Eric T. Bradlow, PhD

Eric Bradlow

The K. P. Chao Professor; Professor of Marketing; Vice Dean, Analytics at Wharton; Chairperson, Wharton Marketing Department; Professor of Economics; Professor of Education; Professor of Statistics, The Wharton School

Research Interests:

Marketing research methods, missing-data problems, psychometrics


About Eric:

An applied statistician, Eric uses high-powered statistical models to solve problems on everything from Internet search engines to product assortment issues. Specifically, his research interests include Bayesian modeling, statistical computing, and developing new methodology for unique data structures with application to business problems.

Eric’s research has been published in the Journal of the American Statistical Association, Psychometrika, Statistica Sinica, Chance, Marketing Science, Management Science, and the Journal of Marketing Research. His most recent study is “Putting a Price Tag on Facebook: Quantifying the Value of Online Social Networks.”

Eric has won numerous teaching awards at Wharton, including the MBA Core Curriculum teaching award, the Miller-Sherrerd MBA Core Teaching Award and the Excellence in Teaching Award. In 2009, he published (with Keith Niedermeier and Patti Williams) Marketing for Financial Advisors (McGraw-Hill).

Read full faculty bio on Wharton website


Executive Education Programs Taught:

Business Analytics: From Data to Insights — ONLINE


This three-month online certificate program provides managers and leaders an understanding of how analytics can help improve their decision-making process. This program will help you look at data and identify insights, improve your ability to make predictions for the long term, and prescribe future actions that help make better business decisions.

Customer Analytics for Growth Using Machine Learning, AI, and Big Data


Sharpen your analytics mindset and build competency in the skills needed to oversee data-driven business decisions. Discover how to bridge knowledge and communication gaps that may exist between your data science teams and your organization’s leadership.