A look at using listening information as data that is used to predict sales and market share, to plan advertising budgets and media.

1.
Cutting Edges: Listening-Led Marketing Science, Media
Strategies, and Organizations
Stephen D. Rappaport
Journal of Advertising Research
Vol. 50, No. 3, 2010

2.
Title: Cutting Edges: Listening-Led Marketing Science, Media Strategies, and
Organizations
Author(s): Stephen D. Rappaport
Source: Journal of Advertising Research
Issue: Vol. 50, No. 3, 2010
Cutting Edges: Listening-Led Marketing Science, Media
Strategies, and Organizations
Stephen D. Rappaport
Advertising Research Foundation
INTRODUCTION
Listening’s ability to transform raw conversations into quantitative measures expands its business potential in exciting new
ways and goes far beyond listening as “the world’s largest focus group” or latest customer service tool. Listening-derived
measures are being developed, tested, and used to study relationships between listening and brand financial performance.
We begin with a review of studies that link share of conversational voice to share of market and then move on to market share
trends and sales predictions. New rigorous and promising research is starting to explain why market share and sales
correlations exist. Turning attention to media, listening-led innovations are taking root in areas such as predicting television
show viewing, qualifying audiences, media planning, and advertising testing. The studies, projects, and examples are
harbingers of the next wave of listening-driven initiatives.
SHARE OF VOICE
Conversational Share of Voice and Share of Market
Setting the advertising budget is one of the most critical decisions brands make. Set it right and market share can be
protected, or grow and increase brand equity; set it wrong and brands’ holds on their share and value can slip. One
established metric for sizing budgets is the ratio between share of advertising voice and share of market. James O. Peckham’s
The Wheel of Marketing (1981) was one of the earliest studies to draw a direct relationship between a brand’s share of voice
in advertising and its market share. This relationship continues to be actively researched. Recently the Institute of Practitioners
in Advertising (IPA) and Nielsen Analytic Consulting jointly issued “How Share of Voice Wins Market Share,” a systematic
analysis covering hundreds of studies from both IPA and Nielsen sources. The research has refined effects for large and small
brands and leaders and challengers.
Given this long and rich history, it is only natural that researchers started asking whether a similar relationship exists for
conversational share of voice and share of market and whether analyzing naturally occurring conversations with listening tools
Downloaded from warc.com
2

3.
would generate worthwhile data.
Share of Conversational Voice and Share of Market Generally Correlate
The most current literature on conversational share of voice, and its relationship to market share reinforces the marketing-
communications importance of consumer passions in driving brand conversation. Studies from TNS Cymfony and Nielsen
show that the basic relationship between share of voice and share of market holds for conversational share of voice.
TNS Cymfony specifically examined the relationship of conversational share of voice to share of market in the ready-to-eat
cereal category (Figure 1; Nail, 2009).
Nielsen’s Online division looked at share of conversation in blogs, forums, social networks, and micro-blogging platforms for
three retail warehouse clubs—BJ’s, Sam’s Club, and Costco. Their analysis found that share of market paralleled share of
conversational voice (Figure 2; Swedowsky, 2009).
A closer look at the Nielsen data reveals that Costco’s conversational share of voice is greater than its market share. An
“excess share of voice,” as Nielsen calls it, occurs when brand share of voice is greater than market share. The IPA/Nielsen
report concludes that excess share is important in that it delivers growth: For every 10 percent of excess share of voice, share
of market increases by one-half of one percent. However, the effect varies by type of brand.
The payoff is bigger for leading brands; their gains may nearly triple to 1.4 percent. Neither the TNS Cymfony nor the Nielsen
study offers evidence of a benefit, but future research should study the impact of excess conversation share and determine
whether it does create growth, for whom, and to what extent.
CONVERSATION DRIVERS
Downloaded from warc.com
3

4.
Given the early evidence on conversational share of voice and market share, brands stand to gain from having fact-based
guidance for implementing strategies that generate or expand the volume of conversations and may increase share. Customer
passions, engagement, and marketing communications also have been shown to drive conversation.
Customer Passion
Nielsen Online notes that the Costco brand’s disproportionate share of voice (see earlier) resulted from the extraordinary
grass-roots passion exhibited by the Costco community. Costco members think highly of the products offered—most
particularly, its Kirkland private-label brand that, listening analysis showed, meets their expectations for “less-expensive”
goods with quality comparable to national brands. Another piece of Costco-fan evidence: The retailer does not have an official
Facebook page, but its customers have created fan pages (one of which has about 50,000 fans). Costco rival BJ’s Facebook
fan page has only 200 members (Swedowsky, 2009).
Emotional Engagement
A consumer’s feelings about a brand can be detected through conversational listening, behavioral observation, and biometrics.
Innerscope has studied the relationships between emotional engagement and advertising effectiveness through biometrics.
Their research on Super Bowl commercials showed that the impact of engagement “correlated significantly with the number of
times the advertisements were downloaded and viewed online, as well as with the number of times the advertisements were
commented on online in the general population at large.” In other words: the greater the emotional engagement, the more
downloads and buzz—and greater online share of voice (Siefert et al., 2009).
Marketing Communications
A variety of media—among them advertising, programming, publicity, and news—can stimulate brand chatter (Keller and
Liebman, 2009; Nail, 2008).
Advertising weight changes may raise the discussion level in social media. A TNS Cymfony analysis of the Sony and Samsung
flat panel HDTV brands showed that outspending a rival and timing that investment to a key selling period can stimulate online
conversations, change historical share-of-voice patterns, and give brands momentum (Figure 3; Nail and Chapman, 2008).
As more research is conducted, brands will benefit by mapping the similarities and differences of conversational share of
voice-share to the conventional work and by developing a set of principles capable of guiding brand actions for market
leaders, challenger brands, and new entrants—in good economies and bad.
PREDICTING OUTCOMES
Brands want to know whether online chatter can be predictive of future events—in particular, they want to know whether
Downloaded from warc.com
4

5.
digital discussions can offer any insight into sales. Improving foresight contributes to better decision making, better resource
use and, presumably, better business performance and financial results. We look at outcome prediction from two listening
angles: search-based and conversation-based sentiment and influence.
Search-based Prediction
One of the earliest studies on search-based prediction of sales explored the question, can online buzz predict book sales?
Dan Gruhl and his colleagues collected and analyzed about 500,000 sales-rank values for more than 2,000 books and
correlated them with online postings. Their early conclusions were that (1) carefully constructed search queries produced
volumes of postings that predicted sales ranks and (2) spikes in sales ranks could be predicted. These findings helped
establish the value of using buzz for prediction (Gruhl et al., 2005).
Economists at Google are at the forefront of exploring relationships between search trends and such economic indicators as
retail sales, automobile sales, home sales, and visits to travel destinations. One characteristic these different data share is a
time lag in reporting. (For example, monthly auto sales for June are not reported until the second Tuesday of July.) That
number, however, is preliminary; numbers often are revised at least two times. Closing that gap so in-month measures predict
end-of-month results means that brands may have to take actions that would maximize sales or minimize adverse impacts.
Google also sought to see whether it could “predict the present“ by developing and comparing economic models that forecast
short-term results with (and without) Google Trends data. Unlike the official sales statistics, Google Trends data are compiled
weekly, thereby giving timely insights into people’s search conversations. After running its models many times, Google
concluded that models including relevant Google Trends data “tend to outperform” those that do not. In some cases, the gains
were small, but in others (e.g., “Motor Vehicles and Parts” and “New Housing Starts”), the gains were “quite substantial.”
Google also plans to use comparable data to discover “turning points” that predict market changes (Choi and Varian, 2009;
Choi, 2009).
Predicting Sales Using Influence or Sentiment
From advances being made in outcome prediction, evidence is mounting that advanced listening analytics—especially those
incorporating some measure of influence and/or sentiment—provide meaningful guidance in the short term. The following
examples show how.
Onalytica, a full-service listening vendor, looks at the relationships among “share of influence” and brands, and then uses the
trend as a predictor of near-term sales and market share. At Onalytica, influence means attributing the right “weight” to each
online voice in the conversation. For example, if a person with a large following writes about a brand, she or he carries more
weight than a person who does not. Similarly, some publications are more “weighty” than others.
Onalytica applied its share-of-influence metric to predict the sales of two Nissan models, the well-known Pathfinder, and the
new Qashqai. Their data revealed that share of influence and sales are related (the Pearson Product Moment Coefficients
were 0.99 and 0.98, respectively) and that each model exhibited a unique relationship (Figure 4). For Pathfinder, share of
influence led sales by less than one month but, for the Qashqai, the change in sales lagged share of opinion by about one
month.
Downloaded from warc.com
5

6.
Additional listening or quantitative research may be able to explain the reasons why the relationships differ and be used to
direct brand action. For example, the Nissan share-of-influence-to-sales pattern may reflect a number of factors: the maturity of
the models; consumers’ knowledge about the nameplates; the extent of information-gathering needed before purchase. For a
new model such as Qashqai, researching and turning to experts or authorities for guidance makes sense as consumers seek
information, need questions answered, or want to satisfy their curiosity. And those needs explain the longer search frame. For
better-known models, however, such as the Pathfinder, people may just quickly update features, pricing, or owner enjoyment
before purchasing. For brand marketing and advertising, understanding the share of influence and its timing in conversations
can be very helpful to developing marketing and sales strategies.
To help predict sales, full-service listening vendor MotiveQuest created an online measure similar to the Net Promoter Score,
which they call the Online Promoter Score. Their score measures advocacy, which is the number of people online who
recommend a brand. MotiveQuest factors sentiment into the score (MotiveQuest, 2009).
MotiveQuest’s work with the MINI automobile demonstrates the value of listening and communications strategy and the power
of their Online Promoter Score to predict sales. After a successful U.S. introduction, MINI did not have a new product for its
second year—a serious hurdle in a marketplace wherein sales momentum and new-car sales are driven by introductions,
relaunches, and updates. MotiveQuest used listening to learn that MINI owners were very community-oriented and enjoyed
customizing their cars. Furthermore, they often shared photos and met up with other owners. This insight was in stark contrast
to other owners in the competitive set who were more focused on “carness” attributes—performance, handling, and fuel
efficiency, for example. Confident that they had located their owners’ passion points, MINI designed customer-specific
messaging (specifically, targeted mail, personalized digital billboards) and experiences (e.g., online and live events).
As the campaign developed, MotiveQuest tracked the relationship between the Online Promoter Score and sales, discovering
a one-month lag between change in score and change in sales from January 2006 through April 2007 (Figure 5). Additionally,
the scatter plot showed that the two measures were tightly correlated (99.8 percent confidence). At the time, MotiveQuest
observed that sales “increase (or decrease) by 53 percent of the increase (or decrease) of change in the Online Promoter
Score” (2007). Though the percentage change in sales will likely differ across brands, the ratio provides valuable guidance for
marketers planning communications programs.
Downloaded from warc.com
6

7.
Predicting Market Share
In predicting market share using influence or sentiment,a number of listening measures reflect approaches that draw upon
alternative models of influence and how it works.
l Online advocacy and market share: In a comparison between Sony and Samsung flat-panel HDTVs (see previous
discussion), Sony, the long-established brand, enjoys higher brand equity and higher awareness. Yet, in 2007,
challenger Samsung was the market share leader with 23 percent, and Sony trailed by six points at 17 percent.
Jim Nail and Jenny Chapman used TNS Cymfony to analyze the brands’ social-media conversations. In addition to finding that
Samsung took Sony’s share of voice lead away, they discovered that Samsung’s advocate base (people who identified
themselves through comments) greatly outnumbered Sony’s; that they shared their views and passions; and that the volume of
favorable posts was also higher (Figure 6).
Downloaded from warc.com
7

8.
From a predictive standpoint, such listening-derived factors could be periodically incorporated into forecasting models to keep
them in sync with consumer conversations, sentiments, and behaviors.
l Share of influence trends and market share: Onalytica’s Nissan study (see earlier) explored the relationship between its
share-of-influence measure and market share trends. In another market study, Onalytica looked at the buzz around such
major contact-lens brands as Accuvue, Optix, and Soflens over a five-month period.
The main brand, Accuvue, exhibited a steady downward trend in influence: People seemed to be focusing elsewhere and were
less connected to the Accuvue brand, paralleling in a loss of market share. This influence pattern also gave clues to the
effectiveness of the marketing-communications program and competitor spending.
In reviewing the data, Fleming Madsen, Onalytica founder, concluded (Madsen, 2009),
A brand’s share of influence will normally decline when the brand’s market communication is ineffective and/or the market
communication spend of competing brands have increased. Monitoring share-of-influence (and total influence) can,
therefore, also be used to pick up changes in competitor’s market communication spend.
HOW SENTIMENT AFFECTS PREDICTABILITY
These examples shows that vendors differ in how they weigh sentiment as a component of predictability. Most, such as
MotiveQuest, Cymfony, or Nielsen, factor in sentiment, whereas a minority, such as Onalytica, generally omit sentiment from
their models.
Onalytica’s Madsen explains their reasoning:
Some may now be thinking that surely more sales will only be the result if a brand is mentioned in a positive context or is
unreservedly recommended. All things being equal, it is normally better for a brand to be mentioned in a positive context
than in a negative one. But we have to remember that every time a brand is mentioned in a negative context there are two
opposing forces at work. The first force is negative. The reader may be slightly less likely to favour the brand because of the
Downloaded from warc.com
8

9.
negative context. However, because the mention of the brand increases the reader’s familiarity with the brand and brings the
brand to the forefront of the reader’s mind, a positive force is at work, too.
With their differences, however, the data from both approaches statistically correlate strongly to sales and are predictive. The
difference between the two models reinforces the need for brands to understand the concepts underlying their listening
measures and models. Clearly sentiment use in predictive models is an area for deeper exploration, study, and discussion.
Listening-led Media
As the acceptance of listening as a valuable research tool increases, its influence will extend beyond marketing science.
Listening-led media practices are emerging, leading advertisers and their partners to explore new ways of performing the most
fundamental tasks—gauging audience, testing commercials, and targeting communications. Though early, the cases reviewed
further begin to suggest the value of new approaches for accomplishing traditional business tasks.
l Gauging audience: television-viewing intention: TV viewing started moving from rigidly scheduled activity to a more
flexible, on-demand model with the introduction of the consumer VCR in the 1970s. The “watch-it-when-I-want” reality is
now so pervasive that concepts such as “appointment television” need to be defined and illustrated with examples. It is
hard for many younger people to imagine that the world once stopped cold when broadcast television programs such as
“Cosby,” “L.A. Law,” and “Moonlighting” aired (Ziegler, 1988).
TV viewers in the mass-media era had scant choice, and watching was ritualistic. Today, with myriad entertainment options,
media companies and advertisers want to know, “Are people intending to watch this or that program, and how do they feel
about the program?” Answering these questions can help brands decide whether they should adjust or modify storylines or
adjust promotion, marketing, and media strategies to maximize their impacts.
Listening metrics can shed light on intention. Collective Intellect, a full-service listening vendor, has developed a tool to rank all
prime-time television shows. In brief, the company analyzes buzz around each show, extracting viewers’ intent to view, their
affinity to the programming, and their positive and negative associations. They weigh their findings by target demographic,
genre, and airdate. The result is a syndicated “Television Viewing Intention” ranking report (Figure 7).
Downloaded from warc.com
9

10.
The report shows which programs people intend to watch in a current week and the average intention score since the start of
the current season. Weekly snapshots and trend scores offer insight into the strength of the connections between programs
and viewers. That knowledge can be tactical in the short term to match advertising to intended viewership. In the longer term,
the data can be integrated into models to help forecast future ratings.
l Commercial testing: Using social media to predict ad likeability: Commercial testing often includes a measure of ad
likeability associated with several measures used to gauge advertising effectiveness, including recall, attitude changes,
and recommendation (Smit et al., 2006).
TNS Cymfony set out to find whether Ad Likeability could be approximated by social media favorability, a measure of positive
sentiment they calculate using their listening toolset. Cymfony’s analysis took the top 11 brands advertised on the Superbowl
(based on the TNS Likeability Index) and added three other, lower-scoring ones (Table 1). The top 11 brands were NFL.com;
Bridgestone; Coca-Cola; E*Trade, Anheuser-Busch; Fedex; Doritos; Vitamin Water; Tide; Pepsi (tie with T-Mobile); and T-
Mobile (tie with Pepsi). The lower-scoring ads were from Semi-Pro, GoDaddy, and Salesgenie.
The analysis revealed a correlation between the listening-derived Social Media Favorability rank and the traditionally scored
Likeability rank. Commenting on these results, Cymfony concluded: “At least for ad likeability, social media is a good proxy for
ad likeability conducted with a more traditional methodology” (Nail, 2008)—a finding that suggests a potential adjunct to
conventional ad testing.
Downloaded from warc.com
10

11.
Media companies face a conundrum: TV remains a popular mass medium, but audiences for individual programs have become
smaller. In response, media companies have worked hard to focus on the quality of their audiences as opposed to only their
size. With listening analytics, companies are able to understand what their viewers actually say and do about their advertising.
l Target audiences based on their brand conversations: Viewers, listeners, or readers talk about brands, shows,
characters, and advertising. CNN decided to research the brands their viewers talked about offline as a way of gaining
insight into their conversations and to determine whether those brand conversations could be used to target advertising.
Working with Keller Fay, a research company firm that studies offline and online word of mouth, CNN discovered that its
viewers had more daily conversations about a car, Lexus, than their cable network rivals overall (Figure 8). The heaviest
conversers were viewers who tuned in to broadcasts and logged on to their Web site. CNN discovered it attracted a
remarkably concentrated crowd of Lexus-talkers; compared to the total population, they were four times more likely to
chat about the brand.
The data “gives us the ability to see which products and services our viewers are talking about more than the viewers of other
networks and show that to our clients and prospects and demonstrate our value,” said Greg Liebman, senior vice president-ad
sales research at CNN (Keller & Liebman, 2009, quote from Steinberg, 2009).
Listening-led organization design
Most advertisers, agencies, and media companies will admit that they are not properly organized to harness and act on
listening-based insights. Too many treat listening as another way to do a project within a legacy research framework rather
than as a new way of working that requires top-level commitment, new organizational structures, new processes, and
continuity in the listening effort.
One company that has used listening to drive organizational design is Lego. Competition from electronics and Internet games
had endangered the brand. By listening to their forums and “brand-backyard” sources, however, Lego understood they had a
passionate core audience “whose collective wisdom, enthusiasm and judgment—as demonstrated in forum after forum
online—exceeds that of the company itself.”
Management pushed the company “to rethink every aspect of its business and institutionalize consumer participation.”
Eventually, Lego was reorganized into four lines of co-equal authority: community education and direction; administration;
supply-chain management; and sales and marketing. Its example showcases the fact that taking listening seriously demands
Downloaded from warc.com
11

12.
more than bolting it onto an existing department, function, or process.
Similarly, in 2008, IBM’s listening research revealed that its new-product introductions and chairman’s remarks had stimulated
online comments and conversation but that the company lacked a strong voice in those discussions. Using listening as an
enterprise resource, IBM introduced a “Smarter Planet” campaign that focused on integrated offerings of hardware, software,
and services. Synthesizing its earlier listening research, the company concluded that it needed social-media outreach and
engagement; such engagement could make differences in brand perceptions and stimulate online buzz.
IBM’s social-media success was evident in a doubled share of voice, a 16-percent increase in its brand-association scores,
and its success in achieving top-five organic search results. And, with a full-on cross-divisional management commitment, the
changes all happened in one quarter. Notably, projects are funded through a federated system. Projects are owned by internal
clients but supported and coordinated through market insights.
Its next steps are to extend its approach worldwide and to transform the market-insights function by providing such new
deliverables as standardized social-media metrics and taking on new roles, including education and community leadership.
Notably, projects are funded through a federated system. Projects are owned by internal clients but supported and
coordinated through market insights.
Lego and IBM’s listening implementations reflect two different organizational models, decentralized and hybrid, respectively.
Lego’s example puts responsibility squarely in the hands of its four units, whereas IBM’s combines centralized expertise, with
enterprise policies, and local ownership.
Populating a Community of Skilled Listeners
Listening needs listeners—properly skilled, passionate staffing devoted to learning about people. Experienced listeners listen
constantly so that they develop a sense of where consumers are, where they are moving, and how to stay in sync. To that
end, listening requires new research; conceptual, analytic, and communication skills for designing projects; implementing
technology; creating search queries for finding the right information to harvest and process; and analyzing very large data
sets. Successful agencies such as Crispin Porter + Bogusky, for example, have brought on cultural anthropologists,
journalists, and others to explore listening, to encourage consumers to tell stories, to work cross-functionally across the
organization, and develop actionable listening-led strategies and programs.
THE PROMISE OF LISTENING
Before 1880, factory machines did not have their own motors; power came from drive belts that were turned by overhead
shafts—massive, noisy, oily networks of rotating pipes with leather loops hanging down. Machines operated well below that
steely architecture, connecting to the belts for their power. Factories were designed and production organized around the
needs to create a power source—to distribute and deliver the energy to looms, sewing machines, and other pieces of
industrial machinery.
From 1880 to 1930, the electric-power generation enabled the independent operation of machines—a great step forward
because it, coincidentally, forced managers to rethink their businesses, thereby gaining overall efficiency. Though cost
reduction was a benefit, it was not an overriding goal; over the next 30 years, businesses brought about “numerous
innovations in factory design and more flexible methods of production” that enabled them to meet market needs in better ways
(Devine, 1983).
Downloaded from warc.com
12

13.
Substitute “digital infrastructure” for “electrification,” “traditional research” for “shafts and belts,” and “listening” for “numerous
innovations in design and production”: The lesson—and the promise of listening—become clear.
Listening does offer the promise of new, actionable insights for building brands. Yet, listening also risks being compromised as
a research function. Change never comes easily. Transformative change means that businesses must be redesigned, just as
the factories of the nineteenth century had to start all over. For listening to avoid that fate, twenty-first-century managers must
anticipate its transformative impact and to remember that, in essence, it is still research, with the demands on integrity and
accuracy that the profession demands.
Stephen D. Rappaport, Knowledge Solutions Director at the Advertising Research Foundation (ARF), is responsible for
creating the knowledge resources, tools, and services that help members build brands. The Knowledge Solutions group
organizes and synthesizes the rich contemporary knowledge generated through ARF activities along with archival sources and
trusted third parties and then makes them available through a family of self-service sources, such as the ARF’s industry-
leading PowerSearch database, Morning Coffee newsreader, and assisted research and consulting services available through
the ARF Knowledge Center. Rappaport also is the lead author of the best-selling The Online Advertising Playbook: Proven
Strategies and Tested Tactics from the Advertising Research Foundation. E-Mail: srappaport@thearf.org.
References
Choi, H. “Predicting the Present: Using Google to Measure Economic Activity.” Presentation to ARF Industry Leader Forum,
November 3, 2009.
Choi, H., and H. Varian (2009) “Predicting the Present With Google Trends.” Google. Retrieved Noverber 4, 2009 from [URL:
http://googleresearch.blogspot.com/2009/04/predicting-present-with-google-trends.html].
Devine, W. D. Jr. “From Shafts to Wires: Historical Perspective on Electrification.” The Journal of Economic History 43(2),
(1983): 347–372.
Gruhl, D., R. Guha, R. Kumar, J. Novak, and A. Tomkins (2005) “The Predictive Power of Online Chatter.” Proceedings of the
eleventh ACM SIGKDD international conference on knowledge discovery in data mining. Retrieved October 5, 2009 from
[URL: www.tomkinshome.com/site_media/papers/papers/GGK+05.pdf] .
Keller, E., and G. Liebman. “The Marketing Value of Influencers.” Presented at ARF Audience Measurement 4.0. June 23–24,
2009.
Madsen, F. (2008) “Predicting Sales from Online Buzz.” Message posted January 27 on [URL:
http://www.onalytica.com/blog/2008/01/predicting-sales-from-online-buzz.html] .
MotiveQuest. (2009) “The Online Promoter Score.” Retrieved October 26, 2009, from [URL:
http://www.motivequest.com/main.taf?p=6].
Nail, J. “Effective PR and Word of Mouth Strategies to Maximize a Brand’s Investment in a Super Bowl Ad.” ARF Webinar
presentation, November 12, 2008.
Nail, J. “Social Media Analysis: Finding the Path to New Insights.” TNS Cymfony Webinar, May 28, 2009. Available on
Downloaded from warc.com
13

14.
request: E-Mail jar@thearf.org.
Nail, J., and J. Chapman. “Social Media Analysis for Consumer Insight: Validating and Enhancing Traditional Market Research
Findings.” TNS Cymfony ARF Webinar, September 9, 2008. Available at [URL:
http://www.cymfony.com/modules/knowledgeBaseRegister.aspx?dl=51].
Peckham, James O. The Wheel of Marketing. Scarsdale, NY: Privately published, 1981.
Siefert, C. J, R. Kothuri, D. B. Jacobs, B. Levine, J. Plummer, and C. D. Marci. “Winning the Super ‘Buzz’ Bowl: How
Biometrically-Based Emotional Engagement Correlates with Online Views and Comments for Super Bowl Advertisements.”
Journal of Advertising Research 49,3 (2009): 293–303.
Smit, E. G., L. Van Meurs, and P. C. Neijens. “Effects of Advertising Likeability: A 10-Year Perspective.” Journal of Advertising
Research 46, 1 (2006): 73–83.
Steinberg, B. “Could TV Be Bought and Sold Based on Who’s Talking about It? ESPN and CNN Think So.” Advertising Age,
July 27, 2009. Retrieved July 13, 2010 from [URL: http://adage.com/mediaworks/article?article_id=138128].
Swedowsky, Maya. (2009) “A Social Media How-to For Retailers.” Retrieved September 22, 2009, from [URL: http://en-
us.nielsen.com/main/insights/consumer_insight/september_2009/asocialmediahowtoforretailers].
Ziegler, Peggy “Where Have All the Viewers Gone?” Los Angeles Times, May 1, 1988.
< body>
© Copyright Advertising Research Foundation 2010
Advertising Research Foundation
432 Park Avenue South, 6th Floor, New York, NY 10016
Tel: +1 (212) 751-5656, Fax: +1 (212) 319-5265
www.warc.com
All rights reserved including database rights. This electronic file is for the personal use of authorised users based at the subscribing company's office location. It may not be reproduced, posted on intranets, extranets
or the internet, e-mailed, archived or shared electronically either within the purchaser’s organisation or externally without express written permission from Warc.
Downloaded from warc.com
14