About

Catherine Tucker is the Sloan Distinguished Professor of Management and a Professor of Marketing at MIT Sloan. She is the faculty director of the EMBA program and has served as the Chair of the MIT Sloan PhD Program. Her research interests focus on how technology enables firms to utilize digital data and machine learning to enhance performance, as well as the regulatory challenges this poses. Tucker has particular expertise in online advertising, digital health, social media, and electronic privacy, studying the interface between marketing, the economics of technology, and law. She has received numerous awards for her research, including an NSF CAREER Award, the Erin Anderson Award for an Emerging Female Marketing Scholar and Mentor, the Garfield Economic Impact Award, the Paul E. Green Award, the William F. O'Dell Award, and the INFORMS Society for Marketing Science Long Term Impact Award. Tucker is a cofounder of the MIT Cryptoeconomics Lab, which studies blockchain applications, and has been a Visiting Fellow at All Souls College, Oxford. She has testified before Congress on digital privacy and algorithms, and presented her research to organizations such as the OECD, World Bank, IMF, and the ECJ. In addition to her research, Tucker is a senior editor at Marketing Science and has held editorial roles at other prominent journals. She co-directs the program on Digital Economics and Artificial Intelligence at the National Bureau of Economic Research. She teaches courses on Pricing and Marketing Management for the Senior Executive at MIT Sloan. She holds a PhD in economics from Stanford University and a BA from the University of Oxford.

Research topics

• Business
• Advertising
• Computer science
• Internet privacy
• Marketing

Selected publications

• Combining Ad Targeting Techniques: Evidence from a Field Experiment in the Auto Industry

  Management Science · 2025-02-05 · 3 citations

  articleSenior author

  Retargeted advertising that tries to entice potential customers back to a website is widely used by advertisers and has often replaced more traditional forms of targeting, such as contextual targeting that tries to match ads to website content. However, existing research has not investigated the extent to which these different targeting techniques compete with or complement each other. To investigate this, we conduct a large-scale field experiment with an automobile manufacturer to investigate how retargeting meshes with more traditional techniques of contextual targeting online and in turn how that should affect ad content. We investigate this using three different measures of online advertising effectiveness: website visits, engagement, and soft conversions. We find that combining contextual targeting and retargeting is more effective for all three measures. However, to unleash this effectiveness, marketers have to pay attention to the ad content in their retargeted ads. We find that when combining retargeted advertising with contextual targeting, ads that prompt users to customize an offering are the most effective. Last, we provide empirical evidence for understanding the underlying mechanism associated with our findings and replicate those findings with a laboratory experiment. This paper was accepted by David Simchi-Levi, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02310 .

  PublisherDOI

• Privacy, Data and Competition: The Case of Apps For Young Children

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• Frontiers: The Intended and Unintended Consequences of Privacy Regulation for Consumer Marketing

  Marketing Science · 2025-08-05 · 3 citations

  articleSenior author

  We discuss the quantitative marketing and economics literatures analyzing the benefits and unintended costs of digital consumer privacy regulation.

  PublisherDOI

• Building an Ecosystem or Prioritizing Personalization with AI? Evidence from a Field Experiment 

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Decentralization, Blockchain, Artificial Intelligence (<scp>AI</scp>): Challenges and Opportunities

  Journal of Product Innovation Management · 2025-07-22 · 3 citations

  articleOpen accessSenior author

  ABSTRACT New technologies like blockchain allow firms to decentralize core functions, forcing managers to reconsider the trade‐off between closed, proprietary control and open strategies that involve external contributors. While proponents often advocate for full decentralization, we argue this view overlooks important economic trade‐offs. We propose that the better strategy is selective decentralization : a disciplined approach to choosing where to centralize for efficiency and where to decentralize for innovation. We propose a three‐level framework— Infrastructure , Decision‐Making , and Operational Control —to guide this choice, helping managers analyze the specific costs and benefits at each layer. We apply this framework to the strategic adoption of Artificial Intelligence (AI), where the technology's powerful pull toward centralization provides a stark test case. Our analysis shows that an “open source AI” strategy—decentralizing operations to foster innovation while keeping infrastructure centralized for efficiency—is more pragmatic than full decentralization. Selective decentralization therefore emerges as a key managerial capability for capturing blockchain's benefits without sacrificing scale efficiencies.

  PublisherOA PDFDOI

• Privacy Regulation and Ad-Tech Consolidation

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Promoting Sustainable Choices: A Large Scale Randomized Field Experiment *

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Identifying the Economic Implications of Artificial Intelligence for Copyright Policy

  SSRN Electronic Journal · 2025-01-01 · 2 citations

  articleOpen access
  PublisherDOI

• Introduction to the Special Issue on the Human-Algorithm Connection

  Management Science · 2025-11-24 · 1 citations

  article
  PublisherDOI

• Data Deserts and Black Boxes: The Impact of Socio-Economic Status on Consumer Profiling

  Management Science · 2024-01-31 · 16 citations

  article

  Data brokers use black-box methods to profile and segment individuals for ad targeting, often with mixed success. We present evidence from 5 complementary field tests and 15 data brokers that differences in profiling accuracy and coverage for these attributes mainly depend on who is being profiled. Consumers who are better off—for example, those with higher incomes or living in affluent areas—are both more likely to be profiled and more likely to be profiled accurately. Occupational status (white-collar versus blue-collar jobs), race and ethnicity, gender, and household arrangements often affect the accuracy and likelihood of having profile information available, although this varies by country and whether we consider online or offline coverage of profile attributes. Our analyses suggest that successful consumer-background profiling can be linked to the scope of an individual’s digital footprint from how much time they spend online and the number of digital devices they own. Those who come from lower-income backgrounds have a narrower digital footprint, leading to a “data desert” for such individuals. Vendor characteristics, including differences in profiling methods, explain virtually none of the variation in profiling accuracy for our data, but explain variation in the likelihood of who is profiled. Vendor differences due to unique networks and partnerships also affect profiling outcomes indirectly due to differential access to individuals with different backgrounds. We discuss the implications of our findings for policy and marketing practice. This paper was accepted by David Simchi-Levi, marketing. Funding: Financial support from the National Science Foundation [CAREER Award 6923256] and an anonymous panel company is gratefully acknowledged. Supplemental Material: The web appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4979 .

  PublisherDOI

Recent grants

• CAREER Digital Privacy and Regulation

  NSF · $503k · 2011–2019

Frequent coauthors

• Avi Goldfarb

  National Bureau of Economic Research

  65 shared

• Amalia R. Miller

  National Bureau of Economic Research

  46 shared

• S. Sriram

  Ross School

  38 shared

• Raghuram Iyengar

  University of Pennsylvania

  37 shared

• Juanjuan Zhang

  37 shared

• Koen Pauwels

  Northeastern University

  37 shared

• Sriraman Venkataraman

  Quantitative BioSciences

  37 shared

• Anindya Ghose

  New York University

  37 shared

Awards & honors

• NSF CAREER Award for her work on digital privacy
• Erin Anderson Award for an Emerging Female Marketing Scholar…
• Garfield Economic Impact Award for her work on electronic me…
• Paul E. Green Award for contributions to the practice of Mar…
• William F. O'Dell Award for most significant, long-term cont…