Greg Allenby

Helen C. Kurtz Chair in Marketing
Professor of Marketing
Professor of Statistics

Marketing & Logistics


Professor Allenby's research focuses on the development and application of quantitative methods in marketing.  He is a co-author of Bayesian Statistics and Marketing (2005, 2024 Wiley). His research is used to improve product, pricing, promotion and targeting strategies at leading firms.

He is a fellow of the Informs Society for Marketing Science (ISMS) and the American Statistical Association (ASA).  He is past editor of Quantitative Marketing and Economics, and past associated editor of Marketing Science, Management Science, Journal of Marketing Research and the Journal of Business and Economic Statistics. Within the American Marketing Association (AMA), Greg has served as Vice President of the Research Council and has chaired the Advanced Research Technique (ART) Forum, a national conference that brings together quantitative researchers from industry and academia. Within the ASA, he has served as Chair of the Section on Statistics in Marketing.  He has authored over 100 publications that have appeared in leading journals in marketing, statistics, and economics.  

Greg is the winner of the 2024 AMA Paul Converse award for contributions to the science of marketing, the 2023 AMA Gil Churchill lifetime achievement award for contributions to marketing research, the 2012 AMA Parlin Award for leadership and impact on the profession of marketing research over an extended period of time, and the 2010 ISMS Long Term Impact Award.


Areas of Expertise

  • Applied Bayesian Modeling in Marketing
  • Statistics


  • Ph.D., 1988, Graduate School of Business, University of Chicago (Statistics and Marketing). Dissertation Title: "The Identification, Estimation and Testing of Demand Structures."
  • M.B.A., 1986, Graduate School of Business, University of Chicago (Statistics and Behavioral Science).
  • M.S., 1981, Illinois Institute of Technology (Operations Research).
  • B.S., 1978, Ohio Northern University (Mechanical Engineering).


BUSML 7243 - Prescriptive Analytics
Focus on prescriptive analytics to determine causal effects and identify optimal decisions in business. Emphasis on programming data analysis using the R statistical language. Prereq: Enrollment in the SMB-A program, or permission of instructor.