Welcome to Eric Schwartz's simple website!

• [2019 Feb] New update to agreement in Federal District Court requires the use of our data-driven approach to find Flint's lead pipes.
• [2019 Feb] Talk at MSI Marketing Analytics for the Transforming Media Landscape.
• [2019 Jan] Talk at MSI Young Scholars

• Research
  • Journal Publications
  • Peer-Reviewed Conference Proceedings Papers
  • Newest Working Papers
  • Other Conference Papers
  • BibTeX Citations
• Press and Media
• Teaching
• About Me

Research and Publications

Journal Publications

1. Misra, Kanishka, Eric M. Schwartz, Jacob D. Abernethy (2019). Dynamic online pricing with incomplete information using multi-armed bandit experiments. Marketing Science, Forthcoming. PDF. BibTeX. Google Scholar.

2. 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.
   • Winner of the 2017 John D. C. Little Award for the best marketing paper in Marketing Science, Management Science, and all INFORMS journals.

3. 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.

4. 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 .

Newest Working Papers

• Aribarg, Anocha and Eric M. Schwartz (2018). Consumer responses to native advertising. Invited for 3rd round review at Journal of Marketing Research.
  • PDF on SSRN.
  • Accepted/Invited presentations at UT Dallas Bass FORMS Conference (2018-Mar), Workshop on Internet and Advertising at Columbia (2018-Jul)
• Schwartz, Eric M. and Jacob D. Abernethy (2018). Active Learning for Sequential Household-level Targeted Intervention: An Application to Find Lead Pipes in Flint, Michigan. Preparing for resubmission to Marketing Science.
  • PDF.
  • Press about Flint's lead pipes
• Schwartz, Eric M., Kenneth Fairchild, Bryan Orme, Alexander Zaitzeff (2018). Active Learning for Ranking and Selection: Bandit MaxDiff for Idea Screening. Preparing for resubmission to Management Science.
  • PDF.
  • Developed in collaboration with Sawtooth Software.
  • Bandit MaxDiff available in Sawtooth Software Lighthouse Studio v9.6 and Support Documentation.
• 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)

Peer-Reviewed Conference Proceedings Papers

• 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.
  • PDF on Arxiv
  • Video
  • Best Student Paper Award, KDD Applied Data Science (2 best paper awards out of 500 submissions in KDD 2018 Applied Data Science track)
  • Press about Flint's lead pipes
• 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.

Other Conference Proceedings

• 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.
  • PDF on Arxiv. Work involving students of the Michigan Data Science Team (MDST).
• 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.
  • PDF. Work involving students of the Michigan Data Science Team (MDST) in collaboration with the University Musical Society, Ann Arbor, MI.
• 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.

Press and Media

• Data science work to find Flint's lead service lines:
  • 2019: The Atlantic (2019-01-02), Flint Journal (2019-02-12), Natural Resources Defense Council (2019-02-12)
  • 2018: U.S. District Court filing (2018-10-01,Concerned Pastors et al. v Kohuri et al.), 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)
  • 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)

Teaching

• Marketing Management, MKT 503, MBA Core (2018 F)
• Living Business Leadership Experience, MBA (2018-19 AY)

• Marketing Management, MKT 503, MBA Core (2017 F)
• Marketing Management, MKT 300, BBA Core (2013 F, 2014 F, 2015 F, 2016 F)

• 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.

• "Blue Apron: Turning Around the Struggling Meal Kit Market Leader" Case study by Daniel M. McCarthy and Eric M. Schwartz . Available for preview and purchase at Harvard Business School Publishing (#W73C09-PDF-ENG) and Michigan WDI Publishing (#5-177-309).

About Me

Employment and Education

• Assistant Professor, Marketing Area, Stephen M. Ross School of Business, University of Michigan, 2013-present
• Arnold M. and Linda T. Jacob Faculty Fellow, Michigan Ross, 2018-19
  

• Ph.D. Marketing Department, The Wharton School, University of Pennsylvania, 2013
• B.A. Mathematics and Hispanic Studies, College of Arts and Sciences, University of Pennsylvania, 2008

Bio

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

(End)