Publication:
Consumer attitude metrics for guiding marketing mix decisions

dc.contributor.authorHanssens, D. M.
dc.contributor.authorPauwels, Koen Hendrik
dc.contributor.authorSrinivasan, S.
dc.contributor.authorVanhuele, M.
dc.contributor.authorYildirim, G.
dc.contributor.departmentBusiness Administration
dc.contributor.ozuauthorPAUWELS, Koen Hendrik
dc.date.accessioned2014-12-17T08:39:57Z
dc.date.available2014-12-17T08:39:57Z
dc.date.issued2014-08
dc.description.abstractMarketing managers often use consumer attitude metrics such as awareness, consideration, and preference as performance indicators because they represent their brand's health and are readily connected to marketing activity. However, this does not mean that financially focused executives know how such metrics translate into sales performance, which would allow them to make beneficial marketing mix decisions. We propose four criteria-potential, responsiveness, stickiness, and sales conversion-that determine the connection between marketing actions, attitudinal metrics, and sales outcomes. We test our approach with a rich data set of four-weekly marketing actions, attitude metrics, and sales for several consumer brands in four categories over a seven-year period. The results quantify how marketing actions affect sales performance through their differential impact on attitudinal metrics, as captured by our proposed criteria. We find that marketing-attitude and attitude-sales relationships are predominantly stable over time but differ substantially across brands and product categories. We also establish that combining marketing and attitudinal metrics criteria improves the prediction of brand sales performance, often substantially so. Based on these insights, we provide specific recommendations on improving the marketing mix for different brands, and we validate them in a holdout sample. For managers and researchers alike, our criteria offer a verifiable explanation for differences in marketing elasticities and an actionable connection between marketing and financial performance metrics.en_US
dc.description.versionpre-print
dc.identifier.doi10.1287/mksc.2013.0841
dc.identifier.endpage550
dc.identifier.issn1526-548X
dc.identifier.issue4
dc.identifier.scopus2-s2.0-84905055363
dc.identifier.startpage534
dc.identifier.urihttp://hdl.handle.net/10679/732
dc.identifier.urihttps://doi.org/10.1287/mksc.2013.0841
dc.identifier.volume33
dc.identifier.wos000343120000005
dc.language.isoengen_US
dc.peerreviewedyesen_US
dc.publicationstatuspublisheden_US
dc.publisherInformsen_US
dc.relation.ispartofMarketing Science
dc.relation.publicationcategoryInternational Refereed Journal
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsconsumer attitude metricsen_US
dc.subject.keywordsResponsivenessen_US
dc.subject.keywordsPotentialen_US
dc.subject.keywordsStickinessen_US
dc.subject.keywordsSales conversionen_US
dc.subject.keywordsHierarchical linear modelen_US
dc.subject.keywordsCross-effects modelen_US
dc.subject.keywordsEmpirical generalizationsen_US
dc.subject.keywordsDynamic programming modelen_US
dc.subject.keywordsOptimal marketing resource allocationen_US
dc.titleConsumer attitude metrics for guiding marketing mix decisionsen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isOrgUnitOfPublication3920f480-c8c2-457c-8c42-5e73823c300f
relation.isOrgUnitOfPublication.latestForDiscovery3920f480-c8c2-457c-8c42-5e73823c300f

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