About

Pradeep K. Chintagunta is the Joseph T. and Bernice S. Lewis Distinguished Service Professor of Marketing at The University of Chicago Booth School of Business. His research primarily focuses on the analysis of household purchase behavior, pharmaceutical markets, and technology products. Early in his career, Chintagunta conducted extensive research using scanner panel data to understand consumer responses to various marketing activities such as pricing, promotions, and advertising. More recently, his research has expanded into the field of development marketing, which involves applying marketing tools to improve outcomes for small businesses, healthcare, agriculture, and other sectors in emerging markets. Additionally, he is interested in exploring the broader effects of marketing on the ecosystem beyond just consumers and firms, aiming to develop a more holistic understanding of marketing's role in society. Chintagunta serves on the advisory editorial boards of several prestigious journals including Quantitative Marketing and Economics, the Journal of Marketing, and the Journal of Marketing Research. His scholarly work has been published in leading academic journals such as the Journal of Marketing Research, Marketing Science, Management Science, Quantitative Marketing and Economics, the Journal of the American Statistical Association, and the Journal of Econometrics.

Research topics

• Business
• Marketing
• Computer Science
• Advertising
• Economics
• World Wide Web
• Psychology
• Environmental health
• Microeconomics
• Family medicine
• Economic growth
• Medicine
• Algorithm
• Knowledge management
• Pathology

Selected publications

• Bayesian Learning and Skill Accumulation in Video Game Play

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Going back to move forward? How search revisits on a website we built inform us about search outcomes

  Quantitative Marketing and Economics · 2026-03-31

  articleOpen accessSenior author

  Why do consumers search some products more than once (revisit) before making a purchase decision? How are these revisits related to search outcomes, such as consumers’ consideration sets and choices? In this paper, we build an online shopping website and run an incentive-aligned research study to find out. We show that most consumers revisit with the goal of comparing products, followed second by an expressed desire to obtain more information, and third by forgetting. Also, we document the ways in which search behavior differs across revisit motivations. Products revisited with the goal of obtaining additional information and comparing are more likely to be purchased than those revisited due to forgetting. Interestingly, revisits also reveal information about consumers’ consideration sets, which are typically unobserved: most consumers have eliminated a product from consideration if they don’t revisit it. This behavior implies consideration sets are dynamic, which we illustrate. Finally, managerial implications and possible extensions of the results are discussed.

  PublisherOA PDFDOI

• Bayesian learning and skill accumulation in video game play

  Quantitative Marketing and Economics · 2026-03-25

  articleOpen accessSenior author

  We study players’ engagement decisions with different game modes in a popular multi-generational franchise video game. Some game modes are competitive where players’ utility might derive from performance, whereas other game modes are casual and feature the aspect of social interactions with other players. Model-free data patterns suggest that three potential mechanisms could jointly determine players’ decisions to engage in competitive versus casual game mode: unobserved preference heterogeneity, learning about their preferences for different modes, and skill accumulation. We develop a novel model that explains players’ decisions along both the extensive and the intensive margins with respect to different modes, while incorporating all three mechanisms. These mechanisms can be separately identified in our data set due to the panel feature of the multi-generational game. Our estimation results show several key insights about each mechanism. First, playing the competitive mode accumulates skills much faster than the casual mode. In addition, we find that 27% of the players are inherently more competitive, and accumulate skills much faster by playing the competitive mode more often. Thus, these players spend more time playing and learn about their true preferences faster than others. Building on these insights, we propose several implementable game mode related actions for the managers to improve player engagement along the extensive and intensive margins. For example, introducing a new game mode with competitive features improves players’ extensive margins more effectively (by 15 $$\sim $$ 32%), while a new game mode with casual features improves players’ intensive margins more effectively (by 20 $$\sim $$ 28%).

  PublisherOA PDFDOI

• (Smart) Technology Doesn't Make Us Dumber: Marketing Analytics Improves the Mental, Managerial and Financial Performance of Entrepreneurs in Rwanda

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Characterization and Targeting of the Main Drivers of Elective Imaging Utilization: A Cross-Sectional Study Applying Conjoint Analysis

  Journal of the American College of Radiology · 2025-01-18

  article
  PublisherDOI

• Customers’ Review Content and Their Referral and (Re)Purchase Behaviors

  Journal of Marketing · 2025-06-16

  articleOpen accessSenior author

  This research examines how customer review content influences review writers’ subsequent decisions. Employing a mixed-methods approach, including a field experiment, a scenario experiment, and archival data analysis, the authors investigated the effects of affective content, cognitive content, and length of customers’ reviews on their subsequent referral and (re)purchase behaviors across various contexts (household services, podcast trials, and airline services). The authors leveraged the random assignment of experimental interventions and other shifters to induce exogenous variation in review content features. Additionally, they employed instrumental and proxy variables to address endogeneity issues. Findings from the three studies consistently demonstrate that affective content in reviews enhances referral and repurchase behaviors, whereas cognitive content exerts adverse effects. Moderation analyses show that the effects of review length on these behavioral outcomes depend on individual and contextual factors that affect customers’ elaboration likelihood during review writing. Overall, this research provides actionable insights for strategically shaping customer review content to drive critical business outcomes and enriches theoretical understanding of how content features of customer reviews affect review writers’ decisions.

  PublisherOA PDFDOI

• Fair Lending in Car Financing: Unintended Racial Consequences of Consumer Financial Protection Bureau Supervision of Dealer Markups

  Journal of Marketing Research · 2025-06-12 · 1 citations

  articleSenior author

  Over 80% of car buyers in the United States obtain a loan through a dealership. Dealers often mark up lender-provided interest rates (buy rates), but consumers cannot distinguish these markups from total rates. In 2014, the Consumer Financial Protection Bureau made an unprecedented public disclosure regarding its supervision of auto lenders, revealing racial disparities in dealer markups disadvantaging minority borrowers. The Bureau strongly recommended—but did not mandate—that lenders providing loans through dealers eliminate dealer discretion in markups by adopting flat dealer compensation per transaction. The authors examine the effectiveness of this intervention in reducing consumer financing, car payments, and the racial gap. Using detailed individual transaction-level data and a regression-discontinuity-in-time design, they find that dealer markups declined by 5.55 basis points after the intervention, saving $66.60 for a typical loan. However, the intervention also increased buy rates, resulting in no significant changes to consumers’ total interest rates and total payments. Dealers experienced a slight increase in financing profits but no significant change in vehicle profits. Importantly, the racial gap in dealer markups and total interest rates widened ; dealer markups decreased for both minorities and nonminorities, but they decreased more for nonminorities. The findings can illuminate the intended and unintended consequences of such government oversight and provide insights into their underlying mechanisms.

  PublisherDOI

• Too Many or Too Few? Information Cues in Recommender Systems and Consequences for Search and Purchase Behavior

  Journal of Marketing · 2025-03-13 · 3 citations

  articleOpen accessSenior author

  This article examines how the number of information cues in recommender systems influences consumer search and purchase. E-commerce platforms often display a list of recommended products on product pages, where consumers can browse and click on individual items for details. Given space constraints, determining the appropriate amount of information to display is crucial, as it affects consumers’ use of both recommender systems and nonrecommender search tools. Through a randomized controlled field experiment with an online retailer, the authors test four information designs: no cues (product name only), single cues (either price or review), and dual cues (price and review). They find an inverted U-shaped relationship between the number of information cues and sales, with single cues yielding the highest sales compared with both more information (dual cues) and less information (no cues). This nonlinear effect stems from the interplay between search intensity and efficiency. The no-cue condition increases search intensity but forces consumers to rely on a less efficient nonrecommender search process. In contrast, the highly efficient dual-cue condition provides sufficient information for evaluation but discourages further exploration beyond recommenders. Single cues strike a balance, offering just enough information to aid product evaluation while maintaining high search intensity across both recommender and nonrecommender tools.

  PublisherDOI

• How do U.S. households change their expenditure patterns in response to income or wealth shocks? Insights from NielsenIQ Data

  Quantitative Marketing and Economics · 2025-04-16

  article
  PublisherDOI

• A Study of Retailer Advertising

  Springer proceedings in business and economics · 2025-01-01

  book-chapterSenior author
  PublisherDOI

Frequent coauthors

• Naufel J. Vilcassim

  London School of Economics and Political Science

  69 shared

• Stephen J. Anderson

  Texas A&M University

  50 shared

• Mario Macis

  William Carey University

  48 shared

• Jessica Goldberg

  39 shared

• Jean‐Pierre Dubé

  28 shared

• Puneet Manchanda

  27 shared

• S. Sriram

  Ross School

  19 shared

• Sandipan Dhar

  Institute of Child Health

  17 shared

Education

• B.S.

  Banaras Hindu University

  1984

• M.D.

  Indian Institute of Management

  1986

• Ph.D., Marketing

  Northwestern University

  1990

Awards & honors

• O'Dell award finalist (1996)
• O'Dell award finalist (2001)
• Hillel J. Einhorn Award for Excellence in Teaching
• Chicago Booth's top professors by BusinessWeek