Homepage of Eric M. Schwartz
Last updated 2019-05-05

Curriculum Vita.
Welcome to Eric Schwartz's simple website!
Eric Schwartz is the Arnold M. and Linda T. Jacob Faculty Fellow, Assistant Professor of Marketing at the Stephen M. Ross School of Business at the University of Michigan. See below for more about me.
What's New
- Aribarg, Anocha and Eric M. Schwartz (2019). Consumer responses to native advertising. Forthcoming Journal of Marketing Research.
- Misra, Kanishka, Eric M. Schwartz, Jacob D. Abernethy (2019). Dynamic online pricing with incomplete information using multi-armed bandit experiments. Marketing Science, 38(2), 226-252.
Journal Link.
PDF.
BibTeX.
Google Scholar.
- Schwartz, Eric M., Bradlow, Eric T., and Fader, Peter S. (2017). Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Science, 36(4), 500-522.
Journal Link.
PDF.
BibTeX.
Google Scholar.
- Schwartz, Eric M., Bradlow, Eric T., and Fader, Peter S. (2014). Model selection using database characteristics: Developing a classification tree for longitudinal incidence data. Marketing Science, 33(2), 188-205.
Journal Link.
PDF.
BibTeX.
Google Scholar.
Press Release.
- Berger, Jonah, and Eric M. Schwartz (2011). What drives immediate and ongoing word of mouth? Journal of Marketing Research, 48 (5), 869-880.
Journal Link.
PDF.
BibTeX.
Google Scholar. Featured in Contagious .
- Schwartz, Eric M., Jacob D. Abernethy, Jared Webb (2019). Active Learning for Sequential Household-level Targeted Intervention: An Application to Find Lead Pipes in Flint, Michigan. Preparing for resubmission to Marketing Science.
- Schwartz, Eric M., Kenneth Fairchild, Bryan Orme, Alexander Zaitzeff (2019). Active Learning for Ranking and Selection: Bandit MaxDiff for Idea Screening. Preparing for resubmission to Management Science.
- Rajaram, Prashant, Puneet Manchanda, and Eric M. Schwartz Bingeability and Ad Tolerance: New Metrics for the Streaming Media Age.
- Accepted presentation at AI and Marketing Science Workshop at AAAI 2018 (2018-Feb-02)
-
Abernethy, Jacob D., Alex Chojacki^, Arya Farahi^, Eric M. Schwartz, Jared Webb^* (2018). ActiveRemediation: The Search for Lead Pipes in Flint, Michigan.
KDD 2018, Proceedings of SIGKDD Conference on Knowledge Discovery and Data Mining, London, England, U.K. *Alphabetical order. ^Student.
-
Chojnacki, Alex^, Chengyu Dai^, Arya Farahi^, Guangsha Shi^, Jared Webb^, Daniel T. Zhang^, Jacob Abernethy, Eric M. Schwartz* (2017). A Data Science Approach to Understanding Residential Water Contamination in Flint. KDD 2017, Proceedings of SIGKDD Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada. ^Student. *Students first, then faculty; alphabetical order.
- PDF on Arxiv
- Work involving students of the Michigan Data Science Team (MDST).
- Press about Flint's lead levels in its water:
- Google funds research and app development, joint with U-M Flint Computer Science and Michigan Data Science Team (2016-May-3) reported in: Chicago Tribune, Tech Crunch, Gizmodo, The Hill, Detroit Free Press, MLive, Michigan Radio, The University Record, Michigan Engineering News.
- Jacob Abernethy, Cyrus Anderson, Chengyu Dai, Arya Farahi, Linh Nguyen, Adam Rauh, Eric M. Schwartz, Wenbo Shen, Guangsha Shi, Jonathan Stroud, Xinyu Tan, Jared Webb, Sheng Yang* (2016). Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing, in Proceedings of 2nd Annual Bloomberg Conference Data for Good Exchange (D4GX 2016), NY, NY.
*alphabetical order.
- Jake Abernethy, Cyrus Anderson, Alex Chojnacki, Chengyu Dai, John Dryden, Eric M. Schwartz, Wenbo Shen, Jonathan Stroud, Laura Wendlandt, Sheng Yang, Daniel Zhang* (2016). Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences, in Proceedings of 2nd Annual Bloomberg Conference Data for Good Exchange (D4GX 2016), NY, NY.
*alphabetical order.
- Fairchild, Kenneth, Bryan Orme, Eric M. Schwartz (2015), Bandit Adaptive MaxDiff Designs for Huge Number of Items, in Proceedings of 2015 Sawtooth Software Conference, 105-117.
- PDF. Work in Collaboration with Sawtooth Software.
- Data science work to find Flint's lead service lines:
- 2019:
NPR OnPoint (2019-08-22),
The Atlantic (2019-01-02),
American Civil Liberties Union (video: 2019-04-10),
ACLU / Flint Journal (2019-04-12),
Natural Resources Defense Council (2019-02-12),
Irish Times (2019-05-02), Michigan Radio
(2019-04-12,
audio: 2019-04-25),
Weather.com (2019-04-29),
Flint Journal
(2019-02-12,
2019-04-11),
Detroit News
(2019-02-12),
Now This News (video: 2019-04-28)
- 2018:
U.S. District Court filing (Concerned Pastors et al. v Kohuri et al., 2018-10-01),
Flint City Council Meeting (2018-12-05),
Bridge Michigan (2018-09-04),
New Scientist (2018-08-22),
Flint Journal (
2018-10-18
,
2018-11-26
),
Bloomberg: Environment (2018-09-21),
Bloomberg: Law and Business (2018-08-09),
Michigan Today (2018-08-20, Video)
- 2016-17: Co-authored report with City of Flint and FAST Start team for service line replacement,
City of Flint Press Release (2016-Dec-01),
CBS Local (2016-Dec-02),
MLive (2016-Dec-01),
The Detroit News (2016-Dec-01,
AccuWeather (2017-May-04),
New York Times (2017-Mar-27),
Wikipedia Citation (Accessed 2018-Jan-01)
- Data science work to predict levels of lead in drinking water in Flint:
- Co-authored op-ed (with Jacob Abernethy) How big data and algorithms are slashing the cost of fixing Flint's water crisis. The Conversation (2016-Sep-08) republished/reported in --
Scientific American ,
Business Insider (2016-Sep-08),
Associated Press (2016-Sep-08),
USA Today (2016-Sep-08),
Government and Technology (date),
Detroit Free Press (
2016-Sep-08,
2016-Sep-09,
2016-Sep-26, and
2016-Sep-11),
GreedBiz (2016-Sep-08),
Civics Analytics and Urban Intelligence on Medium (2016-Oct-30),
U-M Office of Government Relations - Michigan Impact (2016-Oct-30)
Current courses
- Marketing Management, MKT 503, MBA Core (2018 F)
- Living Business Leadership Experience, MBA (2018-19 AY)
Past courses
- Marketing Management, MKT 503, MBA Core (2017 F)
- Marketing Management, MKT 300, BBA Core (2013 F, 2014 F, 2015 F, 2016 F)
Teaching interests
- Customer-base analysis and customer lifetime value; data science, model building, and statistical machine learning for customer analytics; marketing research and experimental design in marketing practice.
Teaching materials developed
Eric Schwartz is an Assistant Professor of Marketing the Arnold M. and Linda T. Jacob Faculty Fellow, 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. 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{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}
}
@article{aribargschwartz2017native,
title={Consumer responses to native advertising},
author={Aribarg, Anocha and Schwartz, Eric M},
year={2017}
}
(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 ~