About

Professor Oded Netzer is the Vice Dean of Research and the Arthur J. Samberg Professor of Business at Columbia Business School. He is also an affiliate of the Columbia Data Science Institute and the author of the book Decisions over Decimals. Professor Netzer is a world-renowned expert in data-driven decision-making and extracting meaningful insights from data. He has written dozens of papers published in top-tier academic journals, and his award-winning research is broadly read and highly cited. In addition to his research accomplishments, Professor Netzer is an award-winning teacher at Columbia Business School, where he teaches in the MBA, Executive MBA, and Executive Education programs.

Research topics

• Computer Science
• Data science
• Machine Learning
• Business
• Data Mining
• Mathematics
• Econometrics
• World Wide Web
• Database
• Biology
• Psychology
• Marketing
• Advertising

Selected publications

• Vol II Introduction: Analysis Methods

  2026-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Intermodality: Combining Linguistic, Audio, and Visual Content for Insight

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• New Tools, New Roles: A Manager’s Guide to Harnessing Generative AI for Marketing Insight

  NIM Marketing Intelligence Review · 2026-04-08

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Learning from Many Experiments: A Hierarchical Bayesian Framework for Decomposing Marketing Treatment Heterogeneity

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• State-Dependence Effects in Surveys

  2026-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• The Effect of Pregnancy and Childbirth on Consumption Behavior

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• The Case for Synthetic Data

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Personalized game design for improved user retention and monetization in freemium games

  International Journal of Research in Marketing · 2025-01-20 · 5 citations

  article
  PublisherDOI

• Personalization and targeting: how to experiment, learn & optimize

  International Journal of Research in Marketing · 2025-07-01 · 5 citations

  articleOpen accessSenior author

  Personalization has become the heartbeat of modern marketing. The rapid expansion of individual-level data, the proliferation of personalized communication channels, and advancements in experimentation have fundamentally reshaped how firms tailor their marketing strategies. Furthermore, causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic overview of these developments. We formalize personalization as a causal inference problem embedded in the test and learn framework. We review key challenges and solutions that arise when personalization is approached through causal inference, including data limitations, treatment effect heterogeneity, policy evaluation, and ethical considerations. Finally, we identify emerging research trends stemming from new methodologies such as generic and double machine learning, direct policy learning, foundation models, and generative AI.

  PublisherDOI

• Sell Me a Story

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Ran Kivetz

  16 shared

• Eva Ascarza

  14 shared

• Rom Y. Schrift

  10 shared

• V. Seenu Srinivasan

  Stanford University

  9 shared

• Peter Ebbes

  7 shared

• Ronen Feldman

  Hebrew University of Jerusalem

  6 shared

• Verena Schoenmueller

  6 shared

• Gita Venkataramani Johar

  Columbia University

  5 shared

Education

• PhD, Marketing

  Stanford Graduate School of Business

  2004