معرفی
Oded Netzer is the Arthur J. Samberg Professor of Business at Columbia Business School, Columbia University, with center affiliations in Financial and Business Analytics and Computational Social Science. His expertise focuses on developing quantitative methods to leverage data for understanding customer behavior and guiding strategic business decisions.
Professor Netzer specializes in building statistical and econometric models to measure consumer preferences and analyze dynamic choice patterns across contexts. He pioneered a framework for customer base management through dynamic segmentation and has recently advanced text-mining applications for business insights. His research bridges marketing science with computational techniques, particularly in natural language processing for causal analysis of consumer language and behavior.
Recent publications (2023-2025) reveal a strong trajectory toward AI-driven marketing solutions, including generative AI for survey research, personalized game design, and human-AI collaboration in B2B pricing. Key themes include extracting value from unstructured text data, optimizing targeting policies, and understanding the causal impact of linguistic elements in consumer interactions.
His research excellence is recognized through multiple awards:
- ISMS Long-term Contribution Award
- John Little Best Paper Award
- Frank Bass Outstanding Dissertation Award
- Paul E. Green Best Paper Award
- William O’Dell Best Paper Award
- SCP Best Paper Award
- Columbia Business School Dean’s Award for Teaching Excellence
- GSAC Faculty Mentoring Award
Professor Netzer teaches Core Marketing, Marketing Research, Developing Quantitative Intuition (QI), and doctoral courses in Empirical Models in Marketing. His industry impact extends through consulting for Fortune 500 companies on data-driven strategy, marketing research, and extracting insights from sparse data sources. He serves on the editorial boards of Marketing Science, Management Science, Journal of Marketing Research, Journal of Marketing, and Quantitative Marketing and Economics.
