• Finalist, Long-Term Impact Award, INFORMS Society for Marketing Science
• Scholar, Marketing Science Institute (MSI), 2018
• Finalist, Long-Term Impact Award, INFORMS Society for Marketing Science, 2017
• Honorable Mention, Dick Wittink Prize, Quantitative Marketing and Economics journal
• Doctoral Consortium Presenter, INFORMS Society for Marketing Science
• Doctoral Consortium Presenter, INFORMS Society for Marketing Science
• Doctoral Consortium Presenter, INFORMS Society for Marketing Science
• Finalist, John D.C. Little Best Paper Award, INFORMS Society for Marketing Science
• Distinguished Service Award, Management Science, 2013
• Doctoral Consortium Presenter, INFORMS Society for Marketing Science
• Young Scholar, Marketing Science Institute, 2013
• Meritorious Service Award, Management Science, 2010
• Finalist, Frank M. Bass Dissertation Paper Award, INFORMS Society for Marketing Science, 2009
• Winner, John D.C. Little Award for Best Paper, INFORMS Society for Marketing Science
• Dissertation Award, Center for Analytical Research in Technology (CART), 2006
• Dissertation Competition Award, MSI Alden G. Clayton, 2006
• Best Ph.D. Student Teacher Award, Carnegie Mellon University, 2004
• Graduate Student Research Grant, Carnegie Mellon University, 2004
• William Larimer Mellon Fellowship, Carnegie Mellon University, 2002-2005

• Co-Editor, Journal of Marketing Research, 2023
• Associate Editor, Journal of Marketing Research, 2022-2023
• Associate Editor, Management Science, 2021-2023
• Editorial Board, Journal of Marketing Research, 2016-2021
• Editorial Board, Quantitative Marketing and Economics, 2014
• Editorial Board, Marketing Science, 2014-2023
• Editorial Board, International Journal of Research in Marketing, 2012-2015

Retail Analytics and Pricing

Research (MKTG-590-0)
Independent investigation of selected problems pertaining to thesis or dissertation. May be repeated for credit.

Topics in Quantitative Marketing (MKTG-552-0)
This seminar required of 2nd-4th year students exposes students to working papers in current areas of active research. Students read, present, and discuss recent papers with the goal of improving their ability to evaluate a paper's academic contribution and managerial relevance and to further extend their knowledge of models and methods.

Quantitative Marketing: Structural Modeling (MKTG-551-3)
This course provides a foundational understanding of static and dynamic discrete-choice models, with applications drawn from quantitative marketing and economics. The course takes a "hands on" approach to research, with class being a mix of lectures, discussion of articles, and hands-on empirical analysis. Coding assignments are the bulk of the grade.

Business Marketing Strategy

If your business is B2B, here is a rare opportunity to learn from the experts how to deepen your understanding of marketing dynamics and drive profitable growth in the new B2B environment.

Strategic Marketing Communications

Discover how to create effective, strategy-driven marketing campaigns that move customers and consumers in today’s ever evolving digital landscape. Utilizing tools such as insight, positioning and creative brief work, along with new tactical approaches across the communication spectrum, you’ll learn to ask the right questions, and explore frameworks and examples applicable to developing both B2C and B2B marketing communications plans.

Retail Analytics and Pricing (MKTG-462-0)
This course will teach you how to use analytics and data to address decisions faced by retailers and manufacturers. Pricing and promotion decisions are emphasized, with additional coverage on topics such as private labels, product assortment, trade funding, shopper marketing, and more. The course is organized around a hierarchy of topics. We spend roughly one week understanding pricing and promoting to an individual customer. This analysis provides the foundation as we move to more aggregate decisions, such as setting regular and promoted prices at the product level, managing category pricing, and understanding the drivers of store traffic. As we progress through this hierarchy of decisions, we illustrate how different types of data can---or can't---be used to answer managerial questions. A key part of the class is understanding the limitations of different types of data and how better planning can both simplify the analytics and increase your confidence in the findings. This class is very practical and hands-on. Most of the data we analyze is from real-world managerial problems, through collaborations with leading retailers and consulting firms who have brought problem-driven challenges to the classroom. Weekly homework assignments, both individual and group, are paired with in-class cases. There is no final exam.