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Isamar Troncoso

Isamar Troncoso

Assistant Professor of Business Administration

Assistant Professor of Business Administration

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Isamar Troncoso is an Assistant Professor of Business Administration in the Marketing Unit at HBS. She teaches the Marketing course in the MBA required curriculum.

Professor Troncoso studies problems related to digital marketplaces and new technologies. She leverages toolkits from econometrics, causal inference, and machine learning for her research. Her work has studied the unintended consequences of different platform designs on users' behavior, and the use of representation learning methods to model consumer choices in large assortments.

She earned a Ph.D. in Business Administration (Marketing) from the Marshall Business School at the University of Southern California. She also earned an Industrial Engineering degree from the University of Chile.

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Marketing
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Isamar Troncoso
Unit
Marketing
Contact Information
(617) 495-6267
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Publications Research Summary Awards & Honors

Journal Articles
Journal Articles

  • Proserpio, Davide, Isamar Troncoso, and Francesca Valsesia. "Does Gender Matter? The Effect of Management Responses on Reviewing Behavior." Special Issue on Marketing Science and Field Experiments. Marketing Science 39, no. 6 (November–December 2020). View Details

Working Papers
Working Papers

  • Chen, Fanglin, Xiao Liu, Davide Proserpio, and Isamar Troncoso. "Product2Vec: Leveraging Representation Learning to Model Consumer Product Choice in Large Assortments." NYU Stern School of Business Research Paper Series, July 2022. View Details
  • Troncoso, Isamar, and Lan Luo. "Look the Part? The Role of Profile Pictures in Online Labor Markets." Working Paper, November 2020. View Details
All Publications

Isamar Troncoso is an Assistant Professor of Business Administration in the Marketing Unit at HBS. She teaches the Marketing course in the MBA required curriculum.

Professor Troncoso studies problems related to digital marketplaces and new technologies. She leverages toolkits from econometrics, causal inference, and machine learning for her research. Her work has studied the unintended consequences of different platform designs on users' behavior, and the use of representation learning methods to model consumer choices in large assortments.

She earned a Ph.D. in Business Administration (Marketing) from the Marshall Business School at the University of Southern California. She also earned an Industrial Engineering degree from the University of Chile.

Journal Articles
  • Proserpio, Davide, Isamar Troncoso, and Francesca Valsesia. "Does Gender Matter? The Effect of Management Responses on Reviewing Behavior." Special Issue on Marketing Science and Field Experiments. Marketing Science 39, no. 6 (November–December 2020). View Details
Working Papers
  • Chen, Fanglin, Xiao Liu, Davide Proserpio, and Isamar Troncoso. "Product2Vec: Leveraging Representation Learning to Model Consumer Product Choice in Large Assortments." NYU Stern School of Business Research Paper Series, July 2022. View Details
  • Troncoso, Isamar, and Lan Luo. "Look the Part? The Role of Profile Pictures in Online Labor Markets." Working Paper, November 2020. View Details
Research Summary
Overview
Professor Troncoso's research explores problems related to digital marketplaces and AI applications in marketing, and combines toolkits from econometrics, causal inference, and machine learning. She has studied how different platform design choices can lead to unintended consequences on their users' behavior, including how management responses in online review platforms can lead to selective attrition of female reviewers, and how using profile pictures in online labor marketplaces affects the choice of service providers. Professor Troncoso's research has also explored how to leverage machine learning methods to study product competition in today's large product assortments.
Awards & Honors
Selected as an AMA-Sheth Foundation Doctoral Consortium Faculty Fellow by the American Marketing Association in 2021.
Finalist in the 2021 American Statistical Association Best Doctoral Dissertation Proposal Competition (Marketing Section).
Selected as an INFORMS Doctoral Consortium Fellow in 2019.
Areas of Interest
  • analytics
  • artificial intelligence
  • electronic commerce
  • machine learning
  • marketing
  • Additional Topics
  • big data
  • econometrics
  • Industries
  • e-commerce industry
  • high technology
  • retailing

Areas of Interest

analytics
artificial intelligence
electronic commerce
machine learning
marketing
 More

Additional Topics

big data
econometrics

Industries

e-commerce industry
high technology
retailing
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