Tracking the impact of media on voter choice in real time: A Bayesian dynamic joint model

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dc.contributor.authorPareek, Bhuvanesh
dc.contributor.authorGhosh, Pulak
dc.contributor.authorWilson, Hugh N.
dc.contributor.authorMacdonald, Emma K.
dc.contributor.authorBaines, Paul R.
dc.date.accessioned2018-01-23T17:01:38Z
dc.date.available2018-01-23T17:01:38Z
dc.date.issued2018-01-15
dc.description.abstractCommonly used methods of evaluating the impact of marketing communications during political elections struggle to account for respondents' exposures to these communications due to the problems associated with recall bias. In addition, they completely fail to account for the impact of mediated or earned communications such as newspaper articles or television news, that are typically not within the control of the advertising party, nor are they effectively able to monitor consumers' perceptual responses over time. This study based on a new data collection technique using cell-phone text messaging (called real-time experience tracking or RET) offers the potential to address these weaknesses. We propose an RET-based model of the impact of communications and apply it to a unique choice situation: voting behavior during the 2010 UK general election, which was dominated by three political parties. We develop a Bayesian zero-inflated dynamic multinomial choice model that enables the joint modeling of: the inter-play and dynamics associated with the individual voter's choice intentions over time, actual vote, and the heterogeneity in the exposure to marketing communications over time. Results reveal the differential impact over time of paid and earned media, demonstrate a synergy between the two, and show the particular importance of exposure valence and not just frequency, contrary to the predominant practitioner emphasis on share-of-voice metrics. Results also suggest that while earned media have a reducing impact on voting intentions as the final choice approaches, their valence continues to influence the final vote: a difference between drivers of intentions and behavior that implies that exposure valence remains critically important close to the final brand choice.en_UK
dc.identifier.citationPareek B, Ghosh P, Wilson HN, Macdonald EK, Baines P, Tracking the impact of media on voter choice in real time: A Bayesian dynamic joint model, Journal of the American Statistical Association, 2018, Volume 113, Issue 524, pp. 1457-1475en_UK
dc.identifier.issn0162-1459
dc.identifier.urihttp://dx.doi.org/10.1080/01621459.2017.1419134
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/12927
dc.language.isoenen_UK
dc.publisherTaylor & Francisen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectAdvertisingen_UK
dc.subjectBayesian Methodsen_UK
dc.subjectCommunications - paid and earneden_UK
dc.subjectReal-time experience trackingen_UK
dc.subjectZero-inflationen_UK
dc.subjectMultinomial Logit Modelen_UK
dc.subjectVoters' Choiceen_UK
dc.titleTracking the impact of media on voter choice in real time: A Bayesian dynamic joint modelen_UK
dc.typeArticleen_UK

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