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 the author of two books: Bayesian Statistics and Marketing (2005, Wiley) that is used to train doctoral students throughout the world, and Seven Summits of Marketing Research (2014) that is used for MBA students. 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 and the American Statistical Association.  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, 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 American Statistical Association, 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.

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 7201 - Marketing Research and Analytics

    Exploration of issues related to data analysis for marketing decisions: costs/benefits of analysis in aggregate; difference between descriptive and structural models; complexities of imperfect information. Prereq: MBA 6252 or 6253. Not open to students with credit for 847.

  • BUSMGT 3333 - Business Analytics: Applied Prescriptive Analytics

    Moving from estimating model parameters, to making data-informed business decisions. Prereq: 3331. Prereq or concur: 3332.

  • BUSML 7227 - Prescriptive Data Analysis

    Focuses on identifying the best course of action to take in response to what is known about the business and its environment.Students will develop skills in translating business objectives, threats and opportunities into actionable data collection and analysis. Students will also become be exposed to and become proficient with concepts spanning the analysis of pre-existing, secondary data. Prereq: MBA 6252 or 6253.

  • BUSML 8253 - Recent Advancements in Marketing Research

    Provide students with exposure to leading marketing scholars and their most current research and give them an opportunity to critically evaluate it. Prereq: Doct standing in BusAdm, or permission of instructor. Repeatable to a maximum of 9 cr hrs or 6 completions.

  • BUSMGT 7223 - Project Management

    Introduces challenges senior managers face in companies that run multiple projects with a focus on overall project portfolio management, project selection, resource allocation between projects & organizational team design. Students identify their strengths & weaknesses in projects with a creative problem-solving exercise & experience real-life applications with visits from local project managers. Prereq: Enrollment in Fisher College of Business Grad program, or permission of instructor. Repeatable to a maximum of 3 cr hrs.

  • 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.

  • BUSML 7244 - Bayesian Analysis

    Overview of recent developments and applications of Bayesian statistical methods in business analytics. Emphasis on developing a conceptual understanding of quantitative models, and their operational translation into methods for data analysis. Prereq: Enrollment in the SMB-A program, or permission of instructor.

  • BUSML 7249 - SMB-A Capstone Projects

    The course provides students with an opportunity to apply, showcase, fine-tune and expand the skills and knowledge acquired in courses in the program by working through substantive real-world analytics projects from initial conception to the production of useful insights and improved decisions. Prereq: Enrollment in SMB-Analytics program.