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This article was downloaded by: [York University Libraries]On: 02 October 2013, At: 16:47Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
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Audience Evolution and the Future ofAudience ResearchPhilip M. Napoli aa Fordham University, USAPublished online: 05 Jun 2012.
To cite this article: Philip M. Napoli (2012) Audience Evolution and the Future of Audience Research,International Journal on Media Management, 14:2, 79-97, DOI: 10.1080/14241277.2012.675753
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The International Journal on Media Management, 14:79–97, 2012Copyright © Institute for Media and Communications ManagementISSN: 1424-1277 print/1424-1250 onlineDOI: 10.1080/14241277.2012.675753
INVITED ESSAYS
Audience Evolution and the Futureof Audience Research
PHILIP M. NAPOLIFordham University, USA
This article considers how changes in audience behaviors and inaudience information systems are affecting the future of academicaudience research. This article first illustrates how changes inthe media environment are undermining traditional approachesto audience research while also giving rise to alternative ana-lytical approaches. This article then outlines the contours of anext-generation audience research agenda that reflects these ongo-ing conceptual and methodological developments. Reflecting thesedevelopments, this article next argues for a definition of ratingsanalysis that focuses not on a particular aspect of audiencebehavior (i.e., exposure), but on whatever forms of currency areemployed in the audience marketplace. From this standpoint, thenew media environment provides new forms of “ratings” that existalongside—and integrate with—the old, and that can renew andrevitalize the field of ratings analysis.
Media audiences are changing. The dynamics of how audiences consume(and, now, even produce) media are changing, thereby giving audiencesincreased control and increased choice over when, where, and how theyconsume media. At the same time, new technologies for monitoring audiencebehavior are revealing aspects of how and why audiences consume mediathat were previously unknown. These technological changes are compellingmedia organizations to think differently about their audiences, undermin-ing traditional conceptual and analytical approaches while opening up new
An earlier version of this research was presented in June 2011 at the COST TransformingAudiences, Transforming Societies Workshop in Helsinki, Finland.
Address correspondence to Philip M. Napoli, Graduate School of Business, FordhamUniversity, 113 W. 60th St., New York, NY 10023. E-mail: [email protected]
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approaches to conceptualizing audiences. Thus, although, in some ways,audiences are becoming more elusive, in other ways, new systems of mea-suring media audiences, and of gathering feedback from them, are making itpossible for media organizations to fundamentally redefine what media audi-ences mean to them and how they factor into the economics and strategiesof their businesses.
Thus, we are in the midst of an evolution in the nature of media audi-ences. Just as a growing body of scholarship has shown how media evolvein response to changing environmental conditions (e.g., see Dimmick, 2002;Noll, 2006), so, too, do audiences evolve in response to such changes; or, tobetter reflect the focus of this analysis, media organizations’ perceptions oftheir audiences evolve in response to changing environmental conditions.
These developments have dramatic, although largely unexplored, impli-cations for the field of audience research1—specifically, for that part of thebroad and multifaceted audience research field that directly engages withmedia management-, economics-, and strategy-related questions. Thus, thisdiscussion focuses on academic audience research traditions with a strongmedia management orientation, such as ratings analysis.2
Today, the theories of audience behavior and the methodological toolsthat have characterized the field are ripe for reconsideration, extension,and innovation in light of the changes that are affecting the dynamics ofaudience behavior, as well as the processes via which the behaviors ofmedia audiences are being measured and valued. This article is an effortto explore these possibilities. Drawing on the concept of audience evolu-tion (Napoli, 2011), the first section of this article provides an overview ofthe major technological changes that are affecting the dynamics of audiencebehavior and the analytical tools via which these behaviors are measuredand monetized. The second section explores how this process of audi-ence evolution affects areas of academic audience research. This sectionillustrates how traditional approaches to audience research are, in someways, being undermined by this process of audience evolution, as wellas how new and potentially illuminating analytical paths are opening up.In addressing these issues, this section outlines the preliminary contours ofa next-generation audience research agenda. The concluding section offersa slight re-definition of the field of ratings analysis that better reflects theanalytical opportunities and imperatives presented by the evolving audiencemarketplace.
AUDIENCE EVOLUTION AND THE NEXT-GENERATIONAUDIENCE RESEARCH
We are in the midst of a period of profound evolution in the nature ofthe institutionalized media audience. This notion of the institutionalizedmedia audience refers to the audience as conceptualized, operationalized,
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and monetized within the marketplace for media audiences (Napoli, 2003,2011)—that is, the audience as reflected in the various audience informa-tion systems that are utilized in the audience marketplace. As this sectionillustrates, the long-established approaches to the institutionalized mediaaudience are under significant pressure from two interconnected fronts. Thefirst of these involves the changing dynamics of media usage. The secondinvolves the technological transformations associated with the gathering ofaudience data. Each of these is discussed in turn.
The Transformation of Media Usage
Contemporary audience evolution is being driven, in large part, by the tech-nological changes that are transforming how audiences use media. As hasbeen well-documented, the media environment is changing in ways thatare dramatically reconfiguring how, when, and where audiences use media(Cover, 2006; Livingstone, 2003). The two key phenomena that are at the coreof these changes—and, thus, the two key phenomena that are affecting theinstitutionalized media audience—are media/audience fragmentation andaudience autonomy (see Napoli, 2011).
Media/audience fragmentation. The ever-growing fragmentation ofthe media environment allows for an increased array of content optionsto be provided across an increased array of distribution platforms while,within many of these distribution platforms, the capacity for providingmore choices continues to dramatically expand. These processes drive thecontinued disintegration of traditional “mass” audiences (Neuman, 1991)and the increasing prominence of “long-tail” scenarios in which audienceattention is clustered around a select few content options, followed by along tail in which the remaining multitude of content options each attractvery small audiences, which, in the aggregate, can exceed the audience forthe “hits” (Anderson, 2006).
From the standpoint of this analysis, the key implication of thesewell-documented developments is the extent to which they are undermin-ing traditional institutionalized approaches to media audiences. Specifically,the fragmentation of media and audiences is undermining the traditional,exposure-based audience measurement and valuation approaches that longhave dominated the audience marketplace (e.g., see Webster, 2008). As manycritics of media industries have noted (e.g., Ang, 1991; Ross & Nightingale,2003), the established institutionalized approach to media audiences haspreferred basic exposure over other aspects of audience behavior, such asaudience appreciation, interpretation, or response.
Increased media and audience fragmentation are making it increas-ingly difficult for media industries to operate from this perspective, giventhe increased difficulties associated with accurately and reliably quantify-ing audiences via traditional exposure-focused measurement systems. Theextent to which audiences today can be spread across a much wider range
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of content options, and across a much wider range of delivery platforms,undermines traditional panel-based measurement approaches that rely onthe creation, and systematic measurement, of a sufficiently large and repre-sentative sample of the audience (Napoli, 2011). It is simply impossible formeasurement firms to recruit and maintain representative audience panelsthat are large enough to capture the true distribution of audience attentionacross the wealth of available content options and across all of the platformsvia which that content can be consumed. As a result, inaccuracy of basicexposure data increases, as do unpredictable fluctuations in the data. “Zerocell” problems also become more prominent.
Research has, for instance, documented substantial discrepanciesbetween various Web audience measurement firms in terms of the rankingsof just the 10 most popular Web sites. The magnitude of this discrepancy dra-matically increases when the frame is expanded to include the top 100 Websites; here, there is, on average, only 40% agreement between measurementservices (Lo & Sedhain, 2006). Most cable networks and most Web siteshave audiences that are too small to even be reported by established panel-based audience measurement systems, such as those operated by Nielsenand comSCORE®. Nielsen is able to provide audience ratings for only about80 of the more than 500 cable networks in operation in the United States.The more than 400 remaining networks have audiences that are, on average,simply too small to be accurately captured by the roughly 25,000 householdsthat are currently included in Nielsen’s national U.S. television audience mea-surement sample; yet, in the aggregate, these unmeasured networks canrepresent as much as 25% of television viewing (McClellan, 2008). Radioaudience measurement firm Arbitron provides audience ratings for onlyone-half of the more than 13,000 radio stations in operation in the UnitedStates (Gunzerath, 2001); and, at the most extreme end of the continuum,Internet audience measurement firms, such as Nielsen and comSCORE, pro-vide detailed audience estimates for roughly 15,000 to 30,000 of the estimated187 million available Web sites (Napoli, 2011). The end result of these pat-terns is a growing amount of what we can term “dark matter” of audienceexposure—audience exposure that marketplace participants know is takingplace, but that they cannot meaningfully categorize.3
The magnitude of these tendencies varies across platforms—somethingthat raises particular challenges as content increasingly flows from one plat-form to the next. Consider, for instance, that the size of the audience exposedto a single broadcast network television program must now be cobbledtogether from estimates not only from traditional broadcast and cable plat-forms, but also from on-demand services, digital video recorders (DVRs),Web streaming, and hand-held devices such as iPods®. As the audience forthis program fragments across different media, it is essentially migrating awayfrom the traditional platform (broadcast television), where the limitationsassociated with traditional panel-based audience measurement systems are
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less pronounced, to newer platforms, where these limitations are much morepronounced and where sufficiently accurate and reliable alternate audienceinformation systems have yet to be fully developed (Friedman, 2009c). Onestudy of television viewing estimated that 13% of television viewing timetakes place via unmeasured platforms (Brill, Bloxham, Holmes, Moult, &Spaeth, 2007).
Today, the overall audience profile (in terms of the proportion ofthe total audience accounted for by different viewing platforms) can varydramatically from program to program (Adalian, 2010). These different frag-mentation patterns of a particular piece of content’s audience across multipleplatforms can have dramatic implications, as the value of audiences—and,thus, the revenue potential of the content—fluctuates, to some extent,according to the platform via which the content is consumed (Napoli, 2003,2011).
There are, it should be noted, a wide range of initiatives currently under-way to combat the strain that the new media environment is imposing ontraditional exposure-oriented approaches to media audiences. Efforts, suchas Nielsen’s Local People Meter initiative, are introducing advanced set-topmeter technologies into local television markets in response to the inabilityof paper diaries to effectively gather audience exposure data in a highly frag-mented television environment (Buzzard, 2002). There are also a variety ofongoing efforts to convert cable and satellite set-top boxes into sources ofsystematic audience data (e.g., see Knox, 2011).
To address television audience fragmentation across platforms, PRResearch (2006) initiated an “Anytime Anywhere Media Measurement” pro-gram that sought to measure television viewing, “regardless of the platformon which it is viewed” (p. 1), as well as a “convergence panel” designed toextract data about television and Internet use from the same panel (Erichson,2009). More recently, the company introduced “online campaign ratings,”which promise to provide measures of audience exposure to individualonline advertisements in a way that is comparable to the demographic reach,frequency, and gross rating point estimates that have long been the standardin television (Buchwalter, 2011).
Along similar lines, NBC Universal has implemented what it callsits “total audience measurement index,” which is a measure of viewersacross multiple platforms, including network and cable TV, online, videoon demand, and mobile (Steinberg, 2008a, 2008b). Upstart firms, such asIntegrated Media Measurement, Inc. (IMMI; 2008), have focused on try-ing to capture audience exposure to content across multiple platforms(television, radio, Web, phones, and movie theaters) using a single mea-surement device—in IMMI’s case, a cell phone enhanced with the necessarymeasurement technology (Kang, 2008).
Arbitron’s Portable People MeterTM (PPMTM) initiative is another effort tocombat the growing inadequacies of paper diaries with the introduction of
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portable electronic meters in a more fragmented radio environment charac-terized by additions such as low-power FM radio, digital audio broadcasting,and satellite radio (Napoli, 2009). At this point, however, the PPM mea-surement technology is limited to only 48 of the 282 radio markets in theUnited States, although Arbitron is working to transition the remaining mar-kets (which continue to be measured by paper diaries) to online surveys inan effort to bring greater accuracy and stability to the measurement of radioaudience exposure in these smaller markets (Stern, 2011).
Online audience measurement services are also working to enhancetheir sample sizes and develop alternative mechanisms for capturing accurateand reliable measures of online media exposure (Bermejo, 2007). Many cur-rent efforts online are focused on melding panel-based methodologies withsite-centric approaches (“ComSCORE Announces,” 2009; Pellegrini, 2009).Site-centric methods focus on gathering data from the server logs of individ-ual Web sites, and are appealing to content providers and advertisers in thesense that they offer the ability to essentially analyze a census—rather thana sample—of all of a Web site’s visitors (e.g., see Quantcast, 2008). It shouldbe noted, however, that it is more difficult to obtain audience demographicinformation from server log data, although some firms have developed sys-tems of projecting audience demographics on the basis of such information(e.g., see Quantcast, 2008), and that, in general, panel-based and site-basedmeasurement systems tend to produce significantly different results regardingthe overall popularity of individual Web sites (Napoli, 2011).
Returning to the long-tail terminology, as the tail comes to account fora greater proportion of overall audience attention, and as the audiencescontained within this tail become increasingly fragmented (i.e., as the taillengthens), the proportion of the total amount of audience attention thatcan be effectively measured—and consequently monetized—via traditionalapproaches to the measurement of audience exposure decreases. The rate atwhich the tail is growing is outpacing the rate at which measurement firmscan capture the audiences occupying the tail.
Audience autonomy. The term audience autonomy, in this case, refersto how contemporary characteristics of the media environment—rangingfrom interactivity, to mobility, to on-demand functionality, to the increasedcapacity for user-generated content—all serve to enhance the extent to whichaudiences have control over their interactions with media. One industry ana-lyst described the contemporary media environment as one in which theconsumer is “devastatingly in control” (Jaffe, 2005, p. 43). The use of theterm devastating in this statement is particularly telling, as it suggests thataudience autonomy, like fragmentation, may be having damaging effects onthe traditional dynamics of the audience marketplace. Indeed, this is the case.
The damaging effects of increased audience control of the media con-sumption process have been felt particularly powerfully in terms of the audi-ences’ increased ability to avoid advertisements. The commercial-skipping
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features of DVRs; online pop-up, ad-blocking software; and the growth ofon-demand, ad-free, or ad-diminished content delivery platforms are justsome of the mechanisms by which today’s audiences can control theirexposure to advertisements.
These developments have given rise to alternative pricing regimes, suchas the C3 ratings currency in television in which advertisers pay only for theaverage size of the commercial audience that an individual program captureswithin 3 days of its original airing. This currency, the compromise outcomeof a heated conflict between advertisers and programmers (see Napoli, 2011),denies programmers the ability monetize program viewers who do not viewcommercials, but does grant them the opportunity to monetize commer-cial viewers for 3 days beyond the program’s (and commercial’s) originalaudience.
Further, as one analysis noted, “Traditional measures that were designedfor passive media fail to capture the important and differentiating dimensionsof response to interactive communication” (Stewart & Pavlou, 2002, p. 382)—that is, the wide-ranging dimensions of autonomy and interactivity that canbe found across the various platforms of our media system are forcing mediaindustry stakeholders to confront the inherently limited and largely unidi-mensional conceptualization of the audience that has been embedded in thetraditional audience marketplace, and in the traditional measurement systemsthat served this marketplace. In this regard, there is a growing recognitionamong the relevant stakeholders in the audience marketplace that traditionalexposure-based systems of audience measurement are really only capturingthe tip of the iceberg in terms of the dynamics of audience behavior (Napoli,2011). Once such perceptions reach a critical mass, the conditions are inplace for the institutionalized audience to undergo change.
As much as the autonomy facilitated by the new media environment isundermining the traditional passive, exposure-focused conceptualization ofaudiences, it is also opening up new avenues of audience understanding—that is, the various interactive components of the new media environmentilluminate previously concealed dimensions of audiences, many of whichare being judged to have significant economic and strategic value. Equallyimportant, these dimensions of audience behavior can be more easily aggre-gated in today’s highly interactive media environment than was the case inyears past.
Transformations of Audience Information Systems
It is important to recognize that, as much as exposure has traditionally servedas the fundamental currency in the audience marketplace, the dynamics ofmedia consumption are much more robust and multifaceted. This point isillustrated in Figure 1, which outlines a multidimensional model of audiencebehavior—one in which exposure resides at the core, but that has other
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Awareness
Engagement
Appreciation RecallBehaviorInterest Exposure
AttitudeLoyalty
Attentiveness
Emotion
FIGURE 1 Audience dimensions.
components which both precede and follow exposure. As Figure 1 illus-trates, exposure is preceded by elements such as awareness of, and interestin, content. Attentiveness and loyalty (2 frequently used criteria for assessingthe performance of media content) are positioned in the model as, essen-tially, derivates of exposure, as both have often been derived from traditionalexposure data (Webster & Phalen, 1997). Then, we reach various forms ofaudience response to content, beginning with appreciation and emotionalresponse, the intensity of which can then contribute to attitudes towardthe content and levels of recall, which, in turn, feed into specific behav-ioral responses (e.g., product-purchasing behaviors). Given the increasinglywidespread, but very much unsettled, use of the operational definitionof the concept of engagement (Advertising Research Foundation, 2006a,2006b, 2007), it is positioned as an overarching concept that can incorpo-rate elements ranging from loyalty and attentiveness to behavioral response,depending on the definition being employed. Today’s media environment isone in which, largely thanks to its increasingly interactive nature, more ofthese dimensions of audience behavior can be monitored and, as a result,can potentially be monetized by media industries and, thereby, become partof institutionalized approaches to audiences (see Harris & Chasin, 2006).
For instance, the market research firm Marketing Evaluations, producersof the venerable “Q scores,” which, for years, have been used to assess therecognizability and marketability of celebrities, introduced a metric called“emotional bonding Q scores,” which the firm applies to individual televisionprograms as a comparative indicator of the level of emotional involvementthat audiences have with programs (Friedman, 2009a; Vasquez, 2008). Thelogic of the value of these scores is, according to an executive within thefirm, that “emotionally committed viewers are more frequently exposed toand more receptive to advertising” (Friedman, 2009a, p. 1).
Firms such as Nielsen, Networked InsightsTM, Wiredset, and GeneralSentiment are offering large-scale analyses of online conversations andaudience activities (including linking activities, sharing, social media discus-sions, and rating content) that can be employed as alternative, or at leastsupplementary, indicators of a piece of content’s popularity and impactthat extends beyond traditional exposure-based ratings. Such analyticalapproaches are being used to assess a variety of forms of media content
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including songs, television programs, advertisements, and even videogames(e.g., see O’Malley, 2008; Plunkett, 2008; Sullivan, 2010). As Nielsen notedwithin the context of its BuzzMetrics service, “Word of mouth, or CGM[consumer-generated media], is intertwined with engagement” (AdvertisingResearch Foundation, 2006b, p. 8). However, reflecting the unsettled defini-tional state of the concept, Nielsen simultaneously stated that CGM can beviewed as “an indicator or proxy of engagement; a form of engagement; ora result of engagement” (Advertising Research Foundation, 2006b, p. 8).
Audiences’ recall of content and advertisements has also been treatedas an element of engagement; and, in some instances, recall has evenbeen treated synonymously with engagement. Nielsen, for example, pro-vides a service called IAG Research, which measures audience engagementfor individual television programs and advertisements. Each day, thousandsof panelists answer questions via online surveys about the details of theprograms and advertisements they watched the previous day. The higherthe average number of questions correctly answered by the panelists, thehigher the “program engagement score” for that program. The IAG data havealready begun to be utilized as currency in transactions between televisionprogrammers and advertisers (Neff, 2007).
Media-buying firm Optimedia recently launched what it called its “con-tent power ratings,” which rank “the relative reach and power of the top100 television programs, based on the number and quality of viewers acrossimportant digital and traditional media” (“Media Industry’s First,” 2007, p. 1;see also Vasquez, 2008). These ratings involve the integration of variousforms of audience exposures and engagement data, including traditionalNielsen television ratings, comSCORE online audience estimates, “tweets,”and online “buzz” data from E-Poll (“Media Industry’s First,” 2007), to forma measure of engagement that Optimedia referred to as “our staple cur-rency” (Young, 2008, p. 2). Online audience research firm Web AnalyticsDemystified developed a multifaceted engagement metric comprising sevendifferent dimensions, including number of page views, time spent on asite, propensity to supply feedback, and level of interaction with the site(Peterson & Carrabis, 2008).
This is just an abbreviated sampling of the wide range of alterna-tive audience information systems that have emerged in recent years toexpand the notion of the media audience beyond traditional, demograph-ically driven, exposure-focused metrics. Of particular importance is the factthat these alternative analytical approaches are beginning to make substan-tive inroads into the operation of the audience marketplace—particularly inthe television industry and online. For instance, the cable network Logo,which targets gay and lesbian viewers and has only 30 million subscribersnationally, is one of the many cable networks that does not attract an audi-ence sufficiently large enough to qualify for detailed ratings reports byNielsen. Logo is also one of the growing number of cable networks that
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are consequently turning to engagement metrics in their efforts to attractadvertising dollars, either as an alternative to traditional exposure metrics oras a supplement intended to bring a premium for more engaged audiences(Carter, 2009; Crupi, 2008).
NBC, which, of course, does not suffer from the same audience verifi-cation problem as cable networks such as Logo, nonetheless began offeringguaranteed levels of audience engagement to advertisers in the same waythat the network has traditionally guaranteed certain levels of ratings points(Neff, 2007). Further reflecting this new expansiveness in the audience mar-ketplace, in 2008, ABC introduced its Advertising Value Index, which allowsadvertisers to choose from more than 15 criteria, including traditional demo-graphic factors such as age, income, and employment status, as well asother criteria such as length of tune-in to commercials or level of programengagement (Kang & Vranica, 2008).
In the online space, the pattern at this point has the exposure-basedpricing model of display advertising continuing to be supplanted by cost-per-click pricing models (Shields, 2008), which, in turn, are in some quartersbeing supplanted by cost-per-action models, which are based on even morestringent behavioral indicators than simple click-throughs (Spencer, 2007).In the social media space, the largest brands are primarily buying audienceson such behavioral “key performance indicators,” rather than on the moretraditional basis of “impressions.” Impression-based pricing continues to loseground to performance-based pricing in the world of online advertising suchthat, according to one estimate, “there are more than 3 trillion impressionseach year that go unsold” (Koretz, 2009, p. 2).
This statement reflects the likelihood that we are in the early stages ofthe migration to a post-exposure audience marketplace. This transition, andits causes, are represented in Figure 2, where the exposure dimension hasbecome a diminished component of a much broader, more multidimensionalconceptualization of the audience and in which the pressures provided byboth the transformations in the dynamics of media consumption and thedevelopment of new audience information systems simultaneously serve toboth reduce the prominence of exposure-based approaches and facilitatethe institutionalization of alternative analytical approaches. As Figure 2 rep-resents, this process takes place only if there is, simultaneously, a widespreaddissatisfaction among marketplace participants with the status quo and theavailability of the means to economically and efficiently employ alternativeor supplemental approaches.
THE FUTURE OF AUDIENCE RESEARCH
The previous discussion illustrates a dramatically changing portrait of theaudience marketplace and, most important, of the analytical tools that are
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Autonomy Fragmentation
Evolving InstitutionalizedMedia Audience
Appreciation
Response
Interest
Exposure
Traditional Institutionalized Media Audience
Exposure
FIGURE 2 The decline of exposure and the rise of alternative audience conceptualizations.
being used to both measure and monetize media audiences. For those audi-ence researchers engaged in lines of inquiry that directly engage with themanagement and operation of the media and advertising industries, thismeans that there is a growing array of analytical tools that can—and should—be integrated into contemporary audience research. The emergence of thesenew analytical approaches to audiences has a range of implications for futuredirections in the field of audience research.
First, analyses that focus on traditional audience exposure variables needto become increasingly multidimensional. Multidimensional, in this case,refers to the notion that audience exposure increasingly needs to be ana-lyzed across multiple media consumption platforms. Thus, for instance, theanalysis of television audience exposure needs to incorporate not only expo-sure via the television, but also exposure via newer platforms such as theWeb and mobile devices. Given the different behavior patterns that charac-terize the use of different media platforms, it is important that contemporaryaudience research seek to account for these differences across platforms, and
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assess their implications for efforts to simultaneously distribute content andmonetize audiences across multiple platforms.
Given the economic differences (e.g., in terms of advertiser valuations)associated with the different distribution platforms, audience research thatcan contribute to predictive models of how audience exposure is likely tobe distributed across these various platforms is essential to understanding theeconomics of—and formulating strategy within—the contemporary audiencemarketplace. To what extent is the distribution of exposure across variousviewing platforms a function of traditionally important explanatory factorssuch as content types, dayparts, audience demographics, or the availabilityof alternative content options? Exploring such questions is the starting pointfor taking audience research into the multiplatform era.
An additional layer of complexity in the analysis of audience exposurearises from the increasing extent to which measurement systems are capa-ble of disentangling content and advertisement exposure. A prime examplewould, of course, be the transition to C3 ratings that has taken place in tele-vision. Such metrics now exist alongside traditional metrics that describe thesize of the audience exposed to the content. Research has already demon-strated that the strength of the correlation between content exposure andcommercial exposure can significantly vary across content options (e.g., seeFitzgerald, 2010). Recent developments, such as Nielsen’s introduction ofonline campaign ratings (see the earlier discussion), allow for similar rela-tions to be investigated in the online context. Why such variations occurhas received relatively little research attention. Does the relation betweencontent exposure and advertisement exposure systematically vary in accor-dance with factors such as content types, audience demographics, or otherstructural or individual audience or media factors? Answers to such ques-tions would certainly provide useful practical and theoretical insights intothe nature of media audience behaviors in the digital age.
The bottom line is that audience exposure is a more complex constructthan it used to be, which provides a variety of new lines of inquiry in audi-ence research. That being said, it is also important to recognize some ofthe growing limitations associated with the analysis of audience exposure.Specifically, as was illustrated earlier, fragmentation is leading to a scenarioin which an increasing amount of audience exposure is essentially goingunmeasured. As more and more audience exposure migrates from the mea-surable head to the less measurable long tail, the extent to which commercialaudience data sources are able to facilitate the analysis of the totality ofaudience exposure patterns is becoming increasingly circumscribed.
For the academic audience researcher, such data are becoming anincreasingly attenuated representation of audiences’ media exposure. It maybe the case that many of the questions central to the operation of the audi-ence marketplace can still be answered by audience information systemscapable of rigorously capturing audience exposure data on only a portion of
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available television networks, radio stations, or Web sites. However, it seemsan increasingly tenuous proposition that a thorough understanding of the fullscope of audience behavior, with the level of rigor that should characterizeacademic social science, can be achieved by data sources that are becom-ing less and less comprehensive in terms of their ability to capture the fulldistribution of media audiences. In this regard, the analytical limitations oftraditional, exposure-focused audience metrics need to be recognized, andthe exact nature of these limitations needs to be a focal point of discussionand analysis in the audience research field.
Then, of course, there is the need to begin rigorously assessing thenature of the relation between audience exposure and the various otherdimensions of audience behaviors that are beginning to gain traction inthe audience marketplace, such as recall, appreciation, and engagement.We already see indications that media content that performs well accord-ing to traditional, exposure-based performance metrics does not necessarilyperform well according to alternative metrics, such as engagement, recall,or appreciation, and vice versa (e.g., see Jackson, 2010; “Optimedia U.S.Launches Content Power Ratings,” 2008). For instance, songs and televisionprograms that do not rate very high according to traditional, exposure-basedmeasures (such as sales charts and Nielsen ratings) often prove to be amongthe most popular when they are assessed by alternative measurement cri-teria, such as how much songs and television programs are featured in theonline conversations found in social media sites (Jackson, 2010; Lafayette,2008; O’Malley, 2008) or in terms of the “stickiness” (i.e., time spent with) ofthe content (Friedman, 2010).
However, once again, why these differences exist, and how they can beexplained and predicted, are questions that remain largely unanswered, butare questions that can lead to a much more robust, integrated, and multi-faceted understanding of the complex construct of “audiencehood.” We can,for instance, begin to build the next generation of audience behavior theoryby examining questions such as the following: To what extent is the rela-tion between exposure and measures of recall, engagement, or behavior afunction of factors such as content types, dayparts, audience demographics,or the availability of alternative content options? To what extent can audi-ence engagement, recall, or behavioral responses be effectively predicted viathe individual and structural audiences and media factors that have provenso useful in the prediction of audience exposure (see Webster & Phalen,1997)?; or, are there alternative explanatory factors that need to be taken intoconsideration when the analytical lens expands beyond exposure?
Considering these latter questions, we have seen indications that whenit comes to certain measures of online audience engagement with adver-tisements, for instance, social media sites perform significantly better thanother types of sites (Morrissey, 2009). Data also indicate that, across a num-ber of emerging engagement indicators, reality television programs perform
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significantly better than scripted programs (Friedman, 2009b). Such findingsrepresent the initial building blocks of a much deeper understanding of theinteraction between the new media environment and the new criteria foraudience value.
Finally, it is important to emphasize that this discussion is not meant to,in any way, contend that the newer measures of audience behavior are nec-essarily conceptually or methodologically superior to established exposuremeasures. Indeed, a thorough interrogation of the underlying assumptionsand methodological approaches to these emergent audience metrics rep-resents yet another important line of inquiry for the field of audienceresearch. These new methodological approaches are emerging and beingintegrated into the operation of the audience marketplace with surprisinglylittle scrutiny of how these representations of the media audience are beingproduced. Ratings scholars have a long tradition of assessing and critiquingthe institutionalized approaches to audience research (e.g., see Ang, 1991;Meehan, 1984), and these emergent audience information systems are verymuch in need of a similar critical eye.
CONCLUSION: RATINGS ANALYSIS 2.0
The lines of inquiry proposed in the previous section reflect the notion thatthe future of the audience marketplace is likely to be one in which thereare “a basket of currencies.”4—something that academic audience researchneeds to take into account as well. The concept of audience ratings—and,thus, ratings analysis—is expanding. This perspective does, however, bumpup against the traditionally defined parameters of ratings analysis.
Ratings analysis has historically relied on the syndicated audience datasources produced by commercial audience measurement firms and used bymedia organizations and advertisers to engage in a wide range of analysesof media audiences’ exposure patterns. Ratings analysis has been used toexplore a variety of questions (both theoretical and applied) related to thedynamics of audience behavior and the operation of the marketplace formedia audiences (Webster, Phalen, & Lichty, 2006).
Webster et al. (2006), in their well-known ratings analysis text, definedratings analysis as the analysis of “a body of data on people’s exposure[italics added] to electronic media” (p. 11). It is important to emphasizethat, under such a definition, analyses employing the emerging commer-cial data sources that are capturing dimensions of audience behavior, suchas engagement, recall, and behaviors, and that are supplementing—and, insome contexts, supplanting—exposure data, fall outside the parameters ofratings analysis.
Given the extent to which these emergent analytical approaches arealready proving to make inroads into a marketplace traditionally dominated
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by exposure-focused metrics (see earlier discussion), suggests a very closeaffinity between these various analytical approaches. Perhaps, then, the fieldof ratings analysis should be more broadly defined. More appropriate wouldbe an alternative approach in which ratings analysis is defined in terms ofthe source and purpose of the data being analyzed—that is, ratings analysismay be more usefully defined as analyses that utilize the commercial datasources (whatever their orientation) used by media industry stakeholders toassess performance and success in the audience marketplace. Such a defi-nitional approach imbues the field with the flexibility necessary to adapt tovarious stages of audience evolution. If this definitional stance is adopted,then we see that we are simply at the beginning of a new era in academic rat-ings analysis, as alternative criteria for monetizing media audiences emergealongside exposure and are accompanied by the same kind of large-scale,syndicated commercial databases that have long been associated with themeasurement of exposure. From this standpoint, the nature of “ratings” ischanging—or, more accurately, expanding, and the nature of the questionsthat can be investigated via ratings analysis is expanding accordingly. Fromthis perspective, this is a very exciting time to be engaged in ratings analy-sis, as this research tradition is, essentially, entering a period of ferment andreinvention.
NOTES
1. For an important context in which the transformation of media audiences and its implicationsfor audience research is being explored, see the ongoing work of the COST Initiative, TransformingAudiences, Transforming Scientists (http://www.cost-transforming-audiences.eu).
2. It is important to recognize that there are a wide range of important audience research traditionsthat do not engage with questions that generally fall within the purview of the fields of media managementand economics.
3. The term dark matter is borrowed from astronomy, where researchers have determined thatthere is a substantial amount of matter in the universe that is undetectable, but the existence of whichcan be inferred from its apparent gravitational effects on visible matter.
4. This statement was made by a panelist at the Advertising Research Foundation’s AudienceMeasurement 4.0 conference, which I attended in June 2009 in New York.
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