Homepage of Eric M. Schwartz

Last updated 2021-06-05

Eric Scwhartz faculty photo
Curriculum Vita.

Welcome to Eric Schwartz's simple website!

Eric Schwartz is an Associate Professor of Marketing, with tenure, at the Stephen M. Ross School of Business at the University of Michigan. He is a data scientist applying research in statistics, machine learning, and econometrics to a range of problems. These span problems in customer analytics for marketing, such as A/B testing methods, native advertising, streaming media, and valuing customers, as well as in optimal resource allocaiton for public health. In the classroom, Professor Schwartz focuses on the quantitative aspects of marketing, including an elective on customer lifetime value as well as the introductory core marketing course. He is also a co-founder of BlueConduit, a social venture spun out of the University of Michigan applying machine learning resarch developed during the Flint Water Crisis to find lead pipes to cities and utilites across North America. For more biographical information, see below.

What's New

Research and Publications

Journal Publications

  1. Aribarg, Anocha and Eric M. Schwartz (2020). Native advertising in online news: Tradeoffs among clicks, brand recognition and website trustworthiness, *Journal of Marketing Research*, 57(1), 20-24. Journal Link. PDF. BibTeX.

Working Papers

Peer-Reviewed Conference Proceedings Papers

Other Conference Proceedings

Teaching

Current courses
Past courses
Teaching interests
Teaching materials developed

Press and Media

About Me

Employment and Education

Employment Education

Bio

Eric Schwartz is an Associate Professor of Marketing (with tenure) at the Stephen M. Ross School of Business at the University of Michigan. Professor Schwartz's expertise focuses on predicting customer behavior, understanding its drivers, and examining how firms actively acquire customers and manage their relationships through interactive marketing experiments and adaptive data collection. His current projects aim to optimize firms' A/B testing and adaptive marketing experiments using a multi-armed bandit framework, often working with companies and organizations. His broader research in customer analytics stretches across managerial applications, including online experiments, online advertising, dynamic pricing, native advertising, streaming video binge viewing, and word-of-mouth. The quantitative methods he uses are primarily machine learning, active learning, Bayesian statistics, and field experiments. Applying those same methods elsewhere, he also works on public policy problems focused on health and safety. His work has been recognized with awards, including ISMS John D. C. Little Best Paper, ISMS Doctoral Dissertation Proposal Competition Winner, and KDD Applied Data Science Best Student Paper. He is a member of the Editorial Review Board of INFORMS journal, Marketing Science. At Ross, he was the Arnold M. and Linda T. Jacob Faculty Fellow 2018-19. Before joining the Michigan Ross faculty in 2013, Professor Schwartz earned his Ph.D. in Marketing from the Wharton School and a B.A. in Mathematics and Hispanic Studies, all from the University of Pennsylvania.

BibTeX Citations

@article{aribargschwartz2020native,
title={Native Advertising in Online News: Trade-Offs Among Clicks, Brand Recognition, and Website Trustworthiness},
author={Aribarg, Anocha and Schwartz, Eric M},
journal={Journal of Marketing Research},
volume={57},
number={1},
pages={20--34},
year={2020}
}

@article{msa2018banditpricing,
title={Customer acquisition via display advertising using
multi-armed bandit experiments},
author={Misra, Kanishka and Schwartz, Eric and Jacob D. Abernethy},
journal={Marketing Science},
volume={Forthcoming},
year={2018},
publisher={INFORMS}
}

@article{schwartzetal2017bandit,
title={Customer acquisition via display advertising using
multi-armed bandit experiments},
author={Schwartz, Eric M and Bradlow, Eric T and Fader, Peter S},
journal={Marketing Science},
volume={36},
number={4},
pages={500--522},
year={2017},
publisher={INFORMS}
}

@article{schwartzetal2014hmmrf,
title={Model selection using database characteristics: Developing a classification tree for longitudinal incidence data},
author={Schwartz, Eric M and Bradlow, Eric T and Fader, Peter S},
journal={Marketing Science},
volume={33},
number={2},
pages={188--205},
year={2014},
publisher={INFORMS}
}

@article{bergerschwartz2011wom,
title={What drives immediate and ongoing word of mouth?},
author={Berger, Jonah and Schwartz, Eric M},
journal={Journal of Marketing Research},
volume={48},
number={5},
pages={869--880},
year={2011},
publisher={American Marketing Association}
}

(End)

This page was typed by hand and written in HyperText Markup Language (HTML). That means Web 1.0, 1990s style, without any fancy apps and slick Web 2.0 style graphics. No WhatYouSeeIsWhatYouGet editors are needed. This is a flat website in a single page. You can see all contents with full transparency with Show Page Source / Inspect Element in your browser. I used the template provided by Frank da Cruz.
~ Eric Schwartz ~