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dc.contributor.author
De Bruyn, Arnaud
dc.contributor.author
Viswanathan, Vijay
dc.contributor.author
Beh, Yean Shan
dc.contributor.author
Brock, Jürgen K.-U.
dc.contributor.author
von Wangenheim, Florian
dc.date.accessioned
2021-01-11T12:24:18Z
dc.date.available
2021-01-11T12:24:18Z
dc.date.issued
2020-08
dc.identifier.issn
1094-9968
dc.identifier.issn
1520-6653
dc.identifier.other
10.1016/j.intmar.2020.04.007
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/461229
dc.description.abstract
This article discusses the pitfalls and opportunities of AI in marketing through the lenses of knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order learning” that distinguishes AI applications from traditional modeling approaches, and while focusing on recent advances in deep neural networks, we cover its underlying methodologies (multilayer perceptron, convolutional, and recurrent neural networks) and learning paradigms (supervised, unsupervised, and reinforcement learning). Second, we discuss the technological pitfalls and dangers marketing managers need to be aware of when implementing AI in their organizations, including the concepts of badly defined objective functions, unsafe or unrealistic learning environments, biased AI, explainable AI, and controllable AI. Third, AI will have a deep impact on predictive tasks that can be automated and require little explainability, we predict that AI will fall short of its promises in many marketing domains if we do not solve the challenges of tacit knowledge transfer between AI models and marketing organizations. © 2020 Direct Marketing Educational Foundation.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.title
Artificial Intelligence and Marketing: Pitfalls and Opportunities
en_US
dc.type
Journal Article
dc.date.published
2020-06-28
ethz.journal.title
Journal of Interactive Marketing
ethz.journal.volume
51
en_US
ethz.journal.abbreviated
J. interact. market
ethz.pages.start
91
en_US
ethz.pages.end
105
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Amsterdam
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03995 - von Wangenheim, Florian / von Wangenheim, Florian
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03995 - von Wangenheim, Florian / von Wangenheim, Florian
en_US
ethz.date.deposited
2020-08-27T03:12:53Z
ethz.source
FORM
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-01-11T12:24:28Z
ethz.rosetta.lastUpdated
2022-03-29T04:46:52Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/461126
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/432827
ethz.COinS
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