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Social media listening: The cutting edges

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

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

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  • 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 FoundationINTRODUCTIONListening’s ability to transform raw conversations into quantitative measures expands its business potential in exciting newways and goes far beyond listening as “the world’s largest focus group” or latest customer service tool. Listening-derivedmeasures 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 sharetrends and sales predictions. New rigorous and promising research is starting to explain why market share and salescorrelations exist. Turning attention to media, listening-led innovations are taking root in areas such as predicting televisionshow viewing, qualifying audiences, media planning, and advertising testing. The studies, projects, and examples areharbingers of the next wave of listening-driven initiatives.SHARE OF VOICEConversational Share of Voice and Share of MarketSetting the advertising budget is one of the most critical decisions brands make. Set it right and market share can beprotected, or grow and increase brand equity; set it wrong and brands’ holds on their share and value can slip. Oneestablished metric for sizing budgets is the ratio between share of advertising voice and share of market. James O. Peckham’sThe Wheel of Marketing (1981) was one of the earliest studies to draw a direct relationship between a brand’s share of voicein advertising and its market share. This relationship continues to be actively researched. Recently the Institute of Practitionersin Advertising (IPA) and Nielsen Analytic Consulting jointly issued “How Share of Voice Wins Market Share,” a systematicanalysis covering hundreds of studies from both IPA and Nielsen sources. The research has refined effects for large and smallbrands and leaders and challengers.Given this long and rich history, it is only natural that researchers started asking whether a similar relationship exists forconversational share of voice and share of market and whether analyzing naturally occurring conversations with listening tools Downloaded from warc.com2   
  • 3.  would generate worthwhile data.Share of Conversational Voice and Share of Market Generally CorrelateThe 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 Nielsenshow 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-eatcereal category (Figure 1; Nail, 2009).Nielsen’s Online division looked at share of conversation in blogs, forums, social networks, and micro-blogging platforms forthree retail warehouse clubs—BJ’s, Sam’s Club, and Costco. Their analysis found that share of market paralleled share ofconversational 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/Nielsenreport concludes that excess share is important in that it delivers growth: For every 10 percent of excess share of voice, shareof 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 Nielsenstudy offers evidence of a benefit, but future research should study the impact of excess conversation share and determinewhether it does create growth, for whom, and to what extent.CONVERSATION DRIVERS Downloaded from warc.com3   
  • 4.  Given the early evidence on conversational share of voice and market share, brands stand to gain from having fact-basedguidance for implementing strategies that generate or expand the volume of conversations and may increase share. Customerpassions, engagement, and marketing communications also have been shown to drive conversation.Customer PassionNielsen Online notes that the Costco brand’s disproportionate share of voice (see earlier) resulted from the extraordinarygrass-roots passion exhibited by the Costco community. Costco members think highly of the products offered—mostparticularly, 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 officialFacebook page, but its customers have created fan pages (one of which has about 50,000 fans). Costco rival BJ’s Facebookfan page has only 200 members (Swedowsky, 2009).Emotional EngagementA 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 oftimes the advertisements were downloaded and viewed online, as well as with the number of times the advertisements werecommented on online in the general population at large.” In other words: the greater the emotional engagement, the moredownloads and buzz—and greater online share of voice (Siefert et al., 2009).Marketing CommunicationsA variety of media—among them advertising, programming, publicity, and news—can stimulate brand chatter (Keller andLiebman, 2009; Nail, 2008).Advertising weight changes may raise the discussion level in social media. A TNS Cymfony analysis of the Sony and Samsungflat panel HDTV brands showed that outspending a rival and timing that investment to a key selling period can stimulate onlineconversations, 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 ofvoice-share to the conventional work and by developing a set of principles capable of guiding brand actions for marketleaders, challenger brands, and new entrants—in good economies and bad.PREDICTING OUTCOMESBrands want to know whether online chatter can be predictive of future events—in particular, they want to know whether Downloaded from warc.com4   
  • 5.  digital discussions can offer any insight into sales. Improving foresight contributes to better decision making, better resourceuse and, presumably, better business performance and financial results. We look at outcome prediction from two listeningangles: search-based and conversation-based sentiment and influence.Search-based PredictionOne 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 andcorrelated them with online postings. Their early conclusions were that (1) carefully constructed search queries producedvolumes of postings that predicted sales ranks and (2) spikes in sales ranks could be predicted. These findings helpedestablish 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 asretail sales, automobile sales, home sales, and visits to travel destinations. One characteristic these different data share is atime lag in reporting. (For example, monthly auto sales for June are not reported until the second Tuesday of July.) Thatnumber, however, is preliminary; numbers often are revised at least two times. Closing that gap so in-month measures predictend-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 forecastshort-term results with (and without) Google Trends data. Unlike the official sales statistics, Google Trends data are compiledweekly, thereby giving timely insights into people’s search conversations. After running its models many times, Googleconcluded that models including relevant Google Trends data “tend to outperform” those that do not. In some cases, the gainswere 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 SentimentFrom advances being made in outcome prediction, evidence is mounting that advanced listening analytics—especially thoseincorporating some measure of influence and/or sentiment—provide meaningful guidance in the short term. The followingexamples show how.Onalytica, a full-service listening vendor, looks at the relationships among “share of influence” and brands, and then uses thetrend as a predictor of near-term sales and market share. At Onalytica, influence means attributing the right “weight” to eachonline voice in the conversation. For example, if a person with a large following writes about a brand, she or he carries moreweight 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 thenew Qashqai. Their data revealed that share of influence and sales are related (the Pearson Product Moment Coefficientswere 0.99 and 0.98, respectively) and that each model exhibited a unique relationship (Figure 4). For Pathfinder, share ofinfluence led sales by less than one month but, for the Qashqai, the change in sales lagged share of opinion by about onemonth. Downloaded from warc.com5   
  • 6.  Additional listening or quantitative research may be able to explain the reasons why the relationships differ and be used todirect brand action. For example, the Nissan share-of-influence-to-sales pattern may reflect a number of factors: the maturity ofthe models; consumers’ knowledge about the nameplates; the extent of information-gathering needed before purchase. For anew model such as Qashqai, researching and turning to experts or authorities for guidance makes sense as consumers seekinformation, need questions answered, or want to satisfy their curiosity. And those needs explain the longer search frame. Forbetter-known models, however, such as the Pathfinder, people may just quickly update features, pricing, or owner enjoymentbefore purchasing. For brand marketing and advertising, understanding the share of influence and its timing in conversationscan 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 whorecommend 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 powerof their Online Promoter Score to predict sales. After a successful U.S. introduction, MINI did not have a new product for itssecond 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 enjoyedcustomizing their cars. Furthermore, they often shared photos and met up with other owners. This insight was in stark contrastto other owners in the competitive set who were more focused on “carness” attributes—performance, handling, and fuelefficiency, for example. Confident that they had located their owners’ passion points, MINI designed customer-specificmessaging (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, discoveringa 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, MotiveQuestobserved that sales “increase (or decrease) by 53 percent of the increase (or decrease) of change in the Online PromoterScore” (2007). Though the percentage change in sales will likely differ across brands, the ratio provides valuable guidance formarketers planning communications programs. Downloaded from warc.com6   
  • 7.  Predicting Market ShareIn predicting market share using influence or sentiment,a number of listening measures reflect approaches that draw uponalternative 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 thatSamsung took Sony’s share of voice lead away, they discovered that Samsung’s advocate base (people who identifiedthemselves through comments) greatly outnumbered Sony’s; that they shared their views and passions; and that the volume offavorable posts was also higher (Figure 6). Downloaded from warc.com7   
  • 8.  From a predictive standpoint, such listening-derived factors could be periodically incorporated into forecasting models to keepthem 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 wereless connected to the Accuvue brand, paralleling in a loss of market share. This influence pattern also gave clues to theeffectiveness 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 marketcommunication 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 PREDICTABILITYThese examples shows that vendors differ in how they weigh sentiment as a component of predictability. Most, such asMotiveQuest, Cymfony, or Nielsen, factor in sentiment, whereas a minority, such as Onalytica, generally omit sentiment fromtheir 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 isunreservedly recommended. All things being equal, it is normally better for a brand to be mentioned in a positive contextthan in a negative one. But we have to remember that every time a brand is mentioned in a negative context there are twoopposing 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.com8   
  • 9.  negative context. However, because the mention of the brand increases the reader’s familiarity with the brand and brings thebrand 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. Thedifference between the two models reinforces the need for brands to understand the concepts underlying their listeningmeasures and models. Clearly sentiment use in predictive models is an area for deeper exploration, study, and discussion.Listening-led MediaAs 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 mostfundamental tasks—gauging audience, testing commercials, and targeting communications. Though early, the cases reviewedfurther 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 feelabout the program?” Answering these questions can help brands decide whether they should adjust or modify storylines oradjust 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 allprime-time television shows. In brief, the company analyzes buzz around each show, extracting viewers’ intent to view, theiraffinity 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.com9   
  • 10.  The report shows which programs people intend to watch in a current week and the average intention score since the start ofthe current season. Weekly snapshots and trend scores offer insight into the strength of the connections between programsand 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 positivesentiment 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 scoredLikeability rank. Commenting on these results, Cymfony concluded: “At least for ad likeability, social media is a good proxy forad likeability conducted with a more traditional methodology” (Nail, 2008)—a finding that suggests a potential adjunct toconventional ad testing. Downloaded from warc.com10   
  • 11.  Media companies face a conundrum: TV remains a popular mass medium, but audiences for individual programs have becomesmaller. In response, media companies have worked hard to focus on the quality of their audiences as opposed to only theirsize. 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 othernetworks and show that to our clients and prospects and demonstrate our value,” said Greg Liebman, senior vice president-adsales research at CNN (Keller & Liebman, 2009, quote from Steinberg, 2009).Listening-led organization designMost advertisers, agencies, and media companies will admit that they are not properly organized to harness and act onlistening-based insights. Too many treat listening as another way to do a project within a legacy research framework ratherthan as a new way of working that requires top-level commitment, new organizational structures, new processes, andcontinuity in the listening effort.One company that has used listening to drive organizational design is Lego. Competition from electronics and Internet gameshad endangered the brand. By listening to their forums and “brand-backyard” sources, however, Lego understood they had apassionate core audience “whose collective wisdom, enthusiasm and judgment—as demonstrated in forum after forumonline—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.com11   
  • 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 stimulatedonline comments and conversation but that the company lacked a strong voice in those discussions. Using listening as anenterprise 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 andengagement; 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, thechanges all happened in one quarter. Notably, projects are funded through a federated system. Projects are owned by internalclients 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 newdeliverables 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 andcoordinated 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, withenterprise policies, and local ownership.Populating a Community of Skilled ListenersListening needs listeners—properly skilled, passionate staffing devoted to learning about people. Experienced listeners listenconstantly so that they develop a sense of where consumers are, where they are moving, and how to stay in sync. To thatend, listening requires new research; conceptual, analytic, and communication skills for designing projects; implementingtechnology; creating search queries for finding the right information to harvest and process; and analyzing very large datasets. 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 theorganization, and develop actionable listening-led strategies and programs.THE PROMISE OF LISTENINGBefore 1880, factory machines did not have their own motors; power came from drive belts that were turned by overheadshafts—massive, noisy, oily networks of rotating pipes with leather loops hanging down. Machines operated well below thatsteely architecture, connecting to the belts for their power. Factories were designed and production organized around theneeds to create a power source—to distribute and deliver the energy to looms, sewing machines, and other pieces ofindustrial machinery.From 1880 to 1930, the electric-power generation enabled the independent operation of machines—a great step forwardbecause it, coincidentally, forced managers to rethink their businesses, thereby gaining overall efficiency. Though costreduction was a benefit, it was not an overriding goal; over the next 30 years, businesses brought about “numerousinnovations in factory design and more flexible methods of production” that enabled them to meet market needs in better ways(Devine, 1983). Downloaded from warc.com12   
  • 13.  Substitute “digital infrastructure” for “electrification,” “traditional research” for “shafts and belts,” and “listening” for “numerousinnovations 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 asa research function. Change never comes easily. Transformative change means that businesses must be redesigned, just asthe factories of the nineteenth century had to start all over. For listening to avoid that fate, twenty-first-century managers mustanticipate its transformative impact and to remember that, in essence, it is still research, with the demands on integrity andaccuracy that the profession demands.Stephen D. Rappaport, Knowledge Solutions Director at the Advertising Research Foundation (ARF), is responsible forcreating the knowledge resources, tools, and services that help members build brands. The Knowledge Solutions grouporganizes and synthesizes the rich contemporary knowledge generated through ARF activities along with archival sources andtrusted 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 throughthe ARF Knowledge Center. Rappaport also is the lead author of the best-selling The Online Advertising Playbook: ProvenStrategies and Tested Tactics from the Advertising Research Foundation. E-Mail: srappaport@thearf.org.ReferencesChoi, 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 theeleventh 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 Webinarpresentation, 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.com13   
  • 14.  request: E-Mail jar@thearf.org.Nail, J., and J. Chapman. “Social Media Analysis for Consumer Insight: Validating and Enhancing Traditional Market ResearchFindings.” 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: HowBiometrically-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 AdvertisingResearch 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 2010Advertising Research Foundation432 Park Avenue South, 6th Floor, New York, NY 10016Tel: +1 (212) 751-5656, Fax: +1 (212) 319-5265www.warc.comAll rights reserved including database rights. This electronic file is for the personal use of authorised users based at the subscribing companys office location. It may not be reproduced, posted on intranets, extranetsor 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.com14