A Model for Inferring Market Preferences from Online Retail Product Information Matrices

Gilbride, TJ; Currim, IS; Mintz, O; Siddarth, S

A Model for Inferring Market Preferences from Online Retail Product Information Matrices

Gilbride, TJ Currim, IS Mintz, O

Siddarth, S

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Publication Type:: Journal Article
Citation:: Journal of Retailing, 2016, 92 (4), pp. 470 - 485
Issue Date:: 2016-12-01

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Field	Value	Language
dc.contributor.author	Gilbride, TJ	en_US
dc.contributor.author	Currim, IS	en_US
dc.contributor.author	Mintz, O https://orcid.org/0000-0003-0512-4283	en_US
dc.contributor.author	Siddarth, S	en_US
dc.date.available	2020-05-25T19:04:31Z
dc.date.issued	2016-12-01	en_US
dc.identifier.citation	Journal of Retailing, 2016, 92 (4), pp. 470 - 485	en_US
dc.identifier.issn	0022-4359	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136068
dc.description.abstract	© 2016 New York University This research extends information display board methods, currently employed to study information processing patterns in laboratory settings, to a field based setting that also yields managerially useful estimates of market preferences. A new model is proposed based on statistical, behavioral, and economic theories, which integrates three decisions consumers must make in this context: which product-attribute to inspect next, when to stop processing, and which, if any, product to purchase. Several theoretical options are considered on how to model product attribute selection and how to treat uninspected attributes. The modeling options are empirically tested employing datasets collected at a popular e-tailer's website, while customers were making product evaluation and purchase decisions. Subsequent to identifying the best model, we show how the resulting attribute preference estimates can be managerially employed to improve customer targeting of abandoned shopping carts for follow up communications aimed at improving sales conversions.	en_US
dc.relation.ispartof	Journal of Retailing	en_US
dc.relation.isbasedon	10.1016/j.jretai.2016.07.002	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Marketing	en_US
dc.title	A Model for Inferring Market Preferences from Online Retail Product Information Matrices	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	92	en_US
utslib.for	1505 Marketing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Marketing Discipline
utslib.copyright.status	open_access	*
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	92	en_US

Abstract:

© 2016 New York University This research extends information display board methods, currently employed to study information processing patterns in laboratory settings, to a field based setting that also yields managerially useful estimates of market preferences. A new model is proposed based on statistical, behavioral, and economic theories, which integrates three decisions consumers must make in this context: which product-attribute to inspect next, when to stop processing, and which, if any, product to purchase. Several theoretical options are considered on how to model product attribute selection and how to treat uninspected attributes. The modeling options are empirically tested employing datasets collected at a popular e-tailer's website, while customers were making product evaluation and purchase decisions. Subsequent to identifying the best model, we show how the resulting attribute preference estimates can be managerially employed to improve customer targeting of abandoned shopping carts for follow up communications aimed at improving sales conversions.

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http://hdl.handle.net/10453/136068