I develop data analysis methods to inform core marketing decisions such as designing new products and planning advertising campaigns. Recently, I’ve focused on how marketers can use randomized experiments to measure advertising incrementality. I’ve also published research on conjoint analysis and data fusion. Methodologically, I am Bayesian with expertise in MCMC sampling, hierarchical models, missing data and decision theory.
My research is inspired by the decision problems that marketers face and I’ve spent most of my career at the boundary between academia and industry, first as a research scientist at General Motors R&D, then as a methodologist at The Modellers and as the Executive Director of Wharton Customer Analytics, and now as a Associate Professor at Drexel University.
I enjoy teaching marketing analytics at all levels and have developed courses at Drexel in data-driven digital marketing (undergraduate), marketing experiments (masters), and Bayesian and causal inference (PhD). I have also developed several workshops and online courses on marketing analytics and wrote R for Marketing Research and Analytics with Chris Chapman, which has been translated to Chinese, Japanese and Korean and adapted to Python.