Faculty & Research

Sanjog Misra

Charles H. Kellstadt Professor of Marketing

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5807 South Woodlawn Avenue
Chicago, IL 60637

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 structural econometric methods to study consumer and firm decisions. In particular, his research involves building data-driven models aimed at understanding how consumers make choices and investigating firm decisions pertaining to pricing, distribution and salesforce management issues. Professor Misra is also interested in the development of statistical and econometric approaches to deal with complex models calibrated on large-scale marketing data and use of such models in enhancing strategic decisions.

Professor Misra currently serves as Co-Editor of Quantitative Marketing and Economics. He has also served as an Associate Editor at Marketing, Quantitative Marketing and Economics, the International Journal of Research in Marketing as well as for special issues of Management Science and the Journal of Marketing Research. Professor Misra is actively involved in partnering with firms in his research and has worked on various projects with companies such as Eli Lilly, Adventis, Mercer Consulting, Sprint, MGM, Bausch & Lomb, Xerox Corporation and Lucent Technologies with the aim of helping them design efficient, analytics-based, management systems that result in better decisions. He currently chairs the research advisory committee at Convertro (now a part of AOL/Verizon) and acts as an advisor to AOL/Verizon of data strategy and science. At Booth Professor Misra will be teaching a new course on Digital and Algorithmic Marketing. This course brings his practical and research expertise in the algorithmic advertising and marketing domain into the classroom. He is hopeful that the class will get students ready for the next evolution of marketing that he believes is already underway.

Prior to joining Booth, Misra was Professor of Marketing at UCLA Anderson School of Management and Professor at the Simon School of Business at the University of Rochester. In addition he has been visiting faculty at the Johnson School of Management at Cornell University and the Graduate School of Business at Stanford University.


2016 - 2017 Course Schedule

Number Name Quarter
37304 Digital and Algorithmic Marketing 2017 (Spring)
37888 Marketing with Big Data 2017 (Summer)
37906 Applied Bayesian Econometrics 2017 (Spring)

2017 - 2018 Course Schedule

Number Name Quarter
37108 Startup Marketing 2017 (Fall)
37304 Digital and Algorithmic Marketing 2017 (Fall)
37906 Applied Bayesian Econometrics 2018 (Winter)

REVISION: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models
Date Posted: Jul  27, 2017
This article proposes a distributed Markov chain Monte Carlo (MCMC) algorithm for estimating Bayesian hierarchical models when the number of cross-sectional units is very large and the objects of interest are the unit-level parameters. The two-stage algorithm is asymptotically exact, retains the flexibility of a standard MCMC algorithm, and is easy to implement. The algorithm constructs an estimator of the posterior predictive distribution of the unit-level parameters in the first stage, and uses the estimator as the prior distribution in the second stage for the unit-level draws. Both stages are embarrassingly parallel. The algorithm is demonstrated with simulated data from a hierarchical logit model and is shown to be faster and more efficient (in effective sample size generated per unit of computing) than a single machine algorithm by at least an order of magnitude. For a relatively small number of observations per cross-sectional unit, the algorithm is both faster and has better ...

REVISION: Big Data and Marketing Analytics in Gaming: Combining Empirical Models and Field Experimentation
Date Posted: Jun  27, 2017
Efforts on developing, implementing and evaluating a marketing analytics framework at a real-world company are described. The framework uses individual-level transaction data to fit empirical models of consumer response to marketing efforts, and uses these estimates to optimize segmentation and targeting. The models feature themes emphasized in the academic marketing science literature, including incorporation of consumer heterogeneity and state-dependence into choice, and controls for the endogeneity of the firm's historical targeting rule in estimation. To control for the endogeneity, we present an approach that involves conducting estimation separately across fixed partitions of the score variable that targeting is based on, which may be useful in other behavioral targeting settings. The models are customized to facilitate casino operations and are implemented at the MGM Resorts International's group of companies. The framework is evaluated using a randomized trial implemented at ...

REVISION: Scalable Price Targeting
Date Posted: Jun  27, 2017
We study the welfare implications of scalable price targeting, an extreme form of third-degree price discrimination, for a large, digital firm. Targeted prices are computed by solving the firm’s Bayesian Decision-Theoretic pricing problem based on a database with a high-dimensional vector of customer features that are observed prior to the price quote. To identify the causal effect of price on demand, we first run a large, randomized price experiment. These data are used to train our demand model. We use lasso regularization to select the set of customer features that moderate the heterogeneous treatment effect of price on demand. We use a weighted likelihood Bayesian bootstrap to quantify the firm’s approximate statistical uncertainty in demand and profitability. Theoretically, both firm and customer surplus could rise with scalable price targeting. We test the welfare implications out of sample with a second randomized price experiment with new customers. Optimized uniform pricing ...

New: Measuring Substitution and Complementarity Among Offers in Menu Based Choice Experiments
Date Posted: Apr  24, 2017
Choice experiments designed to extend beyond the classic application of choice among perfect substitutes have become popular in marketing research. In these experiments, often referred to as menu based choice, respondents face choice sets that may comprise substitutes, complements, and offers that provide utility independently, or any mixture of these three types. The inferential challenge posed by data from such experiments is in the calibration of utility functions that accommodate a mix of substitutes, complements, and independent offers. Moreover, while a prior understanding of the product categories under study may, for example, suggest that two offers in a set are essentially perfect substitutes, this may not be true for all respondents. To address these challenges, we combine Besag's (Besag 1972, Besag 1974) autologistic choice model with a flexible hierarchical prior structure. We explain from first principles how the autologistic choice model improves on the multivariate ...

New: Homogenous Contracts for Heterogeneous Agents: Aligning Salesforce Composition and Compensation
Date Posted: Feb  23, 2014
Observed contracts in the real-world are often very simple, partly reflecting the constraints faced by contracting firms in making the contracts more complex. We focus on one such rigidity, the constraints faced by firms in fine-tuning contracts to the full distribution of heterogeneity of its employees. We explore the implication of these restrictions for the provision of incentives within the firm. Our application is to salesforce compensation, in which a firm maintains a salesforce to market its products. Consistent with ubiquitous real-world business practice, we assume the firm is restricted to fully or partially set uniform commissions across its agent pool. We show this implies an interaction between the composition of agent types in the contract and the compensation policy used to motivate them, leading to a “contractual externality” in the firm and generating gains to sorting. This paper explains how this contractual externality arises, discusses a practical approach to ...

New: Disentangling Preferences and Learning in Brand Choice Models
Date Posted: Oct  24, 2012
In recent years there has been a growing stream of literature in marketing and economics that models consumers as Bayesian learners. Such learning behavior is often embedded within a discrete choice framework that is then calibrated on scanner panel data. At the same time, it is now accepted wisdom that disentangling preference heterogeneity and state dependence is critical in any attempt to understand either construct. We posit that this confounding between state dependence and heterogeneity ...

REVISION: Repositioning Dynamics and Pricing Strategy
Date Posted: Oct  12, 2012
We measure the revenue and cost implications to supermarkets of changing their price positioning strategy in oligopolistic downstream retail markets. Our estimates have implications for long-run market structure in the supermarket industry, and for measuring the sources of price rigidity in the economy. We exploit a unique dataset containing the price-format decisions of all supermarkets in the U.S. The data contain the format-change decisions of supermarkets in response to a large shock to ...

REVISION: Enriching Interactions: Incorporating Outcome Data into Static Discrete Games
Date Posted: Oct  12, 2012
When modeling the behavior of firms, marketers and micro-economists routinely confront complex problems of strategic interaction. In competitive environments, firms make strategic decisions that not only depend on the features of the market, but also on their beliefs regarding the reactions of their rivals. Structurally modeling these interactions requires formulating and estimating a discrete game, a task which, until recently, was considered intractable. Fortunately, two-step estimation ...

REVISION: Estimating Discrete Games
Date Posted: Jan  31, 2012
This paper provides a critical review of the methods for estimating static discrete games and their relevance for quantitative marketing. We discuss the various modeling approaches, alternative assumptions, and relevant trade-offs involved in taking these empirical methods to data. We consider both games of complete and incomplete information, examine the primary methods for dealing with the coherency problems introduced by multiplicity of equilibria, and provide concrete examples from the ...

Update: Disentangling Preferences and Learning in Brand Choice Models
Date Posted: Oct  25, 2011
In recent years there has been a growing stream of literature in marketing and economics that models consumers as Bayesian learners. Such learning behavior is often embedded within a discrete choice framework which is then calibrated on scanner panel data. At the same time it is now accepted wisdom that disentangling preference heterogeneity and state dependence is critical in any attempt to understand either construct. We posit that this confounding often carries through to Bayesian learning mo
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New: How Consumers' Attitudes toward Direct-to-Consumer Advertising of Prescription Drugs Influence and
Date Posted: Aug  17, 2011
Data from 1081 adults surveyed by the FDA were analyzed to explore consumers’ attitudes toward direct-toconsumer advertising (DTCA) of prescription drugs, and the relation between these attitudes and health related consumption behaviors. We report the favorableness of consumers’ reactions to DTCA, and more importantly, demonstrate that consumers’ attitudes toward DTCA are related to whether they search for more information about a drug that is advertised, and ask their physician about the drug.

Update: A Structural Model of Sales-Force Compensation Dynamics: Estimation and Field Implementation
Date Posted: Oct  21, 2009
We present an empirical framework to analyze real-world sales-force compensation schemes. The model is flexible enough to handle quotas and bonuses, output-based commission schemes, as well as "ratcheting" of compensation based on past performance, all of which are ubiquitous in actual contracts. The model explicitly incorporates the dynamics induced by these aspects in agent behavior. We apply the model to a rich dataset that comprises the complete details of sales and compensation plans for a
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REVISION: Scheduling Sales Force Training: Theory and Evidence
Date Posted: Feb  04, 2009
To have a productive sales force, firms must provide their salespeople with sales training. But from a profit-maximizing perspective, there are also reasons to limit training: training is expensive, it has diminishing returns, and trained salespeople need to be compensated at a higher level since their value in the outside labor market has increased. Due to these reasons, the following inter-related questions are not straightforward to answer: (1) How much training should be provided and how ...

Contract Duration: Evidence from Franchise Contracts
Date Posted: Apr  28, 2003
This study provides evidence on the determinants of contract duration using a large sample of franchise contracts. We find that the term of the contract systematically increases with the franchisee's physical and human capital investments, measures of recontracting costs, and the franchisor's experience in franchising (which we argue is negatively related to uncertainty about optimal contract provisions). These results are consistent with the hypothesis that the optimal contract duration ...

Salesforce Design with Experience-based Learning
Date Posted: Sep  09, 2001
This paper proposes and analyzes an integrated model of salesforce learning, product portfolio pricing and salesforce design. We consider a firm with a pool of sales representatives that is split into separate salesforces, one for each product. The salesforce assigned to each product is faced with an independent stream of sales leads. The salesforce may also handle leads that overflow from other product salesforces. In addition, salespeople "learn by doing" over their tenure on the job. In ...