Eric M. Schwartz

Curriculum Vitae

Ross School of Business, University of Michigan
701 Tappan Street, Office R5472, Ann Arbor, MI 48109-1234
734-936-5042 (office)

[email protected] | ericmichaelschwartz.com | SSRN | Google Scholar

Academic Employment

Ross School of Business, University of Michigan

Associate Professor of Marketing, July 2020–present
Assistant Professor of Marketing, July 2013–2020
Arnold M. and Linda T. Jacob Faculty Fellow, July 2018–June 2019

Education

Ph.D. Marketing, Wharton School, University of Pennsylvania, May 2013
B.A. Mathematics and Spanish, College of Arts and Sciences, University of Pennsylvania, May 2008

Published Peer-Reviewed Papers

Braun, Michael, and Eric M. Schwartz (2025). Where A/B Testing Goes Wrong: How Divergent Delivery Affects What Online Experiments Cannot (and Can) Tell You About How Customers Respond to Advertising. Journal of Marketing, 89(2), 71–95. Journal Link. PDF.

Finalist, AMA Shelby D. Hunt/Harold H. Maynard Award (2025) — best paper in Journal of Marketing

Finalist, AMA/MSI/H. Paul Root Award (2025) — best paper in Journal of Marketing

Braun, Michael, Bart de Langhe, Stefano Puntoni, and Eric M. Schwartz (2024). Leveraging Digital Advertising Platforms for Consumer Research. Journal of Consumer Research, 51(1), 119–128. Journal Link.
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.

Finalist, Paul E. Green Award (2020) — best paper in Journal of Marketing Research

Proserpio, D., J. R. Hauser, X. Liu, T. Amano, A. Burnap, T. Guo, D. Lee, R. A. Lewis, K. Misra, Eric M. Schwartz, A. Timoshenko, L. Xu, and H. Yoganarasimhan (2020). Soul and Machine (Learning). Marketing Letters, 31(4), Special Issue for 11th Triennial Invitational Choice Symposium, 393–404. Journal Link.
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.

Finalist, John D. C. Little Award (2019) — best marketing paper in Marketing Science, Management Science, and all INFORMS journals

Abernethy, J. D., A. Chojacki, A. Farahi, Eric M. Schwartz, J. 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. PDF on Arxiv. Video.

Winner, Best Student Paper Award, KDD 2018 — Applied Data Science Track

Chojnacki, A., C. Dai, A. Farahi, G. Shi, J. Webb, D. T. Zhang, J. 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. *Students first, then faculty; alphabetical order. PDF on Arxiv.
Schwartz, Eric M., Eric T. Bradlow, and Peter S. Fader (2017). Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Science, 36(4), 500–522. Journal Link. PDF.

Winner, John D. C. Little Award (2017) — best marketing paper in Marketing Science, Management Science, and all INFORMS journals

Schwartz, Eric M., Eric T. Bradlow, and Peter S. Fader (2014). Model selection using database characteristics: Developing a classification tree for longitudinal incidence data. Marketing Science, 33(2), 188–205. Journal Link. PDF. 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. Featured in Contagious.

Working Papers

Rajaram, Prashant, Puneet Manchanda, and Eric M. Schwartz (2025). Finding the Sweet Spot: Ad Scheduling on Streaming Media. PDF on SSRN.
- Invited for revision at Production and Operations Management.
- Earlier short version appeared in AAAI 2018, Workshop on AI and Marketing Science.
Ahn, Gwen, Fred Feinberg, and Eric M. Schwartz (2025). Customizing Bundles of Experiential Goods: An Application to Performing Arts Ticket Sales.
- Invited for revision at Marketing Science.
Li, T., T. de Queiroz Casanova, Eric M. Schwartz, V. Kostyuk, D. Kong, G. Phillips, and J. J. Williams (2025). RIE-Greedy: Regularization-Induced Exploration for Contextual Bandits. arXiv:2603.11276.
Li, T., T. Mandel, G. Phillips, A. Rafferty, Eric M. Schwartz, D. Kong, and J. J. Williams (2026). A Statistically Reliable Optimization Framework for Bandit Experiments in Scientific Discovery. arXiv:2603.11267.
- Invited for rebuttal at KDD 2026.
Li, Tong, Eric M. Schwartz, and Joseph J. Williams (2026). Adaptive Experiments in Factorial Designs: PostDiff as a Power-Analysis-Driven Algorithm for Balancing Reward and Inference.
Li, T., J. Nogas, H. Song, H. Kumar, A. Durand, A. Rafferty, N. Deliu, S. S. Villar, Eric M. Schwartz, and J. J. Williams (2025). Algorithm for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Adaptively Combining Uniform Random Assignment and Thompson Sampling. arXiv:2112.08507.
Schwartz, Eric M., Jacob D. Abernethy, and Jared Webb (2026). What Public Utilities Can Teach Us About Customer Targeting Methods: When Bandits, Active Learning, and Tree Ensembles Combine. PDF.
- Preparing for resubmission to Marketing Science.
- Press about this work in practice finding Flint's lead pipes.
- Featured on the Business + Society Podcast: BlueConduit and Flint Water Project.

Work in Progress

Active Learning for LLM Fine-Tuning and Prompt Optimization, with John Loretizo (Ross PhD student).
- Develops active-learning methods for LLM fine-tuning labels and prompt selection, with application to student evaluation of teaching and instructors. Generalizes from SETs to any expensive-labeling problem (e.g., consumer reviews, content moderation).
“Active Learning for Ranking and Selection: Bandit MaxDiff for Idea Screening” (2019) with Kenneth Fairchild, Bryan Orme, Alexander Zaitzeff.
- Preparing for resubmission to Management Science.
- Developed in collaboration with Sawtooth Software.
- Bandit MaxDiff available in Sawtooth Software Lighthouse Studio v9.6.
“Sequential Allocation for Customer Acquisition: Delayed Bandits with Partial Monitoring Feedback” (2021) with Liangbin Yang and Peter S. Fader.

Other Published Conference Proceedings

Cha, S., C. Byrd, T. Drake, M. Ronfeldt, and Eric M. Schwartz (2025). “Identifying Identity-Based Bias in Student Evaluation of Teaching Through Large Language Models Anchored on Human Judgements.”
Rajaram, Prashant, Puneet Manchanda, and Eric M. Schwartz (2018). “Bingeability and Ad Tolerance: New Metrics for the Streaming Media Age.” Workshops of the 32nd AAAI Conference on Artificial Intelligence, pp. 93–99.
Abernethy, J., C. Anderson, C. Dai, A. Farahi, L. Nguyen, A. Rauh, Eric M. Schwartz, W. Shen, G. Shi, J. Stroud, X. Tan, J. Webb, and S. Yang* (2016). “Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing.” In Proceedings of Bloomberg Conference Data for Good Exchange, NY, NY. *Alphabetical order. PDF on Arxiv.
- In collaboration with Michigan Data Science Team, University of Michigan.
Abernethy, J., C. Anderson, A. Chojnacki, C. Dai, J. Dryden, Eric M. Schwartz, W. Shen, J. Stroud, L. Wendlandt, S. Yang, and D. Zhang* (2016). “Data Science in Service of Performing Arts: Applying Machine Learning to Predicting Audience Preferences.” In Proceedings of Bloomberg Conference Data for Good Exchange, NY, NY. *Alphabetical order. PDF.
- In collaboration with Michigan Data Science Team and University Musical Society, University of Michigan.
Fairchild, Kenneth, Bryan Orme, Eric M. Schwartz (2015). “Bandit Adaptive MaxDiff Designs for Huge Number of Items.” Proceedings of 2015 Sawtooth Software Conference, 105–117. PDF.
- Formed the basis for commercialization of Sawtooth Software’s Bandit MaxDiff product.

Research Interests

Substantive: adaptive marketing experiments, digital advertising, dynamic pricing, customer acquisition and lifetime value, media consumption, public health policy, social impact, environmental justice

Methodological: statistical machine learning, adaptive sampling, multi-armed bandits, active learning, reinforcement learning, artificial intelligence, Bayesian data analysis and econometrics, and dynamic programming

Research Seminars, Invited Talks, Competitive Conferences

(in person unless otherwise indicated)

Virtual Quantitative Marketing Seminar (remote)	April 2026
Hosmer-Hall Seminar, Michigan Ross	March 2026
UT Dallas Bass FORMS, Discussant	February 2026
Maryland, Smith Marketing Seminar	2025 (rescheduled)
Emory, Goizueta Marketing, Research Camp	May 2024
WU Vienna University of Economics and Business, Marketing	April 2023
London Business School, Marketing Camp	June 2022
University of Delaware, Lerner Marketing Camp	April 2022
Bocconi University, Marketing	March 2022
Virtual Quantitative Marketing Seminar – Panelist (remote)	March 2022
University of Georgia, Terry Marketing (remote)	2022
Marketing Modelers (remote)	September 2021
UT Dallas Bass FORMS (remote)	February 2021
University of Colorado, Boulder, Leeds Marketing (remote)	January 2021
Northwestern University, Kellogg Marketing (COVID cancellation)	2020
Washington University–St. Louis, Olin Marketing (COVID cancellation)	2020
University of Florida, Marketing	February 2020
Duke University, Fuqua Marketing	January 2020
Southern Methodist University, Cox Marketing	October 2019
Temple University, Fox Marketing	October 2019
Marketing Effectiveness Conference, Bologna, Italy	June 2019
Choice Symposium, co-organizer	May 2019
University of Miami, Marketing	April 2019
Boston University, Questrom Marketing	March 2019
MSI Marketing Analytics Conference, Columbia, NY	February 2019
MSI Young Scholars, Park City, Utah	January 2019
Frontiers in Empirical Marketing, Miami	November 2018
University of Miami, PhD Seminar (remote)	October 2018
Economics of Advertising Workshop, Columbia	July 2018
Notre Dame, Mendoza Marketing	April 2018
UT Dallas Bass FORMS, Presenter	March 2018
Emory, Goizueta Marketing	March 2018
UT Dallas, Marketing	February 2018
Chicago Booth Marketing	February 2018
Michigan, School of Information Seminar	October 2017
Carnegie Mellon Tepper	September 2017
Erasmus University, Rotterdam School of Management	April 2017
UT Dallas Bass FORMS, Discussant	March 2017
Hosmer-Hall Seminar, Michigan Ross	January 2017
Management Science Workshop, Chile	January 2017
Quantitative Marketing and Economics	October 2016
Michigan, School of Public Health	September 2016
Michigan, Computer Science Engineering Faculty Seminar	September 2016
Dartmouth, Tuck Marketing Camp	June 2016
Texas A&M Marketing	April 2016
Marketing in Israel 15 Conference	December 2015
NYU Conference on Big Data and Marketing Analytics	October 2015
Hosmer-Hall Seminar, Michigan Ross	March 2015
Temple, Fox Global Center for Big Data and Mobile Analytics	November 2014
Microsoft Research, Seattle	June 2014
Cornell Johnson Marketing	February 2014
Electronic Arts, Redwood City	February 2014
Google Play, Mountain View	February 2014
Stanford GSB Marketing	January 2014
London Business School	November 2012
INSEAD	November 2012
University of Michigan	November 2012
UCLA	October 2012
NYU	October 2012
Carnegie Mellon	October 2012
Northwestern	October 2012
Yale	October 2012
Boston University	October 2012
Emory	September 2012
University of Pittsburgh	September 2012
University of Washington	September 2012
Rotterdam School of Management / Erasmus School of Economics	January 2012
Tilburg University	January 2012
Marketing in Israel 11 Conference	December 2011

Awards, Honors, and Grants

Highlighted Awards

Winner, AMA Robert J. Lavidge Global Marketing Research Award (2025) — Ross News | Reel
Finalist, AMA Shelby D. Hunt/Harold H. Maynard Award for Best Paper in Journal of Marketing (2025)
Finalist, AMA/MSI/H. Paul Root Award for Best Paper in Journal of Marketing (2025)
Finalist, Paul E. Green Award for Best Paper in Journal of Marketing Research (2020)
Finalist, John D. C. Little Award for Best Marketing Paper (2019)
Winner, John D. C. Little Award for Best Marketing Paper (2017)
AMA Seenu Srinivasan Award, Young Quantitative Scholar in Marketing (2019)
Poets & Quants Best 40 Under 40 Professors (2019)
Marketing Science Institute Young Scholar (2019)
KDD Best Student Paper Award, Applied Data Science (2018)
MSI Clayton Dissertation Proposal Competition, Honorable Mention (2012)
ISMS Doctoral Dissertation Proposal Competition, Sheth Winner (2012)

Other Awards

40 Under 40, Crain’s Business Detroit (2022)
AMA-Sheth Consortium Faculty Fellow (2019)
MCubed Grant ($60,000) with Elizabeth Bruch and Fred Feinberg (2019)
MCubed Grant ($60,000) with Laura Balzano and Al Hero (2016)
20 in Their 20s, Crain’s Business Detroit (2016)
Golden Apple Teaching Award Nominee, University of Michigan (2014, 2015)
Top 25 Reviewer for Marketing Science (2015)
AMA-Sheth Foundation Doctoral Consortium, Fellow (2011)
Workshop on Quantitative Marketing and Structural Econometrics, Fellow (2010)
Russell Ackoff Award for Doctoral Student Research, Recipient (2009–2012)
Jay H. Baker Retailing Initiative Research Grant, Recipient (2009)
Lauder CIBER Grant, Recipient (2009)
University of Pennsylvania Class of 1939 Fellowship, Recipient (2008–2009)
INFORMS Marketing Science Doctoral Consortium, Fellow (2009, 2010, 2011, 2012)
Wharton Doctoral Fellowship, Recipient (2008–2012)
Summa Cum Laude, Dean’s List, University of Pennsylvania, GPA: 3.9/4.0 (2004–2008)
Benjamin Franklin Scholar, University of Pennsylvania (2004–2008)

Media Coverage (Selected Press)

BlueConduit and Flint Water Project
Business + Society Podcast, Michigan Ross, 2025
Artificial Intelligence Accelerates Lead Pipe Removal in U.S. Cities
The Rockefeller Foundation, Dec 18, 2024
Biden sets 10-year deadline for US cities to replace lead pipes nationwide
Associated Press (and dozens of outlets), Oct 8, 2024
BlueConduit Launches LeadOut Map To Identify Potential Risk of Lead in Drinking Water
The Rockefeller Foundation, Aug 26, 2024
TIME Best Inventions 2021, “Lead Pipe Finder”
TIME Magazine, Nov 2021
We don’t know where all the lead pipes are. This tool helps find them
Fast Company, Oct 4, 2021
An Algorithm Is Helping a Community Detect Lead Pipes
WIRED, Sidney Fussell, Jan 14, 2021
After Flint’s Crisis, An Algorithm Helps Citizens Find Lead Pipes
NPR Science Friday (radio/podcast), Jan 22, 2021
Pandemic Paved the Way for Sim Racing, but Will It Last?
New York Times, Roy Furchgott, Jan 18, 2021

For more press and media coverage, see ericmichaelschwartz.com.

Teaching

Instructor at Michigan Ross

Marketing Management (MBA Core, MKT 503)
F 2017, ’18, ’19, ’21, ’22, ’23, ’24, ’25
Customer Analytics: Measuring and Managing Customer Value (MBA, MKT 626)
W 2021, F 2024, ’25
Customer Valuation (BBA, MKT 419)
F 2024, ’25
Enhancing Marketing Practice with AI (BBA, MKT 420)
Coming Winter 2026
Customer Analytics: Measuring and Managing Customer Value (BBA, MKT 426)
W 2020, W 2021
Marketing Management (BBA Core, MKT 300)
F 2013, 2014, 2015, 2016

Action-Based Learning Courses

Data Consulting Studio (MBAn), W 2024
Living Business Leadership Experience (MBA and BBA), 2018–19, 2019–20, 2020–21

Executive Education

Chief Data and AI Officer (CDAIO) Program, Michigan Ross, 2025–26
- Cohorts 1 and 2: delivered four live sessions (one online and one in-person each)
- Cohort 3: developed digital asynchronous modules

Teaching Materials Developed

“Blue Apron: Turning Around the Struggling Meal Kit Market Leader” (2018). Case study by Daniel M. McCarthy and Eric M. Schwartz. Michigan WDI Publishing (#5-177-309). Also available at Harvard Business School Publishing (#W73C09-PDF-ENG).
- 2nd most-selling WDI case, 2023
- Over 6,000 units sold across 17 countries
“BlueConduit: A Case Study” (2025). Case study by Charlene Zietsma and Eric M. Schwartz. Michigan WDI Publishing.

Teaching Interests

Customer-base analysis and customer lifetime value; model building and statistical machine learning for customer analytics; marketing research and experimental design in marketing practice; action-based learning.

Advising

PhD Advising

John Loretizo, Marketing, Michigan Ross — active learning for LLM fine-tuning and prompt optimization

PhD Dissertation Committee Member

Jangwon Choi	Marketing	2023
Gwen Ahn	Marketing	2023
Prashant Rajaram	Marketing	2021
Nicolas Padilla	Marketing (Columbia)	2021
Mengzhenyu (Zhenyu) Zhang	Technology and Operations	2021
Baekjin Kim	Statistics	2021
Aravind Govindarajan	Technology and Operations	2019
Longxiu Tian	Marketing	2019
Aniket Anand Deshmukh	Electrical and Computer Engineering	2019
Yanzhe (Murray) Lei	Technology and Operations	2018
Evgeny Kagan	Technology and Operations	2018
Eunsoo Kim	Marketing	2017
Qi George Chen	Technology and Operations	2017
Guy Benedict Wilkinson	Sports Management	2017
Joseph Golden	Economics	2015

Service

To the Marketing Field

Editorial Review Board

Marketing Science (2019–present)

Ad hoc reviewer

Journal of Marketing
Journal of Marketing Research
Management Science
Quantitative Marketing and Economics
Journal of Consumer Research
International Journal of Marketing Research
Information Systems Research
Marketing Letters
Journal of the American Statistical Association
Journal of Applied Econometrics

Conference activity

Co-Host, Marketing Science Institute, Young Scholars (June 2022)
Session co-organizer, “Consumer Choice, Big Data, and Machine Learning,” Choice Symposium (May 2019)
Track co-organizer, “Machine Learning in Marketing,” Marketing Science Conference, Philadelphia (June 2018)
Track co-organizer, “Machine Learning in Marketing,” Marketing Science Conference, Los Angeles (June 2017)
Session co-organizer, “Machine Learning in Marketing,” INFORMS Annual Meeting, Philadelphia (November 2015)

To Ross School of Business and the University of Michigan

Undergraduate Council for Curriculum (chair: Scott Page), 2025–26
Science Communication Committee (chair: Scott Rick), 2025–26
Ross MBA Faculty Council, 2020–21, 2024–25, 2025–26
Marketing AI Symposium, Co-coordinator with Anocha Aribarg, 2025–26
Ross MBA Marketing Club Advisor, 2023–24, 2024–25, 2025–26
Ross Marketing MBA Core Course Instructor, 2017, 2018, 2019, 2020, 2021, 2023, 2024, 2025
- Coordinator: 2020 F, 2021 F, 2023 F
Ross Community Values Committee, 2023–24
School of Information, Theme Year of Water Advisor, 2023–24
Ross Marketing Faculty Recruiting Committee, 2023 F
Ross Centennial Committee, 2023
Ross Marketing Internal Renewal Committee, 2021, 2023, 2024
Ross Marketing Seminar Organizer, 2018–19, 2019–20, 2020–21
Ross Marketing Student Awards Committee, 2013–14, 2014–15, 2016–17, 2017–18, 2018–19, 2019–20
Ross MBA Data Insights and Analytics Club Co-Advisor, 2015–16, 2016–17, 2017–18, 2018–19, 2019–20
Michigan Data Science Team (MDST), Faculty Co-Advisor, 2015–16, 2016–17, 2017–18, 2019–20
Michigan Initiative for Data Science (MIDAS), Education and Training Committee, 2018–19
Ross Marketing Undergrad Core Course Coordinator, 2015 F, 2016 F
Ross Undergraduate Marketing Club Advisor, 2014–15, 2015–16, 2016–17
School of Kinesiology, Sports Marketing Faculty Search Committee, 2016 W
Ross Marketing Internal Seminar Coordinator, 2014–15
Marketing Undergraduate Case Competition Coach, 2014 (First place at national L’Oreal Brandstorm Competition)
Google Online Marketing Challenge Advisor, 2015, 2016

Professional Affiliations

American Marketing Association
INFORMS Society for Marketing Science
American Statistical Association
Phi Beta Kappa

Other Affiliations

Co-Founder and Board Member, BlueConduit, LLC.

Company providing data science software and consulting to identify homes with hazardous lead drinking water pipes for water utilities spanning 300+ cities and towns in US and Canada.
Charitable work in under-resourced communities funded by Google.org, Rockefeller Foundation, and Kresge Foundation.
Launched through University of Michigan’s Office of Innovation Partnerships (fka Tech Transfer).
Featured on the Business + Society Podcast: BlueConduit and Flint Water Project.

LeadOut.org

One-of-a-kind public dataset for lead service line remediation, funded by the Rockefeller Foundation and Google.org. Used by collaborators at Columbia, Berkeley, and University of Michigan Ford School.

Computer and Natural Languages

Fluent: R, Spanish, SQL, Tidyverse
Proficient: Catalan, Matlab, Python, SAS

← Back to homepage

~ Eric M. Schwartz ~

Michigan Ross AI for Research Talk	April 2026
Michigan Ross AI for Teaching Talk	January 2026
MSI Young Scholars, Boulder – Host	June 2022
Smart Water Analytics Network (SWAN) Forum	May 2022
Marketing Science Conference, Simon, Rochester (remote)	June 2021
– Recommendation and Machine Learning, Special Track
U.S. EPA Briefing, Conference Call	July 2019
State of Michigan, Department of Environmental Quality	July/August 2019
Marketing Science Conference, Rome, Italy	June 2019
– Machine Learning in Marketing, Special Track
Ross Talks, Alumni Event, Michigan Ross	May 2019
Smart Water Analytics Network (SWAN) Forum, Keynote, Miami	May 2019
Alumni Board of Governors, Michigan Ross	October 2018
LEAD Summer Institute, Michigan Ross	July 2018
Marketing Science Conference, Philadelphia	June 2018
– Machine Learning in Marketing, Special Track, Co-organizer
American Marketing Association Conference, New Orleans	February 2018
Marketing Science Conference, Los Angeles	June 2017
– Machine Learning in Marketing, Special Track, Co-organizer
Customer Analytics in Retail Marketing, Los Angeles	May 2017
Michigan Student Symposium for Interdisciplinary Statistical Sciences, Keynote	March 2017
Quicken Loans Data Science, MIDAS Collaboration	January 2017
Water @ Michigan Conference	January 2017
Artificial Intelligence Lab, Michigan	October 2016
Customer Analytics in Retail Marketing, New York	October 2016
SPARK Machine Learning Workshop, Ann Arbor	October 2016
Kickstart Computer Science, Ann Arbor	September 2016
Bloomberg Data for Good Exchange (Presenter and Panelist), New York	September 2016
Big Data Summer Institute Symposium, Ann Arbor	July 2016
SPARK Workshop for Startups, Ann Arbor	March 2016
INFORMS Annual Meeting, Philadelphia, session co-organizer	November 2015
Marketing Science Conference, Baltimore	June 2015
Sawtooth Software Conference, Orlando	March 2015
American Marketing Association Conference, San Antonio	February 2015
Joint Statistical Meetings, Montreal, Session Organizer	August 2013
ART Forum, Chicago	June 2013
Capital One, Webinar	June 2013
Marketing Science Conference, Boston	June 2012
Wharton Customer Analytics Initiative, Webinar with Elea Feit	September 2012
Marketing Science Conference, Houston	June 2011
Marketing Science Conference, Cologne	June 2010
Jay H. Baker Retailing Initiative Board Meeting	November 2009
Marketing Science Conference, Ann Arbor	June 2009