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Yulia Nevskaya

Assistant Professor
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Overview

Yulia Nevskaya is an Assistant Professor of Marketing in the Smith School of Business at Queen’s University. Yulia is an empirical researcher and uses various methods of quant marketing and economics to study how consumers respond to purchase and consumption stimulations.

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Academic Area

  • Marketing

Interest Topics

  • Analytics & AI
  • Brand
  • Marketing & Sales
  • Economics

Faculty Details

Profile

Full Bio

Dr. Yulia Nevskaya studies how consumers respond to purchase and consumption stimulations. Specifically, Yulia looks at how consumers respond to incentives and rewards, in the settings ranging from loyalty programs to massively-multiplayer online video games. Additionally, she investigates the role of social interactions in facilitating consumption, both within the digital realm and the physical world. Her special interest is habit-formation and addiction, particularly to video games. Methods-wise, Yulia is trained as an empirical modeler and uses various techniques of quantitative marketing and economics. Her research has been published in Marketing Science and Journal of Marketing Research.

Prior to joining Smith, Dr. Nevskaya was a faculty member at the Olin Business School at Washington University in St. Louis. She taught courses in digital marketing and text mining. She earned her Ph.D. in Business Administration with a specialisation in Marketing from the University of Rochester.

Academic Degrees

PhD in Business Administration (Marketing)
University of Rochester

M.A. in Economics
University of Colorado

BS in Management
Kostroma State Technological University

 

 

Publications

Publications

Nevskaya, Yulia and Paulo Albuquerque (2019), How Should Firms Manage Excessive Product Use? A
Continuous-Time Demand Model to Test Reward Schedules, Notifications, and Time Limits,
Journal of Marketing Research, Volume 56, Issue 3, pp. 379-400.

Gopalakrishnan, Arun, Zhenling Jiang, Yulia Nevskaya, and Raphael Thomadsen (2021), Can Non-
Tiered Loyalty Reward Programs be Profitable?  Marketing Science, Volume 40, Issue 3, pp. 395-591.

Albuquerque, Paulo, and Yulia Nevskaya (2022), The Impact of New Content and User Community
Membership on Usage of Online Games,  Customer Needs and Solutions 9, 1-24.

Working Papers

Consumer Information Asymmetry in Online Product Reviews

  • Abstract: Firms usually know the distribution of tastes and price sensitivities across the consumer
    population, or do their best to learn it. At the same time, individual consumers are
    unaware of this distribution. This constitutes an information asymmetry. This paper shows
    that when an individual makes a purchase decision using online consumer product reviews
    as her major source of information about the product quality, the lack of information about
    consumer heterogeneity does not allow her to correctly infer the true product quality from
    the reviews. The inferred product quality is systematically biased, and the bias depends on
    the consumer’s characteristics. In the market with such information asymmetry, a strategic
    forward-looking firm is able to maximize its profits by setting the price to attract consumers
    who would give the product a high rating. This paper shows that the firm still earns higher
    profits if consumers are informed about the consumer heterogeneity distribution, i.e. in the
    market with no information asymmetry. The findings are in line with the empirical evidence
    that firms selling their products/services over the Internet (such as hotel booking web-sites)
    try to reduce the information asymmetry, for example, by disclosing the average product
    ratings by consumer types to their prospective customers.

 

Work In Progress

Advertising and Product Returns with Peter Danaher and Bhoomija Ranjan

Leveraging the Predictive Power of Fashion Trendsetters with Guangying Chen

Teaching

Academic Appointments

Assistant Professor of Marketing, Smith School of Business, Queen’s University, July 2023-Present

Assistant Professor of Marketing, Olin Business School,Washington University in St. Louis, July 2013-June 2023

Teaching

Text Mining (graduate, developed by me) Spring 2018-present
Digital Marketing (MBA and undergraduate, developed by me) Spring 2016-2018
Principles of Marketing (undergraduate) Spring 2014, 2019
Seminar in Empirical Methods in Structural Modeling (doctoral level) Spring 2015, 2017, 2018, 2020, 2022

PhD Advising

Bicheng Yang (dissertation committee member, initial placement: University of British Columbia)
Yijun Chen (dissertation committee member, initial placement: Imperial College London)
Fan Zhang (dissertation committee member, initial placement: Amazon)

Research

Interests

Consumer response to rewards, social interactions and choice, gamification and habit formation,
computational machine learning, dynamic structural choice models

 

Presentations

Conference and Invited Talks

EMAC Annual Conference, Odense, Denmark, May 2023
ET Symposium for Canadian Marketing Strategy, Kingston, ON, May 2023 (discussant) Marketing
Dynamics Conference, Atlanta, GA, November 2022
Ohio State University, Fisher College of Business, November 2022 Bentley University, Waltham, MA,
October 2022
University of Central Florida, Orlando, Florida, October 2022
Queen’s University, Smith School of Business, Kingston, ON, September 2022
Quantitative Marketing and Economics Conference, Los Angeles, CA, October 2021 (discussant)
Management Science Workshop, Puerto Varas, Chile, January 2020 (accepted for presentation) Monash
University, Monash Business School, Melbourne, Australia, November 2019
Deakin University, Deakin Business School, Melbourne, Australia, November 2019 INFORMS Marketing
Science Conference, Rome, Italy, June 2019*
Marketing Dynamics Conference, Baltimore, MD, June 2019 Washington University in St. Louis, Spring
Seminar Series, May 2019 New Economic School, Moscow, Russia, September 2018
Summer Institute in Competitive Strategy (SICS), Berkeley, CA, June 2018* Marketing Dynamics
Conference, Hong Kong, August 2017
INFORMS Marketing Science Conference, Los Angeles, CA, June 2017 University of Missouri at Columbia
Research Camp, Columbia, MO, April 2017
13th ZEW Conference on the Economics of Information and Communication Technologies, Man- nheim,
Germany, June 2015 (paper presenter and discussant)
Analytics Roundtable, Washington University in St. Louis, May 2015
Marketing Dynamics Conference, Las Vegas, NV, August 2014 (accepted for presentation) Washington
University in St. Louis, Spring Seminar Series, May 2014
Washington University in St. Louis, November 2012 Yale University, School of Management, October
2012
Northwestern University, Kellogg School of Management, October 2012 McGill University, October 2012
University of British Columbia, October 2012 Rice University, October 2012
Southern Methodist University, October 2012 Emory University, October 2012
Erasmus University, Department of Business Economics, September 2012 Tilburg University, September
2012
Summer Institute in Competitive Strategy (SICS), Berkeley, CA, June 2012* INFORMS Marketing Science
Conference, Boston, MA, June 2012 University of Rochester Seminar Series, October 2010
INFORMS Marketing Science Conference, Cologne, Germany, June 2010
* – presentation by a co-author

 

Awards

Fellowships and Awards

  • Teradata Data Challenge Competition Winner (faculty advisor), Las Vegas, NV, October 2018
  • AMA-Sheth Doctoral Consortium Fellow, Dallas-Fort Worth, TX, 2010
  • Institute on Computational Economics Fellow, University of Chicago, 2008
  • Doctoral Fellowship, Simon School of Business Administration, University of Rochester
  • IREX Russia-U.S. Young Leadership Fellow: Sponsored by the U.S. Department of State, 2001-2002

 

Service

Academic Service

  • Ad hoc referee for Management Science, Marketing Science, Journal of Marketing Research, Alden G.
    Clayton Doctoral Dissertation Proposal Competition

Conference Organizer

  • Junior Scholars in Marketing Science - Faculty Development Forum,Washington University in St. Louis,
    2019 and 2023