Sanjog Misra

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…

Research topics

• Computer Science
• Business
• Marketing
• Data Mining
• Artificial Intelligence
• Mathematics
• Statistics
• Machine Learning
• Economics
• Econometrics

Selected publications

• Personalized Pricing and Consumer Welfare

  Journal of Political Economy · 2022 · 153 citations

  Senior authorCorresponding

  We study the welfare implications of personalized pricing implemented with machine learning. We use data from a randomized controlled pricing field experiment to construct personalized prices and validate these in the field. We find that unexercised market power increases profit by 55%. Personalization improves expected profits by an additional 19% and by 86% relative to the nonoptimized price. While total consumer surplus declines under personalized pricing, over 60% of consumers benefit from p…

  PublisherOA PDFDOI

• Frontiers: The Identity Fragmentation Bias

  Marketing Science · 2022 · 32 citations

  Senior authorCorresponding

  The Identity Fragmentation Bias

  PublisherDOI

• Heterogeneous treatment effects and optimal targeting policy evaluation

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

  article
  PublisherDOI

• Deep learning for individual heterogeneity: an automatic inference framework

  2021-07-27 · 18 citations

  preprintOpen access

  We develop methodology for estimation and inference using machine learning to enrich economic models.Our framework takes a standard economic model and recasts the parameters as fully flexible nonparametric functions, to capture the rich heterogeneity based on potentially high dimensional or complex observable characteristics.These "parameter functions" retain the interpretability, economic meaning, and discipline of classical parameters.In contrast to common implementations of machine learning i…

  PublisherOA PDFDOI

• Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models

  Journal of Marketing Research · 2020 · 12 citations

  Many problems in marketing and economics require firms to make targeted consumer-specific decisions, but current estimation methods are not designed to scale to the size of modern data sets. In this article, the authors propose a new algorithm to close that gap. They develop a distributed Markov chain Monte Carlo (MCMC) algorithm for estimating Bayesian hierarchical models when the number of consumers is very large and the objects of interest are the consumer-level parameters. The two-stage and…

  PublisherOA PDFDOI

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

Awards & honors

• Distinguished Service Professor of Marketing and Applied AI

Similar researchers at University of Chicago

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• Alex Imash-27
• Baris Atah-22