Better predictive models could reduce clothing returns
Adding images to predictive models can help retailers estimate return rates as they decide what to feature on their websites.
Faculty
John R. Hauser is the Kirin Professor of Marketing at the MIT Sloan School of Management where he teaches new product development, marketing management, and statistical and research methodology.
He has served MIT as Head of the MIT Marketing Group, Head of the Management Science Area, Research Director of the Center for Innovation in Product Development, and co-director of the International Center for Research on the Management of Technology.
He is the co-author of two textbooks, Design and Marketing of New Products and Essentials of New Product Management, and three other books. He is a former editor of Marketing Science. He has published over one hundred scientific papers.
Hauser has been the winner or finalist for over twenty best paper awards, and his students have won a variety of best dissertation awards. He has been recognized with the Converse Award for contributions to the science of marketing; the Parlin Award, the oldest award offered by the American Marketing Association for contributions to marketing research; the Buck Weaver Award for lifetime contributions to the theory and practice of marketing science; and the Churchill Lifetime Achievement Award for contributions to marketing research. He received an award from the MIT Sloan School for outstanding teaching in the Master's program.
Hauser has consulted for a variety of corporations on product development, sales forecasting, marketing research, voice of the customer, defensive strategy, and R&D management and has been an expert witness in over seventy-five cases. He is a founder and principal at Applied Marketing Science, Inc.; a fellow of INFORMS; an inaugural fellow of the INFORMS Society of Marketing Science (ISMS); and serves on many editorial boards. He is an affiliate of both the Analysis Group and Cornerstone Research. He has served as an academic trustee of the Marketing Science Institute, a founding officer of ISMS, a Departmental Editor at Management Science, an associate editor at the Journal of Marketing Research.
Hauser holds an SB and an SM in electrical engineering, an SM in civil engineering, and an ScD in operations research, all from MIT. He holds an honorary doctorate from the Erasmus School of Economics.
Befurt, Rene, Felix Eggers, and John R. Hauser (Edward Elgar Publishing, Natalie Mizik and Dominique Hanssens, Eds.). Handbook of Marketing Analytics: Methods and Applications in Marketing, Public Policy & Litigation (2024).
Ibragimov, Marat, Siham El Kihal, and John R. Hauser, MIT Sloan Working Paper 6967-24. Cambridge, MA: MIT Sloan School of Management, 2024. Download Paper.
Alex Burnap, John R. Hauser, and Artem Timoshenko. Marketing Science Vol. 42, No. 6 (2023): 1029-1056.
Daria Dzyabura, Siham El Kihal, John R. Hauser, and Marat Ibragimov. Marketing Science Vol. 42, No. 6 (2023): 1125-1142. Download Paper.
Timoshenko, Artem and John R. Hauser. Marketing Science Vol. 38, No. 1 (2019): 1-20. Download Paper.
Liberali, Gui, Eric Boersma, Hester Lingsma, Jasper Brugts, Diederik Dippel, Jan Tijssen, and John R. Hauser. Journal of Clinical Epidemiology. Forthcoming. Download Paper. Appendix. Visual Summary.
Befurt, Rene, Felix Eggers, and John R. Hauser (Edward Elgar Publishing, Natalie Mizik and Dominique Hanssens, Eds.). Handbook of Marketing Analytics: Methods and Applications in Marketing, Public Policy & Litigation (2024).
Daria Dzyabura, Siham El Kihal, John R. Hauser, and Marat Ibragimov. Marketing Science Vol. 42, No. 6 (2023): 1125-1142. Download Paper.
Alex Burnap, John R. Hauser, and Artem Timoshenko. Marketing Science Vol. 42, No. 6 (2023): 1029-1056.
Hauser, John R. Foundations and Trends in Marketing Vol. 16, No. 1-2 (2022): 116-121. Download Paper.
Proserpio, Davide, John R. Hauser, Tomomichi Amano, Alex Burnap, Tong Guo, Dokiun Lee, Xiao Liu, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. Marketing Letters Vol. 31, No. 4 (2020): 393-404. Download Paper.
Urban, Glen, Artem Timoshenko, Paramveer Dhillon, and John R. Hauser. MIT Sloan Management Review Vol. 61, No. 2 (2020): 71-76. Download Paper.
Hauser, John R., Felix Eggers, and Matthew Selove. Marketing Science Vol. 38, No. 6 (2019): 1059-1081. Appendices.
Dzyabura, Daria and John R. Hauser. Marketing Science Vol. 38, No. 3 (2019): 417-441. Download Paper.
Timoshenko, Artem and John R. Hauser. Marketing Science Vol. 38, No. 1 (2019): 1-20. Download Paper.
Topic: Marketing
Ibragimov, Marat, Siham El Kihal, and John R. Hauser, MIT Sloan Working Paper 6967-24. Cambridge, MA: MIT Sloan School of Management, 2024. Download Paper.
Burnap, Alex, and John R. Hauser, MIT Sloan Working Paper 5815-19. Cambridge, MA: MIT Sloan School of Management, January 2020.
Hauser, John, Chengfeng Mao,and James Li. In Review of Marketing Research, edited by K. Sudhir and Olivier Toubia, 147-167. Leeds, UK: Emerald Group Publishing, 2023. Download Paper.
Liverali, Gui, John R. Hauser, and Glen Urban. In Handbook of Marketing Decision Models, edited by Berend Wierenga and Ralf van der Lans, 531-560. New York, NY: Springer US, 2017.
Hauser, John R. Presentation, INFORMS Doctoral Consortium, University of Southern California, Los Angeles, United States. June 8, 2017.
Hauser, John R. The Conversation, April 19, 2017.
Hauser, John R. Journal of the Academy of Marketing Sciences, January 2017.
Chintagunta, Pradeep, Dominique Hanssens, and John R. Hauser. In The GfK Marketing Intelligence Review, Special Issue on Data Science, Vol. 8, No. 2, 18-23: November 2016.
Hauser, John R. In 103rd Dies Natalis of Erasmus University, Rotterdam, Netherlands: November 2016.
Felix Eggers, John R. Hauser, and Matthew Selove. In Proceedings of the Sawtooth Conference, September 2016, Park City, UT: September 2016.
Artem Timoshenko, and John R. Hauser. In Proceedings of the Sawtooth Conference, September 2016, Park City, UT: September 2016.
John R. Hauser. In Proceedings of the Sawtooth Conference, September 2016, Park City, UT: September 2016.
Adding images to predictive models can help retailers estimate return rates as they decide what to feature on their websites.
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