How can machine learning aid behavioral marketing research?

Linda Hagen (), Kosuke Uetake (), Nathan Yang (), Bryan Bollinger (), Allison J. B. Chaney (), Daria Dzyabura, Jordan Etkin (), Avi Goldfarb, Liu Liu (), K. Sudhir (), Yanwen Wang (), James R. Wright () and Ying Zhu ()
Additional contact information
Linda Hagen: University of Southern California
Kosuke Uetake: Yale University
Nathan Yang: Cornell University
Bryan Bollinger: New York University
Allison J. B. Chaney: Duke University
Jordan Etkin: Duke University
Liu Liu: University of Colorado Boulder
K. Sudhir: Yale University
Yanwen Wang: University of British Columbia
James R. Wright: University of Alberta
Ying Zhu: University of California San Diego

Marketing Letters, 2020, vol. 31, issue 4, No 7, 370 pages

Abstract: Abstract Behavioral science and machine learning have rapidly progressed in recent years. As there is growing interest among behavioral scholars to leverage machine learning, we present strategies for how these methods that can be of value to behavioral scientists using examples centered on behavioral research.

Keywords: Behavioral science; Big data; Semi-supervised learning; Supervised learning; Unsupervised learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11002-020-09535-7

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