Faculty & Research

Sanjog Misra

Sanjog Misra

Charles H. Kellstadt Professor of Marketing

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. In particular, his research involves building data-driven models aimed at understanding how consumers make choices and investigating firm decisions pertaining to pricing, targeting and salesforce management issues. More broadly, Professor Misra is interested in the development of scalable algorithms, calibrated on large-scale data, and the implementation of such algorithms in real world decision environments.

Professor Misra currently serves as Co-Editor of Quantitative Marketing and Economics and as Associate Editor at Management Science and the Journal of Business and Economic Statistics. He has also served as an Associate Editor at Marketing Science, Quantitative Marketing and Economics, the International Journal of Research in Marketing 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 Oath, Verizon, Eli Lilly, Adventis, Mercer Consulting, Sprint, MGM, Bausch & Lomb, Xerox Corporation, Ziprecruiter and Lucent Technologies with the aim of helping them design efficient, analytics-based, management systems that result in better decisions. He currently serves as an advisor to several startups in the marketing technology, measurement and AI space. At Booth Professor Misra teaches courses on Algorithmic Marketing. These courses bring his practical and research expertise in the algorithmic marketing domain into the classroom. He is hopeful that these classes 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.


2020 - 2021 Course Schedule

Number Title Quarter
37304 Digital and Algorithmic Marketing 2021  (Winter)
37704 Algorithmic Marketing Lab 2021  (Spring)
37905 Marketing Literature Seminar 2021  (Spring)
37906 Applied Bayesian Econometrics 2021  (Winter)

REVISION: Valuing Brand Collaboration: Evidence From a Natural Experiment
Date Posted: Aug  17, 2020
We study complementarities between brands in the context of collaborations across museums. Over the course of our sample, one major museum with a highly recognized brand closed temporarily and sequentially collaborated with two established local museums. With individual panel data on museum memberships around these events, we measure how collaborations affect demand using an empirical framework of complementarities that are newly applied to the branding context. We observe two counter-acting demand patterns. First, customers with no history of buying membership from either museum enter the market, suggesting brand complementarities. Second, a sub-group of customers who previously purchased from either or both of the museums display decreased demand, consistent with brand dilution. Any structural approach that models the demand for collaboration with existing preferences for separate brands fails to create accurate demand predictions. The magnitude of the offsetting forces varies ...

REVISION: Personalized Pricing and Customer Welfare
Date Posted: Feb  21, 2020
Abstract We study the welfare implications of personalized pricing, an extreme form of third-degree price discrimination implemented with machine learning for a large, digital firm. We conduct a randomized controlled pricing field experiment to train a demand model and to conduct inferences about the effects of personalized pricing on firm and customer surplus. In a second experiment, we validate our predictions out of sample. Personalized pricing improves the firm's expected posterior profits by 19%, relative to optimized uniform pricing, and by 86%, relative to the firm's status quo pricing. On the demand side, customer surplus declines slightly under personalized pricing relative to a uniform pricing structure. However, over 60% of customers benefit from personalized prices that are lower than the optimal uniform price. Based on simulations with our demand estimates, we find several cases where customer surplus increases when the firm is allowed to condition on more customer ...

REVISION: Can Open Innovation Survive? Imitation and Return on Originality in Crowdsourcing Creative Work
Date Posted: Jan  14, 2020
Open innovation platforms that enable organizations to crowdsource ideation to parties external to the firm are proliferating. In many cases, the platforms use open contests that allow the free exchange of ideas with the goal of improving the ideation process. In open contests, participants (“solvers”) observe the ideas of others as well as the feedback received from the contest sponsor (“seeker”). The open nature of such contests generate incentives for imitating successful early designs by future solvers at the cost of the original solvers. As such, this creates the possibility of the platform unraveling when original solvers strategically withdraw from the platform, expecting their ideas will be copied without recompense. To investigate agent behavior in such a setting, we analyze publicly accessible micro-data on more than 6,000 design contests, submissions and participants from crowdsourced open ideation platforms and augment this analysis with field and online experiments. ...

New: The Identity Fragmentation Bias
Date Posted: Jan  10, 2020
Consumers interact with firms across multiple devices, browsers, and machines; these interactions are often recorded with different identifiers for the same individual. The failure to correctly match different identities leads to a fragmented view of exposures and behaviors. This paper studies the identity fragmentation bias, referring to the estimation bias resulted from using fragmented data. Using a formal framework, we decompose the contributing factors of the estimation bias caused by data fragmentation and discuss the direction of bias. Contrary to conventional wisdom, this bias cannot be signed or bounded under standard assumptions. Instead, upward biases and sign reversals can occur even in experimental settings. We then propose and compare several corrective measures, and demonstrate their performances using an empirical application.

New: Selling and Sales Management
Date Posted: Jun  19, 2019
About 10% of the US labor force is employed in selling related occupations and the expenditures on selling activities total close to 5% of the US GDP. Without question, selling occupies a prominent role in our economy. This chapter offers a discussion on the construct of selling, its role in economic models and the various aspects of firm decisions that relate to it.

REVISION: Estimation of Sequential Search Model
Date Posted: May  09, 2019
We propose a new likelihood-based estimation method for the sequential search model. By allowing search costs to be heterogeneous across consumers and products, we can directly compute the joint probability of the search sequence and the purchase decision when consumers are searching for the idiosyncratic preference shocks in their utility functions. Under this procedure, one recursively makes random draws for each dimension that requires numerical integration to simulate the probabilities associated with the purchase decision and the search sequence under the sequential search algorithm. We then present details from an extensive simulation study that compares the proposed approach with existing estimation methods recently used for sequential search model estimation, viz., the kernel-smoothed frequency simulator (KSFS) and the crude frequency simulator (CFS). In the empirical application, we apply the proposed method to the Expedia dataset from Kaggle which has previously been ...

REVISION: Exact MCMC for Choices from Menus -- Measuring Substitution and Complementarity among Menu Items
Date Posted: May  08, 2019
Choice environments in practice are often more complicated than the well studied case of choice between perfect substitutes. Consumers choosing from menus or configuring products face choice sets that consist of substitutes, complements and independent items, and the utility maximizing choice corresponds to a particular item combination out of a potentially huge number of possible combinations. This reality is mirrored in menu-based choice experiments. The inferential challenge posed by data from such choices is in the calibration of utility functions that accommodate a mix of substitutes, complements, and independent items. We develop a model that not only accounts for heterogeneity in preferences but also in what consumers perceive to be substitutes and complements and show how to perform Bayesian inference for this model based on the exact likelihood, despite its practically intractable normalizing constant. We characterize the model from first principles and show how it ...

New: Targeted Undersmoothing
Date Posted: May  09, 2018
This paper proposes a post-model selection inference procedure, called targeted undersmoothing, designed to construct uniformly valid confidence sets for functionals of sparse high-dimensional models, including dense functionals that may depend on many or all elements of the high-dimensional parameter vector. The confidence sets are based on an initially selected model and two additional models which enlarge the initial model. By varying the enlargements of the initial model, one can also conduct sensitivity analysis of the strength of empirical conclusions to model selection mistakes in the initial model. We apply the procedure in two empirical examples: estimating heterogeneous treatment effects in a job training program and estimating profitability from an estimated mailing strategy in a marketing campaign. We also illustrate the procedure’s performance through simulation experiments.

New: Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation
Date Posted: Feb  06, 2018
We discuss how to construct optimal targeting policies and document the difference in profits from alternative targeting policies by using estimation approaches that are based on recent advances in causal inference and machine learning. We introduce an approach to evaluate the profit of any targeting policy using only one single randomized sample. This approach is qualitatively equivalent to conducting a field test, but reduces the cost of multiple field tests because all comparisons can be conducted in only one sample. The approach allows us to compare many alternative optimal targeting policies that are constructed based on different estimates of the conditional average treatment effect, i.e. the incremental effect of targeting. We draw a conceptual distinction between methods that predict the conditional average treatment effect indirectly via the conditional expectation function trained on the outcome level, and methods that directly predict the conditional average treatment ...

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 ...

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 ...