About

Sanjog Misra is the Charles H. Kellstadt Professor of Marketing at the University of Chicago Booth School of Business. His research focuses on the use of machine learning, deep learning, and structural econometric methods to study consumer and firm decisions. His work involves building data-driven models aimed at understanding how consumers make choices and investigating firm decisions related to pricing, targeting, and salesforce management issues. Professor Misra is interested in the development of scalable algorithms, calibrated on large-scale data, and their implementation in real-world decision environments. His research has been published in prominent journals such as Econometrica, The Journal of Marketing Research, The Journal of Political Economy, Marketing Science, Quantitative Marketing and Economics, the Journal of Law and Economics, among others. He has served as co-editor of Quantitative Marketing and Economics and as an area editor for several leading journals including Management Science, the Journal of Business and Economic Statistics, Marketing Science, Quantitative Marketing and Economics, the International Journal of Research in Marketing, and the Journal of Marketing Research. In addition to his academic pursuits, Professor Misra actively partners with firms, advising companies such as Transunion, Oath, Verizon, Eli Lilly, Adventis, Mercer Consulting, Sprint, MGM, Bausch & Lomb, Xerox Corporation, and Ziprecruiter to help design efficient, analytics-based management systems. He currently serves as an advisor to startups in the marketing technology, measurement, and AI space. Prior to his current position, Misra was a Professor of Marketing at UCLA Anderson School of Management and at the Simon School of Business at the University of Rochester. He has also been a visiting faculty member at the Johnson School of Management at Cornell University and the Graduate School of Business at Stanford University. He teaches courses on Algorithmic Marketing at Booth, integrating his practical and research expertise to prepare students for the evolving landscape of marketing driven by AI and algorithms.

Research topics

• Computer Science
• Business
• Marketing
• Data Mining
• Artificial Intelligence
• Mathematics
• Statistics
• Machine Learning
• Economics
• Econometrics
• Microeconomics
• Industrial organization
• Algorithm
• Art
• Database
• Mathematical optimization

Selected publications

• Reply: “Revisiting Scalable Target Marketing…”

  Journal of Marketing Research · 2025-05-05

  article

  Editor's Note After a critique of Bumbaca, Misra and Rossi (2020) was conditionally accepted by the journal, the editor invited the authors to provide a brief response. An Associate Editor and two reviewers evaluated the response. By publishing both the critique and response, we hope to underscore JMR 's commitment to ensuring the accuracy of the work we publish and maintaining transparency when corrections are necessary.

  PublisherDOI

• Foundation Priors

  ArXiv.org · 2025-11-30

  preprintOpen access1st authorCorresponding

  Foundation models, and in particular large language models, can generate highly informative responses, prompting growing interest in using these ''synthetic'' outputs as data in empirical research and decision-making. This paper introduces the idea of a foundation prior, which shows that model-generated outputs are not as real observations, but draws from the foundation prior induced prior predictive distribution. As such synthetic data reflects both the model's learned patterns and the user's subjective priors, expectations, and biases. We model the subjectivity of the generative process by making explicit the dependence of synthetic outputs on the user's anticipated data distribution, the prompt-engineering process, and the trust placed in the foundation model. We derive the foundation prior as an exponential-tilted, generalized Bayesian update of the user's primitive prior, where a trust parameter governs the weight assigned to synthetic data. We then show how synthetic data and the associated foundation prior can be incorporated into standard statistical and econometric workflows, and discuss their use in applications such as refining complex models, informing latent constructs, guiding experimental design, and augmenting random-coefficient and partially linear specifications. By treating generative outputs as structured, explicitly subjective priors rather than as empirical observations, the framework offers a principled way to harness foundation models in empirical work while avoiding the conflation of synthetic ''facts'' with real data.

  PublisherOA PDFDOI

• Agentic Interactions

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Foundation Priors *

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Simulated maximum likelihood estimation of the sequential search model

  Quantitative Marketing and Economics · 2024-05-29 · 2 citations

  articleOpen accessSenior author

  Abstract We propose a new approach to simulate the likelihood of the sequential search model. By allowing search costs to be heterogeneous across consumers and products, we directly compute the joint probability of the search and purchase decisions when consumers are searching for the idiosyncratic preference shocks in their utility functions. Under the assumptions of Weitzman’s sequential search algorithm, the proposed procedure recursively makes random draws for each quantity that requires numerical integration while enforcing the conditions stipulated by the algorithm. In an extensive simulation study, we compare the proposed method with existing likelihood simulators that have recently been used to estimate the sequential search model. The proposed method attributes the uncertainty in the search order to the consumer-product-level distribution of search costs and the uncertainty in the purchase decision to the distribution of match values across consumers and products. This results in more precise estimation and an improvement in prediction accuracy. We also show that the proposed method allows for different assumptions on the search cost distribution and that it recovers consumers’ relative preferences even if the utility function and/or the search cost distribution is mis-specified. We then apply our approach to online search data from Expedia for field-data validation. From a substantive perspective, we find that search costs and “position” effects affect products in the lower part of the product listing page more than they do those in the upper part of the page.

  PublisherOA PDFDOI

• Coarse Personalization

  2024-07-08 · 3 citations

  articleSenior author

  Advances in estimating heterogeneous treatment effects enable firms to personalize marketing mix elements and target individuals at an unmatched level of granularity, but feasibility constraints limit such personalization. In practice, firms choose which unique treatments to offer and which individuals to offer these treatments with the goal of maximizing profits: we call this the coarse personalization problem. We propose a two-step solution that makes segmentation and targeting decisions in concert. First, the firm personalizes by estimating conditional average treatment effects. Second, the firm discretizes by utilizing treatment effects to choose which unique treatments to offer and who to assign to these treatments. We show that a combination of available machine learning tools for estimating heterogeneous treatment effects and a novel application of optimal transport methods provides a viable and efficient solution. With data from a large-scale field experiment for promotions management, we find that our methodology outperforms extant approaches that segment on consumer characteristics or preferences and those that only search over a prespecified grid. Using our procedure, the firm recoups over 99.5% of its expected incremental profits under fully granular personalization while offering only five unique treatments. We conclude by discussing how coarse personalization arises in other domains.

  PublisherDOI

• Bayesian Statistics and Marketing

  2024 · 9 citations

  Senior authorCorresponding
  • Computer Science
  • Statistics
  • Econometrics

  Fine-tune your marketing research with this cutting-edge statistical toolkit Bayesian Statistics and Marketing illustrates the potential for applying a Bayesian approach to some of the most challenging and important problems in marketing. Analyzing household and consumer data, predicting product performance, and custom-targeting campaigns are only a few of the areas in which Bayesian approaches promise revolutionary results. This book provides a comprehensive, accessible overview of this subject essential for any statistically informed marketing researcher or practitioner. Economists and other social scientists will find a comprehensive treatment of many Bayesian methods that are central to the problems in social science more generally. This includes a practical approach to computationally challenging problems in random coefficient models, non-parametrics, and the problems of endogeneity. Readers of the second edition of Bayesian Statistics and Marketing will also find: Discussion of Bayesian methods in text analysis and Machine Learning Updates throughout reflecting the latest research and applications Discussion of modern statistical software, including an introduction to the R package bayesm, which implements all models incorporated here Extensive case studies throughout to link theory and practice Bayesian Statistics and Marketing is ideal for advanced students and researchers in marketing, business, and economics departments, as well as for any statistically savvy marketing practitioner.

  PublisherDOI

• Heterogeneous treatment effects and optimal targeting policy evaluation

  Quantitative Marketing and Economics · 2024-04-05 · 30 citations

  article
  PublisherDOI

• Value Aligned Large Language Models

  SSRN Electronic Journal · 2024-01-01 · 7 citations

  articleOpen accessSenior author
  PublisherDOI

• Simulated Maximum Likelihood Estimation of the Sequential Search Model

  SSRN Electronic Journal · 2023-01-01 · 4 citations

  articleOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Tengyuan Liang

  22 shared

• Harikesh S. Nair

  Google (United States)

  21 shared

• Max Farrell

  University of California, Berkeley

  16 shared

• Paul B. Ellickson

  Duke University

  16 shared

• Tesary Lin

  Boston University

  6 shared

• Max H. Farrell

  6 shared

• Jean‐Pierre Dubé

  5 shared

• William J. Hornbuckle

  MGM Resorts International (United States)

  4 shared

Education

• Ph.D.

  University of Chicago Booth School of Business

• M.S.

  University of Chicago Booth School of Business

• B.S.

  University of Chicago Booth School of Business

• Ph.D.

  University of Rochester

• M.S.

  University of Rochester

• B.A.

  University of Rochester

• Ph.D.

  Cornell University

• M.S.

  Cornell University

• B.A.

  Cornell University

• Ph.D.

  Stanford University

• M.S.

  Stanford University

• B.A.

  Stanford University

Awards & honors

• Distinguished Service Professor of Marketing and Applied AI