About

Michael Trusov is the Dean’s Professor of Digital Marketing and Analytics at the Robert H. Smith School of Business at the University of Maryland. He received his Ph.D. from the Anderson School of Management at UCLA and holds a Master's degree in Computer Science as well as a Master's degree in Business Administration. His research interests include Digital Marketing, encompassing social media marketing, search engine marketing, social networks, clickstream analysis, electronic word-of-mouth marketing, e-commerce, and recommendation systems. He also specializes in Text Analysis, Eye-tracking, and Data Mining. Professor Trusov has extensive industry experience in software development and IT consulting, with a focus on marketing automation, database management, Internet applications, and e-commerce. He teaches Digital Marketing and Digital Analytics courses across various programs at the University of Maryland.

Research topics

• Computer Science
• Advertising
• Business
• Marketing
• Artificial Intelligence
• Industrial organization
• Operating system
• Econometrics
• Psychology
• Computer graphics (images)
• Economics
• World Wide Web
• Computer vision

Selected publications

• The Impact of Air Pollution on Consumer Spending

  Journal of Marketing · 2024-09-01 · 8 citations

  articleOpen accessSenior author

  Air pollution is a growing threat to economies and societies. Despite the common knowledge that air pollution impairs emotions and cognition and, hence, behavioral outcomes, the impact of air pollution on consumer spending remains an open question. Analyzing air quality readings and individual-level credit card transactions in South Korea, this article shows that consumers spend more money when air quality is poorer. This correlation is more prominent in hedonic categories, such as entertainment or leisure activities, where the nature of consumption is characterized by greater emotional benefits. The authors consider potential explanations, and the leading hypothesis is that consumers treat spending as a mood-regulating resource. The results survive an array of robustness checks and are supported in a controlled experiment, reinforcing a causal inference behind the main findings. The authors provide implications for stakeholders to develop a sustainable marketing program that not only pursues managerial interests but also concerns consumer well-being in the face of environmental change.

  PublisherDOI

• Efficient Estimation of Network Games of Incomplete Information: Application to Large Online Social Networks

  Management Science · 2021-06-30 · 9 citations

  articleOpen accessSenior author

  This paper presents a structural discrete choice model with social influence for large-scale social networks. The model is based on an incomplete information game and permits individual-specific parameters of consumers. It is challenging to apply this type of models to real-life scenarios for two reasons: (1) The computation of the Bayesian–Nash equilibrium is highly demanding; and (2) the identification of social influence requires the use of excluded variables that are oftentimes unavailable. To address these challenges, we derive the unique equilibrium conditions of the game, which allow us to employ a stochastic Bayesian estimation procedure that is scalable to large social networks. To facilitate the identification, we utilize community-detection algorithms to divide the network into different groups that, in turn, can be used to construct excluded variables. We validate the proposed structural model with the login decisions of more than 25,000 users of an online social game. Importantly, this data set also contains promotions that were exogenously determined and targeted to only a subgroup of consumers. This information allows us to perform exogeneity tests to validate our identification strategy using community-detection algorithms. Finally, we demonstrate the managerial usefulness of the proposed methodology for improving the strategies of targeting influential consumers in large social networks. This paper was accepted by Matthew Shum, marketing.

  PublisherOA PDFDOI

• Modeling Dynamics in Crowdfunding

  Marketing Science · 2020 · 51 citations

  • Computer Science
  • Marketing
  • Computer Science

  This paper investigates the underlying mechanisms of crowdfunding behavior (forward-looking delaying investment behavior and social interactions), which lead to the crowdfunding dynamics.

  PublisherDOI

• Deal or No Deal? Online Deals, Retailer Heterogeneity, and Brand Evaluations in a Competitive Environment

  Information Systems Research · 2020 · 11 citations

  Senior authorCorresponding
  • Business
  • Marketing
  • Advertising

  Daily deal platforms, such as Groupon, peaked in the mid-2000s, by letting retailers offer 50% promotions to consumers using an app. When used right, retailers were able to get consumers to try them for the first time and build a customer base. When used wrong, retailers lost revenue unnecessarily and sometimes went out of business. Even now, in 2020, you can find lovers and haters of daily deals, and yet they remain an integral part of the marketing mix for many retailers. One lingering question about these deals remained: How do customers perceive a retailer that offers daily deals before going to the retailer? Do retailers look desperate or confident? Through a series of laboratory experiments, we test whether offering a deal changes consumers’ preconsumption brand evaluations. Our research shows that brand evaluations are contingent on the retailer type (i.e., price segment and age), the success of the current deals offered (i.e., number of page visits and purchases), and the number of competitors that are also using deals. Together, our work demonstrates specific conditions where offering deals may lead to positive or negative consumer perceptions even before arriving at the retailer.

  PublisherDOI

• Efficient Estimation of Network Games of Incomplete Information: Application to Large Online Social Networks. {forthcoming}

  Data Archiving and Networked Services (DANS) · 2020-01-01 · 1 citations

  articleOpen accessSenior author

  This paper presents a structural discrete choice model with social influence for large-scale social
\nnetworks. The model is based on an incomplete information game and permits individual-specific
\nparameters of consumers. It is challenging to apply this type of models to real-life scenarios for two
\nreasons: 1) the computation of the Bayesian-Nash equilibrium is highly demanding, and 2) the
\nidentification of social influence requires the use of excluded variables that are oftentimes unavailable. To
\naddress these challenges, we derive the unique equilibrium conditions of the game, which allow us to
\nemploy a stochastic Bayesian estimation procedure that is scalable to large social networks. To facilitate
\nthe identification, we utilize community detection algorithms to divide the network into different groups
\nthat, in turn, can be used to construct excluded variables. We validate the proposed structural model with
\nthe login decisions of more than 25,000 users of an online social game. Importantly, this dataset also
\ncontains promotions that were exogenously determined and targeted to only a subgroup of consumers.
\nThis information allows us to perform exogeneity tests to validate our identification strategy using
\ncommunity detection algorithms. Finally, we demonstrate the managerial usefulness of the proposed
\nmethodology for improving the strategies of targeting influential consumers in large social networks.

  Publisher

• The Path to Click: Are You on It?

  Marketing Science · 2020 · 25 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  An eye-tracking study that examines consumers’ dynamic visual inspection on search engines.

  PublisherDOI

• Customer satisfaction underappreciation: The relation of customer satisfaction to CEO compensation

  International Journal of Research in Marketing · 2019-08-15 · 17 citations

  articleSenior author
  PublisherDOI

• Swayed by the Numbers: The Consequences of Displaying Product Review Attributes

  Journal of Marketing · 2018-07-23 · 3 citations

  articleSenior author
  PublisherDOI

• Swayed by the Numbers: The Consequences of Displaying Product Review Attributes

  Journal of Marketing · 2018-10-04 · 70 citations

  articleSenior author

  Prior research has shown the independent effects of average product ratings and number of reviews for online purchases, but the relative influence of these aggregate review attributes is still debated in the literature. In this research, the authors demonstrate the conditional influences of these two attributes as a function of the valence of average product ratings and the level of review numbers in a choice set. Specifically, they argue that the diagnosticity of the number of reviews, relative to average product ratings, increases when average product ratings are negative or neutral (vs. positive) and when the level of review numbers in a choice set is low (vs. high). As a result, when consumers choose among the best options on one of the review attributes (average product ratings or the number of reviews), their preference shifts from the higher-rated option with fewer reviews toward the lower-rated option with more reviews. The authors demonstrate this preference shift in seven studies, elucidate the underlying process by which this occurs, and conclude with a discussion of the implications for retailers and brands.

  PublisherDOI

• User profiling in display advertising

  Edward Elgar Publishing eBooks · 2018-03-30

  book-chapter1st authorCorresponding

  Constructing behavioral profiles from consumer online browsing activities is challenging: first, individual consumer-level records are massive and call for scalable high performance processing algorithms; second, advertising networks only observe consumer’s browsing activities on the sites participating in the network, potentially missing site categories not covered by the network. The latter issue can lead to a biased view of the consumer’s profile and to suboptimal advertising targeting. We present a method that augments individual-level ad network data with anonymized third-party data to improve consumer profile recovery and correct for potential biases. The approach is scalable and easily parallelized, improving almost linearly in the number of CPUs. Using economic simulation, we illustrate the potential gains the proposed model may offer to a firm when used in individual-level targeting of display ads.

  PublisherDOI

Frequent coauthors

• Randolph E. Bucklin

  27 shared

• Koen Pauwels

  Northeastern University

  22 shared

• Anand V. Bodapati

  5 shared

• Ralf van der Lans

  5 shared

• Oliver J. Rutz

  University of Washington

  4 shared

• Jared Watson

  New York University

  4 shared

• Wendy W. Moe

  3 shared

• Liye Ma

  Harbin Engineering University

  3 shared

Awards & honors

• Finalist, Jan-Benedict E.M. Steenkamp Award for Long-Term Im…
• Finalist, EMAC-IJRM Jan-Benedict Steenkamp Award for Long-Te…
• Finalist, Sheth Foundation/Journal of Marketing Award, 2022
• MSI Scholar, 2020
• Finalist, INFORMS Society for Marketing Science Long Term Im…