About

Greg Allenby is the Helen C. Kurtz Chair in Marketing and a Professor of Marketing and Statistics at the Fisher College of Business. His research focuses on the development and application of quantitative methods in marketing, with particular emphasis on Bayesian modeling. He is a co-author of the book Bayesian Statistics and Marketing, published in 2005 and 2024 by Wiley. His work is utilized to improve product, pricing, promotion, and targeting strategies at leading firms. Allenby has authored over 100 publications in leading journals across marketing, statistics, and economics. He holds a Ph.D. from the Graduate School of Business at the University of Chicago, earned in 1988, with a dissertation titled "The Identification, Estimation and Testing of Demand Structures." His academic background also includes an M.B.A. from the University of Chicago, an M.S. from Illinois Institute of Technology, and a B.S. from Ohio Northern University. Allenby has served as an editor for several prominent journals and has held leadership roles within the American Marketing Association and the American Statistical Association. He has received numerous awards, including the 2024 AMA Paul Converse Award, the 2023 AMA Gil Churchill lifetime achievement award, the 2012 AMA Parlin Award, and the 2010 ISMS Long Term Impact Award.

Research topics

• Computer Science
• Information Retrieval
• Econometrics
• Artificial Intelligence
• Mathematics
• Natural Language Processing
• Data science
• Marketing
• Business
• Linguistics
• Economics
• Statistics

Selected publications

• Re-examining the no-choice option in conjoint analysis

  Journal of Choice Modelling · 2025-10-18

  articleOpen accessSenior author

  The validity of using conjoint analysis to conduct an economic evaluation of product characteristics rests on the inclusion of brand names, prices, and an outside “no-choice” option in the choice task. The no-choice option is included in case respondents determine that some other offering, not included in the conjoint choice task, is preferred to those that are included and that it would be better to hold onto their money and not make a purchase at that time. Selecting the no-choice option assumes that respondents have some level of knowledge of the value and prices of goods in the market. In this paper, we show that survey respondents may lack this information and make inferences about market prices from the conjoint exercise itself. This learning effect is especially problematic for new products for which a set of reference prices do not yet exist, but can also be problematic in established markets that are familiar. We discuss results from two sets of conjoint experiments, one in a new product category conducted in three countries in Europe, and another in an established category in the United States involving three experimental conditions that inform respondents about products and prices available in the marketplace. We find that the lack of knowledge of competitive offerings and prices affects estimates of brand values but not the value of other product features. In addition, we discuss aspects of how a well-designed conjoint study mitigates the effects of this type of learning in conjoint analysis. • No-choice option ensures valid inference by capturing outside preferences. • Outside good value rises over tasks in unfamiliar categories like MP3 players. • Outside good value is stable when the category and brands are well known. • Market info further stabilizes outside good value in familiar categories.

  PublisherDOI

• Teaching Prescriptive Analytics in Business School: an inter-coherent case study in the suv market

  Marketing Education Review · 2025-06-16 · 1 citations

  articleSenior author
  PublisherDOI

• A topic change point model for phrase-based topic inference

  International Journal of Research in Marketing · 2025-06-01

  articleOpen accessSenior authorCorresponding

  Obtaining customer insights from large and unstructured text corpora (e.g. customer reviews, blogs, tweets, written exchange in user forums or customer service emails) has attracted significant interest in marketing research. Topic models are among the most popular descriptive devices to analyze such data. We propose a new type of topic model built on observed word sequences. The proposed topic change point model assumes that text consists of sequences of words belonging to the same topic, possibly interspersed by ubiquitous terms such as stop words, and that topics change from run to run so that the end of each topic run marks a topic change point. Our model yields strings of words assigned to the same topic for phrase-based topic inference. In an extension of our model, we use parts-of-speech tags as prior information to topic change points which allows for observed syntactical structure of text to enter topic inference. We apply our model to two data sets and compare it to alternative modeling approaches, including state-of-the-art, BERT based topic models. We investigate the capability of models to predict consumer ratings which addresses their power in summarizing words so that the origin of ratings can be assessed by marketers. Implications and directions for future research are discussed.

  PublisherDOI

• Accounting for Formative and Reflective Topics in Product Review Data for Better Consumer Insights

  Journal of Marketing Research · 2025-01-13

  articleOpen accessSenior author

  Observations of product and service reviews suggest that textual product reviews may contain statements about the overall experience (“We had a great time”) or, similarly, about whether to recommend a particular product. The authors argue that such statements encapsulate an overall assessment and hence are not independently informative about, but rather reflect, overall ratings. The authors propose a model that allows for the distinction between topics that contribute to and topics that merely reflect an overall evaluation and apply the model to a dataset consisting of luxury hotel reviews. The findings show that, compared with a standard supervised latent Dirichlet allocation, the proposed model better fits the data and improves customer insights by resulting in more semantically coherent topics that point at specific aspects with positive and negative relationships to customers’ evaluation of their experience.

  PublisherDOI

• Supervised Mediation Analysis

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Dual Response in Conjoint Analysis

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• A Hierarchical Dirichlet Process Model for Customer Heterogeneity

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Dual response in conjoint analysis

  Marketing Letters · 2025-08-07 · 2 citations

  articleOpen accessSenior author

  Abstract Conjoint analysis often incorporates a no-choice option to allow respondents to opt out of a choice task. This paper examines the benefits of a dual-response format in which respondents first select their preferred alternative from among a set of available alternatives, excluding the no-choice option, and then decide whether they would actually buy it. We show that respondents employ a budget constraint when responding to the second response task but not to the first, indicating that respondents assess affordability separately from preference. We also show that our model with the dual-response format improves parameter estimation efficiency by 30% as measured by the Fisher information matrix relative to a standard model using a single-response format. We use our proposed budget constraint model to examine optimal pricing in a competitive market, where the standard model is shown to overestimate equilibrium prices.

  PublisherOA PDFDOI

• Integrating Conjoint and Maximum Difference Scaling Data

  Management Science · 2025-03-18 · 3 citations

  articleSenior author

  Customer preferences for product features play an important role in designing successful goods and services. Preferences for features are typically obtained by utilizing a model of choice where the utility for all but one level of an attribute is estimable. That is, the traditional discrete choice model can provide information on the change in utility between attribute-levels, but cannot separately estimate the utility associated with all levels of an attribute. In this paper, we propose a model that integrates conjoint and Maximum Difference scaling data to identify part-worth utilities for all product features, using the outside good as a common reference level, instead of the usual practice of having a reference level for each product attribute. The preference data are also integrated with satisfaction data to identify market opportunities for new and existing products. We illustrate our model with data from a survey measuring customer satisfaction and preferences for large-screen TVs. This paper was accepted by Raphael Thomadsen, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02560 .

  PublisherDOI

• Advancing the science of marketing

  Marketing Letters · 2024-09-13

  articleOpen access1st authorCorresponding

  Abstract This paper presents a comprehensive overview of the significant contributions of the 2024 AMA Converse Award recipient Dr. Greg M. Allenby, as presented at the Converse Symposium held at the Gies College of Business, University of Illinois, at Urbana-Champaign. Highlighting the dual emphasis on theory and practice, the paper underscores Dr. Allenby’s pivotal role in advancing Bayesian statistical methods within marketing, thereby shaping modern marketing research methodologies. His work encompasses a broad spectrum of research, including choice modeling, heterogeneous response analysis, decision theory, and the integration of text data into marketing models. The paper also reflects on Dr. Allenby’s academic journey, professional achievements, and his influential role in national marketing and statistical organizations. Additionally, the paper discusses the historical context and ongoing evolution of the Converse Award, emphasizing its importance in recognizing and fostering innovation and leadership in the field of marketing.

  PublisherOA PDFDOI

Frequent coauthors

• Peter E. Rossi

  59 shared

• Sanghak Lee

  36 shared

• Jaehwan Kim

  Korea University

  31 shared

• Thomas Otter

  Goethe University Frankfurt

  18 shared

• Moon Young Kang

  Soongsil University

  17 shared

• Byungho Park

  17 shared

• Jeff D. Brazell

  University of Utah

  15 shared

• James L. Ginter

  The Ohio State University

  14 shared

Education

• PhD, Graduate School of Business

  University of Chicago

  1988

• MBA, Graduate School of Business

  University of Chicago

  1984

• MSOR, Business

  Illinois Institute of Technology

  1981

• BSME, Engineering

  Ohio Northern University

  1978

Awards & honors

• 2024 AMA Paul Converse award for contributions to the scienc…
• 2023 AMA Gil Churchill lifetime achievement award for contri…
• 2012 AMA Parlin Award for leadership and impact on the profe…
• 2010 ISMS Long Term Impact Award