Brett R. Gordon joined Kellogg in 2014 as an Associate Professor of Marketing. His research interests include pricing, advertising, promotions, retailing, innovation, and competitive strategy. Professor Gordon studies these topics by drawing on methods from empirical industrial organization, econometrics, and statistics. His articles have appeared in scholarly journals such as American Economic Review, Journal of Political Economy, Marketing Science, Journal of Marketing Research, and Quantitative Marketing and Economics. His paper on replacement cycles in the PC processor market received the John D.C. Little Award in 2009 for the best paper appearing in Management Science or Marketing Science. Another paper published in 2013 on advertising effects in presidential elections was also nominated for this award.

Professor Gordon is actively involved in the academic field. He serves as an Associate Editor for Quantitative Marketing and Economics and as an Editorial Board member for Marketing Science and the International Journal of Research in Marketing. 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.

Previously, he was the Class of 1967 Associate Professor of Business at Columbia Business School, which he joined in 2007. He earned both his Ph.D. in Economics and Masters in Information Systems from Carnegie Mellon University. At Kellogg he teaches the MBA course on Retail Analytics and a Ph.D. course on Structural Models for Quantitative Marketing.

Quantitative Marketing, Empirical Industrial Organization, Pricing, Advertising, Innovation, Online Marketing, Dynamic Oligopoly, Market Structure

• 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

• Associate Professor (with tenure), Marketing, Kellogg School of Management, Northwestern University, 2014-present
• Visiting Associate Professor, Marketing, Booth School of Business, University of Chicago, 2013-2013
• Visiting Scholar, Graduate School of Business, Stanford University, 2012-2012
• Class of 1967 Associate Professor, Business, Columbia Business School, Columbia University, 2011-2014
• Associate Professor, Business, Columbia Business School, Columbia University, 2011-2011
• Assistant Professor, Business, Columbia Business School, Columbia University, 2007-2011

• Honorable Mention, Dick Wittink Prize, Quantitative Marketing and Economics journal
• Finalist, John D.C. Little Best Paper Award, INFORMS Society for Marketing Science
• Doctoral Consortium Presenter, INFORMS Society for Marketing Science
• Doctoral Consortium Presenter, INFORMS Society for Marketing Science
• Doctoral Consortium Presenter, 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
• John D.C. Little Award for Best Paper, INFORMS Society for Marketing Science, 2009
• 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

• Editorial Board, Journal of Marketing Research, 2016
• Associate Editor, Quantitative Marketing and Economics, 2014-Present
• Editorial Board, Marketing Science, 2014-Present
• Editorial Board, International Journal of Research in Marketing, 2012-Present

Retail Analytics

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-style class exposes students to working papers in current areas of active research. 2nd, 3rd, and 4th year students are required to enroll. Participants will 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. This class will reinforce the research environment and foster collaboration between students and faculty. One or more faculty can teach the course, and we expect to rotate it among the faculty on a consistent basis. Faculty who teach this course must be careful to coordinate the content relative to the three core classes.

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 will take a "hands on" approach to research. Class will be a combination of lectures, discussions of assigned articles, and hands-on empirical analysis. What you get out of this course depends on what you are willing to put into it. To this end, the course has three broad goals: (i) to provide students with a conceptual understanding of key empirical methods; (ii) to expose students to classic papers and recent research in marketing and empirical industrial organization in economics; (iii) to give you the hands-on experience of implementing some of these methods in code. The course material can be divided into three roughly equal parts: (1) Static discrete-choice models with individual-level data; (2) Static discrete-choice models with aggregate-level data; (3) Dynamic discrete-choice models with individual- or aggregate-level data. I encourage students from other departments in Kellogg or outside the business school to enroll. Feel free to contact me if you have questions about whether your background is suitable for the class.

Quantitative Marketing: Analytic Modeling (MKTG-550-0)
In this course you will learn what analytic models are, their purposes, and how to build and analyze an analytic model to provide insight into an applied research problem. Analytic models in Marketing are used in many substantive areas, including competitive marketing strategy, pricing, product design, distribution channel design/management, and sales force compensation. They provide insight into the impacts of market forces, competitive actions, consumer segmentation characteristics, and firms' own choices on outcomes such as profit, sales, and welfare. The course uses these literatures as well as exercises to both give you broad information on these topics, and equip you to use analytic modeling in your own work. This course builds on prior course content in the Economics core coursework.

Strategic Marketing Communications in the Digital Age

Explore the power of an integrated marketing communications strategy from fundamental planning to tactical execution utilizing the most current thinking about media channels, customer engagement and measurement. The frameworks and examples provided are applicable to developing both B2C and B2B marketing communications strategies.

Retail Analytics (MKTG-462-0)
This course will teach you how to use analytics and data to address decisions faced by retailers and manufacturers. Decisions around pricing and promotions are emphasized, but we also address other topics in retailing. The course is organized around a hierarchy of topics in retailing. We spend roughly one week understanding pricing and promoting to an individual customer. We then gradually move to more aggregate decisions: products, categories and the store. As we progress through this hierarchy of retail decisions, we illustrate how different types of data can be used to answer managerial questions. We categorize data into two broad types: opportunistic data and designed data. A key part of the class is understanding the limitations of different types of data. There will be weekly homework assignments that are a mix of individual and group work. There is no final exam. This class is very practical and hands-on. Most of the data we will analyze is from real-world managerial problems. The class has evolved over the years with collaborations from leading retailers and consulting firms who have brought problem-driven challenges to the classroom.