Elea McDonnell Feit is Associate Professor of Marketing and Associate Dean of Research at Drexel University’s LeBow College of Business. She has spent most of her career at the boundary between academia and industry working as a research scientist at General Motors R&D, as a methodologist at The Modellers, as the Executive Director of Wharton Customer Analytics and as an economist at Amazon Ads. Her research is inspired by the decision problems that marketers face and most recently she has focused on how marketers can use randomized experiments to measure advertising incrementality to improve ROI of ad campaigns. She has also published research on advertising auctions, conjoint analysis and data fusion. Methodologically, she is Bayesian with expertise in MCMC sampling, hierarchical models, missing data and decision theory. Elea enjoys making marketing analytics accessible and has developed courses in data-driven digital marketing, marketing experiments, and Bayesian and causal inference. She has developed several open-source workshops and online courses for practitioners and is co-author of R for Marketing Research and Analytics, which has been translated to Chinese, Japanese and Korean and adapted to Python.

Views here do not represent my employers

Elea McDonnell Feit


Elea McDonnell Feit is Associate Professor of Marketing and Associate Dean of Research at Drexel University’s LeBow College of Business. She has spent most of her career at the boundary between academia and industry working as a research scientist at General Motors R&D, as a methodologist at The Modellers, as the Executive Director of Wharton Customer Analytics and as an economist at Amazon Ads. Her research is inspired by the decision problems that marketers face and most recently she has focused on how marketers can use randomized experiments to measure advertising incrementality to improve ROI of ad campaigns. She has also published research on advertising auctions, conjoint analysis and data fusion. Methodologically, she is Bayesian with expertise in MCMC sampling, hierarchical models, missing data and decision theory. Elea enjoys making marketing analytics accessible and has developed courses in data-driven digital marketing, marketing experiments, and Bayesian and causal inference. She has developed several open-source workshops and online courses for practitioners and is co-author of R for Marketing Research and Analytics, which has been translated to Chinese, Japanese and Korean and adapted to Python.

Views here do not represent my employers