Tracking the Impact of Media on Voter Choice in Real
Time: A Bayesian Dynamic Joint Model
Bhuvanesh Pareek, Pulak Ghosh,
Hugh N. Wilson, Emma K. Macdonald, and Paul Baines
September 9, 2017
Bhuvanesh Pareek is Assistant Professor, Department of Operations Management and Quantitative Techniques
at Indian Institute of Management, Indore, India; Pulak Ghosh is Professor, Department of Decision Sciences and
Information Systems, Indian Institute of Management, Bangalore, India; Hugh N. Wilson, Emma K. Macdonald and
Paul Baines are Professors of Marketing at Cranfield School of Management, Cranfield University, UK; Big thanks to
Prof. Rajiv Sinha who has been very instrumental in this work, although he is no more with us, his contribution will
be cherished.
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Journal of the American Statistical Association, Available online December 15 January 2018
DOI:10.1080/01621459.2017.1419134
Published by Taylor & Francis. This is the Author Accepted Manuscript issued with: Creative Commons Attribution Non-Commercial License (CC:BY:NC 4.0).
The final published version (version of record) is available online at DOI:10.1080/01621459.2017.1419134 Please refer to any applicable publisher terms of use.
Abstract
Commonly 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.
Key Words: Advertising; Bayesian Methods; Communications - paid and earned; real-time
experience tracking; Zero-inflation; Multinomial Logit Model; Voters’ Choice;
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1 Introduction
Recent advances in information technology, along with the advent of social media and enablement
of user-generated content, have resulted in a proliferation of consumer data. Additionally, the wi-
despread use of mobile devices by consumers makes it feasible to collect data about influences on
consumer choice in real time. This presents an opportunity for researchers wanting to model the
effects of communication on consumer choice, such as organizations working to better understand
the voter’s choice during an election campaign. However both the volume of consumer data being
generated and its real-time nature necessitates the use of new and original analytical tools. This
creates fresh opportunities for informed managerial decision-making, particularly for firms wor-
king to better understand the voter’s choice during an election campaign. This paper addresses
two substantive issues concerning the impact of marketing communications on voting choice: the
differential effectiveness of alternative marketing communications, and the relative importance of
communications frequency on the one hand and the valence of the consumer’s perceptual response
on the other. Global spend on advertising in 2017 was $583.91bn(eMarketer 2017). Communicati-
ons evaluation is a substantial business activity in its own right, providing around 15% of the trade
for the $68bn market research industry(ESOMAR 2016). Many firms also work hard to generate
positive communications that are mediated by third parties such as journalists and retailers. Much
of this effort aims to influence the consumer’s choice of brand, whether it be consumer products
and services, business-to-business suppliers, non-profit organizations, or political candidates and
parties; the latter being the context for this study. Given the massive expense involved and their
potential impact on consumer choice, evaluation and optimization of the effectiveness of commu-
nications is critical. While there have been several studies analysing election data (Linzer 2013,
Christensen and Florence 2008, Gelman et al. 2008), to our knowledge no study has examined
the relative influence of multiple paid and earned media communications on a voter’s choice of a
political party during an election period.
This study makes use of a new and innovative data collection procedure that permits individu-
als to report on communications from competing political parties as soon as they encounter them,
thereby alleviating the recall bias associated with traditional research techniques. Importantly,
we distinguish between exposure to communications that are controlled and paid for by a firm or
party (‘paid’ media such as advertising, newspaper inserts, and billboards) and those that are not
controlled by them (‘earned’ media such as news, editorials, communications about a particular
brand by rival brands, etc.). These are referred to as paid and earned communications respectively.
Although these are important modes of communication, finding the impact of these communicati-
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ons on voters’ choice has four key challenges. The first is the typical problem with survey based
data. Although surveys can explore voters’ attitudes towards a political party and can often, but
not always, be quite helpful in forecasting election results (Linzer 2013), most respondents find
it difficult to recall their exposure to communications accurately. One can examine the impact of
media spend on party choice, but only rarely are data available on the impact of earned communi-
cations such as media coverage (which is not controlled by political parties), and it is often difficult
to disentangle the effects of the medium from the message. The second problem is in assessing
the differential impact of various communications that are controlled by the brands. Models based
on media spend are extensively used by media planners to examine the relative impact of different
paid media such as TV, radio, and billboards. Although the impact of some earned media such
as newspaper editorials has been similarly assessed, this stream of work generally uses entirely
separate data sources and models (Goh et al. 2011, Stephen and Galak 2012). However, marketing
directors and political strategists need to allocate resources between paid media on the one hand
and PR and service improvements focused on influencing earned media, on the other. Third, in
assessing communications impact, researchers frequently ignore the crucial role of the consumer’s
perceptual response to the communication. Theoretical and experimental work confirms that atti-
tude towards communications influences attitude towards the brand (Bri
˜
nol et al. 2004), but most
models of field data, particularly in the case of paid media, focus purely on communications ex-
posure (Verhoef 2003) or the proxy of media spend (Naik and Peters 2009), presumably because
perceptual response data are frequently unavailable. Fourth, practitioners have an interest in un-
derstanding how the impact of communications on voters’ intentions evolve over time and prior to
the final brand/party choice. In many consumer choice contexts, particularly in a voting scenario,
consumers have extended purchase journeys, within which the timing of communications may be
important. Furthermore, communications such as advertising are often intended to have medium-
term impacts via attitude change as well as more immediate behavioral impacts; similarly, PR may
deliberately or accidentally have lasting effects. Traditionally, a key area of interest has been how
to model shifts in voting intention or preference over time and how these shifts impact on subse-
quent choice decisions. However, what has not been researched, up to now, is why these preference
changes have occurred and what role paid and earned communications play in that attitude change
process.
In this article, we explore the potential of a real-time experience tracking (RET) technique in
addressing many of the aforementioned weaknesses. RET has recently been adopted for commu-
nications evaluation by a number of multinationals such as Unilever, BSkyB, PepsiCo, HP, Ener-
gizer, InterContinental Hotels and 20th Century Fox, among others (see Macdonald et al. 2012).
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The genesis of this technique may be traced to the need for capturing the most essential aspects
of consumer encounters with brand communications in real time and in a quick and easy response
format. This research method involves asking a panel of trained respondents to send a cell phone
text (SMS) message as soon as they encounter any of a set of competitive brands during the study.
The respondents, who receive prior training about sending these brief, very structured messages,
provide information on three essential aspects of their encounter: the brand name, the commu-
nications type (for example, TV advertisement or press editorial), and their perceptual response
to the encounter. Respondents also complete a brand attitude survey at the start and end of the
study. RET can capture the impact of a wide range of communications on an individual in real
time; however, no model of brand choice is currently available to exploit the novelty of these kind
of data. We use the RET method to collect data on choice of political parties in the UK’s 2010
General Election, with a tracking period - the length of time for which each participant is asked
to report their communications exposure-of the complete four-week election period. In addition to
reporting all party (brand) encounters throughout the month by text message, we asked participants
to provide their initial voting intentions, and to provide weekly updates on their voting intentions,
via weekly surveys. For modeling purposes, this context has three benefits: the final party choice
is made simultaneously by all participants, thereby alleviating problems of data censoring; the
2010 UK election campaign spanned a four-week period from the announcement of the election to
the actual vote, allowing us to track intentions for the entire choice cycle for all participants; and
it enables the capture of paid and earned communications on the choice of political party for an
individual in a way that many existing research techniques cannot.
Consequently, we propose a RET-based model to capture the impact of these unique, real-time
encounters on the choice of political party. Our model incorporates both paid and earned commu-
nications, and takes into account both the frequency of exposures and their valence, a measure of
the voter’s perceptual response to the communication. The ability to track, differentiate and model
these two effects is one of the most important contributions of this research. We present a Bay-
esian Zero-Inflated dynamic multinomial choice model for simultaneously analyzing four critical
factors that are associated with the impact of communications on voters’ choice: (a) voter’s choice
intentions and their evolution over time (b) voters’ final choice (c) the frequency and valence of
paid and earned media, and (d) heterogeneity in the exposures to different communications over
time.
In summary, this article contributes to the statistical literature on voter choice theory and, more
broadly, on consumer choice in four ways: (1) it unpacks the differential impact of paid and earned
communications during a particular purchase window, thereby allowing firms to optimize their re-
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