Financial and Business Analytics
Computational Social Science
Professor Netzer’s expertise centers on one of the major business challenges of the data-rich environment: developing quantitative methods that leverage data to gain a deeper understanding of customer behavior and guide firms’ decisions. He focuses primarily on building statistical and econometric models to measure consumer preferences and understand how customer choices change over time, and across contexts. Most notably, he has developed a framework for managing firms’ customer bases through dynamic segmentation. More recently, his research focuses on leveraging text-mining techniques for business applications.
Professor Netzer published numerous papers in the leading scholarly journals. His research was nominated for and won multiple awards including, ISMS Long-term Contribution Award, the John Little Best Paper Award, the Frank Bass Outstanding Dissertation Award, the Paul E. Green Best Paper Award, the William O’Dell Best Paper Award, the Gary L. Lilian ISMS/MSI Practice Prize Award, the Society for Consumer Psychology (SCP) Best Paper Award, and the George S. Eccles Research Fund Award. He serves on the editorial board of several leading journals including: Marketing Science, Management Science, Journal of Marketing Research, Journal of Marketing, Quantitative Marketing and Economic, and International Journal of Research in Marketing.
Oded teaches several courses including the Core Marketing course, a course on Marketing Research, a course on Developing Quantitative Intuition (QI), a masters and doctoral course on Empirical Models in Marketing, as well as several executive education programs. Professor Netzer has won the Columbia Business School Dean’s Award for Teaching Excellence, and the Columbia University GSAC Faculty Mentoring Award to commemorate excellence in the mentoring of Ph.D. students.
Professor Netzer frequently consult to Fortune 500 companies and entrepreneurial organization on strategy, data-driven decision making, marketing research and extracting useful information from rich and thin data.