Reconfiguring the Audience Commodity

Abstract

Changes in the ways that audiences use television, and the ways in which such usage can be measured, raise the possibility of a transformation of the audience commodity, and the currency that fuels the audience marketplace. Specifically, it appears at this point that social media analytics are beginning to play a role in how television program success is measured, and in how advertising dollars are allocated across programs. Essentially, then, the emergence of social TV analytics represents the possibility of a new market information regime taking hold in the audience marketplace. Working from an institutional theoretical framework, this article uses trade materials as a window into industry dynamics and discourses in an effort to provide an account of the recent emergence and usage of social TV analytics in the U.S. television industry and thus explore the process of institutionalization of a new market information regime.

Keywords

audience social TV analytics market information regimes metrics currency institutionalization institutional theory

Audience attention has long served as the currency that fuels the television industry (Napoli 2011). Through the process of measuring this attention, audiences become effectively commodified in the audience marketplace (Meehan 1984). These processes of measurement, and the economic, social, and cultural implications of how this measurement takes place, have been a frequent point of focus in the media studies literature (see, for example, Ang 1991; Bourdon and Meadel 2011; Buzzard 2012; Hurwitz 1984; Meadel 2015).

Changes in the ways that audiences use television, and the ways in which usage can be measured, raise the possibility of a transformation of the commodified television audience (see, for example, Adams 1994; Buzzard 2002; Meadel 2015). Today, social media analytics are beginning to play a role in how television program success is measured, and in how advertising dollars are allocated across programs (Shively 2014; Wright 2014). Services devoted specifically to the analysis of the volume and valence of social media conversation about television programs (social TV analytics) have emerged to offer participants in the audience marketplace an analytical alternative to traditional Nielsen ratings. Whereas Nielsen ratings measure audience exposure, these new services measure audience engagement. Consequently, social TV analytics data produce a very different portrait of which shows are succeeding and which are failing (Hayes 2014). Furthermore, social TV analytics serve as a means of providing insights for the large number of programs and networks with audiences that are too small to be measured and reported by the Nielsen system, which has had difficulty keeping up with the increasingly fragmented television audience.

Essentially, the emergence of social TV analytics represents the possibility of a new market information regime taking hold in the audience marketplace. The term market information regime refers to the socially constructed mechanisms via which marketplace participants assess their performance and the performance of their competitors (Anand and Peterson 2000). The information produced by these regimes becomes fundamental to how marketplace participants perceive the dynamics of their market, and thus affects organizational strategy and decision making. They are, essentially, the agreed upon lens through which marketplace participants perceive their world. Market information regimes operate in a variety of industry sectors (think, for instance, of college rankings), and are particularly prominent in the media sector, where best-seller lists for books, weekly box office rankings, hit charts for recorded music, and, of course ratings for television, radio, and online content, all serve as important mechanisms by which the participants in these industries make sense of their performance, analyze trends in audience preferences, and monitor their competition. Any change in how a market information regime is calculated or produced, or any introduction of an alternative market information regime, can introduce substantial changes in organizations’ perceptions of a market’s dynamics and their performance within it (Anand and Peterson 2000; Andrews and Napoli 2006).

Given this influential role in affecting cognitions and behaviors, market information regimes such as audience measurement systems essentially function as institutions in the television audience marketplace (Napoli 2003). The term institutions has two interconnected meanings. It refers to formal, complex organizations as well as formal or informal routines, norms, and rules that guide cognitions and behaviors (Jepperson 1991). Both of these definitional approaches apply to audience measurement systems. What has been termed the “institutionally effective” audience (i.e., the media audience as manifested in the norms, cognitions, and practices of media markets and organizations) resides at the intersection of the behaviors of formal, complex media organizations (e.g., audience measurement firms, media buying agencies) and established norms, cognitions, and values that have gained traction across participants in the audience marketplace (Ettema and Whitney 1994).

Although there is a long history of media studies research that focuses on media institutions (Moe and Syvertsen 2007), relatively little of this research applies institutional theory to the audience marketplace (for exceptions, see Napoli 2011; Meadel 2015). Within a context such as media audience measurement, an institutional theory perspective not only helps to illuminate the central role that audience measurement systems play in the operation of media organizations and media markets, but also helps to understand the complex dynamics surrounding how new audience measurement systems are embraced or rejected by the audience marketplace—essentially, the process of the institutionalization of a new market information regime (Meyer and Rowan 1977). For a new audience measurement system to serve as a market information regime,all participants in the marketplace must work through a complex process of evaluation, consideration, and experimentation before potentially ending up at a point at which they agree to let this measurement system dictate how they perceive the functioning of the marketplace and their performance within it (Meadel 2015). Of course, this process needs to take place alongside an assessment of the pros and cons of the legacy system (in this case, Nielsen), for a final outcome to be reached in terms of whether the new system is rejected, supplants the legacy system, or perhaps functions in a supplementary capacity alongside the legacy system. It is this process of institutionalization that is the focus of this analysis. The goal is to determine whether existing theoretical frameworks regarding the process of institutionalization can enhance our understanding of the dynamics surrounding the introduction of a potential new market information regime.

The first section of the article discusses audience measurement systems as market information regimes, and the role that they play in determining value in the audience marketplace. Next, the article provides a brief overview of the relevant work on institutional theory, specifically as it relates to the process of institutionalization. The third section outlines the data and methods used. In the fourth section, we analyze industry developments related to social TV analytics over the past five years using a theoretical framework on the process of institutionalization. The concluding section considers the implications of the findings for the future of the television industry and for institutional theory. This section also proposes directions for future research.

Market Information Regimes, Audience Measurement, and Contested Visions of Audience Value

As the term market information regime suggests, for an audience measurement system to effectively dictate the cognitions and behaviors of all participants in the audience marketplace, it must possess a certain amount of authority. It must serve as the single standard—the single “currency”—in the audience marketplace. This is why the history of audience measurement has been one of monopoly or near-monopoly across most media industry sectors (Buzzard 2012).

Inherent in this dynamic, however, is the notion that there is only one source of value in the audience marketplace. Thus, Nielsen’s history of measuring audiences’ exposure to television programs, and advertisers spending money on the basis of these exposure metrics, is premised on the notion that the value of television audiences is constrained within their exposure to programming, as measured in terms of ratings and shares. Social TV analytics, however, capture an alternative (though certainly interconnected) source of audience value—audiences’ engagement with television programs, as measured through their social media activity (Arvidsson and Bonini 2014).

The emergence of social TV analytics thus presents a scenario in which there are potentially “competing understandings of value” (Moor and Lury 2011, 440), and consequently, “both the nature of value and the means to measure it are objects of debate.” In this regard, the television industry is dealing with questions of whether there is any value inherent in social media conversation about television programs, as well as questions about the best approaches to measuring this activity and turning it into coherent performance metrics. Such scenarios, in which value and how to measure it are being reconsidered, are most common in periods when “the organizational environment is turbulent” (Stark 2009, 6). This certainly describes the state of the television industry today, in which new technologies continue to increase the means by which viewers can watch television, further complicating (and undermining) traditional approaches to measuring these audiences.

Ultimately, the existence of multiple sources of value can prove beneficial, as they “create wealth by inviting more than one way of evaluating worth” (Stark 2009, 27). From the standpoint of the television industry, the existence of two market information regimes would essentially offer up two different sets (and thus a greater diversity) of “hits.” At the same time, however, an additional market information regime introduces more complexity and uncertainty into the market for television audiences (Napoli 2011).

From a market information regimes standpoint, the central question involves how the marketplace responds. Does the marketplace embrace the new source of audience value and reject the old? Does it reject the new source of audience value and maintain the old? Or, perhaps, do these alternative sources of audience value co-exist? Determining the answers to these questions—and how and why participants in the audience marketplace reached those answers—takes us back to the process of institutionalization.

The Institutionalization Process for a New Market Information Regime

New market information regimes have the potential to legitimize new strategic approaches, norms, and even business models. Within the context of audience measurement, these effects are a by-product of the fact that new audience measurement systems often provide very different portraits of the size, composition, and behavioral patterns of media audiences than those provided by the legacy audience measurement systemThis notion of legitimacy is a central component of institutional theory, which has a long-standing interest in how organizational practices become routinized and taken for granted (Meyer and Rowan 1977). Whereas much research has focused on explicating institutions as drivers of change, less attention has been paid to institutionalization, or the process of institutional change itself (Tolbert and Zucker 1996). One notable exception is Greenwood et al. (2002) who combine threads from other institutional theorists to develop a model that addresses the stages of “institutional change”—a framework of particular relevance to understanding if and how market information regimes take hold and evolve within specific industries.

Greenwood et al. (2002, 60) break down the process of institutional change into six key stages: (1) precipitating jolts, (2) deinstitutionalization, (3) preinstitutionalization, (4) theorization, (5) diffusion, and (6) reinstitutionalization. The first stage occurs when jolts disrupt stabilized practices. These jolts can be social, technological, or regulatory in nature, and give rise to the second stage (deinstitutionalization), which is characterized by the emergence of new players and efforts at institutional entrepreneurship. These new players introduce new ideas and the possibility of change (Greenwood et al. 2002). This scenario leads to the stage of preinstitutionalization, which involves the generation of new structural arrangements and the formalization of these arrangements in the policies and procedures of organizations. The next stage, theorization, involves the identification of a general organizational problem for which a particular innovation is a potentially viable solution, as well as justification for the innovation, which involves articulating its moral and/or pragmatic legitimacy (Tolbert and Zucker 1996). The establishment of pragmatic legitimacy is particularly relevant to this analysis, as it involves linking an innovation with desired economic outcomes. The next stage, diffusion, involves the process wherein new practices are adopted and gain legitimacy, until finally, at the reinstitutionalization, these new practices are “taken-for-granted as the natural and appropriate arrangement” (Greenwood et al. 2002, 61). In their model, there are two possible outcomes, one in which the new institutional structure achieves legitimacy and displaces the status quo, and one in which the new institutional structure is ultimately rejected.

This complex process of institutionalization provides the theoretical framework for this analysis. We apply it to the ongoing dynamics between established (Nielsen ratings) and emergent, potential (social TV analytics) market information regimes in the television audience marketplace to see how well it explains developments that have taken place thus far, and how it might helpanticipate future developments. The goal is to provide theoretically grounded insights into if and how new market information regimes, specifically those based on an alternative evaluation such as audience engagement, are institutionalized in the audience marketplace.

Data and Method

This article uses industry documents and trade publications as a window into the institutionalization process surrounding social TV analytics as a potential new market information regime. A qualitative textual analysis was conducted of the relevant industry trade publications, marketing and promotional materials, presentations, reports, and white papers that addressed the topics of social TV analytics and contemporary television audience measurement. In terms of trade publications, a search was conducted for articles on the topics of social TV analytics and television audience measurement in outlets such as Advertising Age, Broadcasting & Cable, MediaWeek, and MediaPost during the time period from 2009 to early 2015. In instances where one article referenced or linked to another, those articles were utilized as well.

Media industry trade publications and materials are a commonly used data source in media and communication research (Wilkinson and Merle 2013). Trade publications can be used as a secondary data source to provide background and orientation for a particular research topic. They can also be utilized as a primary data source, as is the case here, to represent industry dynamics, developments, and discourse surrounding a particular issue (e.g., Astroff 1988; Napoli 1997; Endres 2004). From this standpoint, these trade publications provide an observable forum where organizational actions, marketplace developments, and stakeholder dynamics and discursive positions are represented, and thus serve as a useful means of conducting institutional analyses (Bertrand and Hughes 2005).

These data sources must, of course, be used with an awareness of their inherent limitations and biases. Thus, for instance, industry trade publications may not provide the most objective reporting of industry developments and their implications, given their position relative to the industry’s key organizations and personnel (Wilkinson and Merle 2013). The positions that the trade publications of different industry sectors take toward specific industry or technological developments can reflect the best interests of these different sectors (Napoli 1997). Such tendencies need to be taken into account when analyzing these data sources. Nonetheless, industry trade reporting and materials provide a window into industry dynamics and developments that serve as a useful entry point for analyzing the institutionalization process surrounding a potential new market information regime.

Disruption and Institutionalization in the Television Audience Marketplace

This section presents the analysis of the institutionalization process surrounding the introduction of social TV analytics as a potential alternative market information regime. This analysis uses the theoretical framework put forth by Greenwood et al. (2002), which delineates the six stages of the institutionalization process (see above). As will become clear, for the most part, this framework corresponds quite well with the process observed thus far via the trade publications’ coverage of the television audience marketplace. However, it should be emphasized that the process of institutionalization is, in this case, ongoing and not yet fully resolved, which limits to some extent the definitiveness with which latter stages in the process can be discussed.

Precipitating Jolt: Fragmentation and the Destabilization of Established Practices

According to Greenwood et al. (2002), the first stage in the institutionalization process occurs when jolts that are social, technological, or regulatory in nature disrupt stabilized practices. In the realm of television audience measurement, we have in fact seen such a jolt in terms of the tremendous audience fragmentation that is continuing across a growing range of delivery platforms and an expanding array of content options. The industry is struggling with the fact that viewers are accessing programs not only through traditional broadcast and cable systems but also via digital video recorders and streaming platforms (Hampp 2009). Of course, the adequate measurement of audiences across these different platforms is fundamental to their value in the marketplace. However, many of these newer platforms are not yet being adequately measured, which means that an increasing proportion of audience attention is not being effectively monetized (Poggi 2014a).

This proliferation of platforms represents only part of the disruptive process affecting the television industry. Perhaps even more important is the ongoing process of “intra-media” fragmentation—fragmentation within individual platforms. The continuing growth in the number of channels available to the typical television household has put a tremendous strain on household People Meter samples that serve as the basis for the Nielsen ratings. The bottom line is that the overwhelming majority of television networks operating in the United States do not have large enough audiences to be accurately and reliably measured via the current national sample of approximately twenty-two thousand homes (Tadena 2014). By some estimates, roughly 20 percent of all television viewing goes unmeasured, with this unmeasured viewing clustered within the “long tail” comprising dozens of niche programming networks (Friedman 2014b; Morgan 2011).

These circumstances have led to efforts to repair traditional exposure/demographics-based television ratings in ways that can better account for audience fragmentation across platforms and content. One approach is the use of digital set top boxes to generate much larger samples, allowing for more accurate and reliable measurement than currently achieved under the traditional household meter/sampling system. Other alternatives are in place to try and capture “cross-platform” viewing. Nielsen, for example, announced plans to measure viewership from both mobile viewing (Perlberg 2014) and third-party streaming platforms like Amazon and Netflix (Hagey and Vranica 2014). This is an important indicator that a truly comprehensive market information regime should provide performance data on all relevant market participants—even those that do not compete in all of the same revenue streams.

The widespread consensus among the various stakeholders within the television industry, however, is that these efforts to repair the traditional approach to measuring and valuing television audiences have not kept pace with the destabilizing effects of technological change. In the U.S. television ad market worth roughly $70 billion (Hagey 2014), currency based solely on traditional ratings becomes devalued when audience exposure is dispersed to such an extent that an increasing proportion of it cannot be effectively measured.¹ Clearly, the technological changes that have undermined the value of traditional audience ratings have served as the precipitating jolt that creates an environment that is hospitable to alternative approaches and the emergence of new players (Greenwood et al. 2002).

Deinstitutionalization: The Entrance of Social TV Analytics

It is this emergence of new players that characterizes the second stage of the institutionalization process, which Greenwood et al. (2002) refer to as deinstitutionalization. In this second stage, these new players introduce new ideas and the possibility of change (Greenwood et al. 2002). In the face of the destabilizing effects that fragmentation has had on the traditional approach to television audience measurement, alternative measurement approaches such as social TV analytics have indeed emerged

One ironic aspect of the technological disruption that has been affecting the television audience marketplace is that some of the technological changes that are undermining traditional approaches to measuring and valuing television audiences also facilitate potential alternatives. Specifically, the interactivity inherent in Internet-based platforms and devices creates a “backchannel” of audience data that can provide participants in the audience marketplace with new forms of data about television audiences (Harrington et al. 2013)—data that extend beyond the exposure-based metrics of traditional measurement into realms such as recall, engagement, and behavioral response.

Social media analytics have emerged as the primary means by which audience measurement is moving beyond exposure to television programs and embracing metrics that capture other aspects of audience behavior. In 2007, multiple, independent startup companies entered the marketplace, introducing new methods for measuring and valuing television audiences. Companies such as General Sentiment, Bluefin Labs, and SocialGuide arrived on the scene offering analytical tools for social media data that could influence how media buyers allocate their spending (Patel 2011).

These social media-based performance metrics bear very little resemblance to the television audience ratings that preceded them. Most social media analytics services use some form of “web scraping,” in which the conversations posted on a wide range of social media platforms are aggregated and classified via sophisticated algorithms. A key point of distinction, obviously, is that whereas traditional TV ratings required individuals to agree to be part of the measurement sample, social media metrics draw from the online population’s expressions of their viewing habits, reactions, and opinions. These services also seek to provide subscribers with not only systematic information on their own performance but also data on the performance of their competitors and the marketplace as a whole. It is in this important way that these services meet key criteria as to what constitutes a market information regime. By 2013, there were more than one hundred startup companies in the area of social TV (Futurescape 2013), representing the entry of new players offering a fundamentally different approach to measuring and valuing television audiences—a development that corresponds quite well with the second stage in the process of institutionalization.

Preinstitutionalization: Experimentation with Social TV Analytics

The preinstitutionalization stage is when there are relatively few organizations that have adopted the new structure or practice, and are doing so in an ad hoc, or experimental, manner. Here, new structural arrangements are beginning to be established and formalized in response to a particular problem (Tolbert and Zucker 1996). Within the context of the television audience marketplace, this is essentially the stage at which some programmers and advertisers began exploring how to use social TV analytics in their work.

Preinstitutionalization occurred when individual media buying firms—and even television networks—partnered with one or more measurement firms working in this space to develop specialized performance metrics that serve their particular needs (Marich 2008). On the broadcast end, Fox partnered with trueAnthem to track social TV across a variety of properties (Edelsburg 2012). Big social media players such as Twitter promoted real-time interaction, which eventually evolved into the promise of “augmenting TV viewer engagement” (McGirt and Laporte 2013). In addition, Facebook sent weekly reports to the big four networks showing how many “actions” on the social network were inspired by each TV episode (Rusli 2013).

Preinstitutionalization also represents the stage at which organizations begin to evaluate the utility of this alternative approach to measuring and valuing television audiences. A number of different industry consortia devoted to various aspects of the question of how the marketplace should best measure and value audiences were formed—all of which engaged with the promise of social TV analytics to some degree. For instance, stakeholders ranging from large product advertisers to media companies to campaign planners came together to form an organization called the Coalition for Innovative Media Measurement to push for and examine new audience measurement systems that addressed the shortcomings of the legacy systems (Hampp 2009). Industry associations such as the American Association of Advertising Agencies, the Collaborative Alliance, and the Council for Research Excellence all formed committees and/or held conventions devoted to the question of the value and utility of social TV analytics.² These consortia are a concrete reaction to the destabilization of established practices of audience measurement. As preinstitutionalization occurs, legacy stakeholders and organizations attempt to make sense of this new playing field with some degree of exploration and potential participation.

Theorization and the Emergence of an Alternative Source of Audience Value

The theorization stage focuses primarily on the establishment of the moral and pragmatic legitimacy of a particular practice as the solution to a problem, or as a viable alternative to the status quo. Moral legitimacy is often achieved by “nesting new ideas within prevailing normative prescriptions” (Greenwood et al. 2002, 60). Within the television audience marketplace, this stage in the institutionalization process take place in the form of discussion of the connection between social TV analytics and the industry’s virtually unanimous enthusiasm for “big data.” As with most other industry sectors, the television industry has become enamored with notions of “big data” and how they can be harnessed to generate strategic insights and enhanced revenues (Baldwin 2014; Broussard 2014). Social media analytics, of course, represent a prime site for reaping the benefits of big data, and so, much industry discourse has focused on how social TV analytics represent a step into the realm of big data and a step away from the comparatively limited data pools represented by traditional Nielsen ratings panels (see, for example, Brandon 2014; Hill 2014). Thus, it is primarily through the consensus that has emerged regarding the value of big data (Marks 2013) that the moral legitimacy of social TV analytics is being established.

Pragmatic legitimacy involves linking a new idea with actual economic outcomes (Greenwood et al. 2002). The pragmatic legitimacy of social TV analytics is currently being established via the relationship between social TV activities and advertisement recall. For instance, research shows that “when TV is combined with social interaction, there’s a nice lift for brand, purchase, advertiser recognition” (Bloom 2014). A recent Twitter study found that viewers using Twitter while watching TV had significantly higher ad recall at 53 percent than those watching without a second screen, at 40 percent. The lift in purchase intent was also higher, further supporting the relationship between social media use, television viewing, and ad impact (Midha 2014). Ultimately, research of this type is fundamentally about establishing the pragmatic legitimacy of social TV analytics.

Pragmatic legitimacy also has been established through social TV analytics’ potential utility for those stakeholders that are poorly served by the legacy market information regime. As discussed previously, the continued fragmentation of the media audience makes the traditional business model of selling viewers based on size and demographics more difficult. Thus, many programmers lack the ability to deal in traditional Nielsen ratings. For these networks unable to provide potential advertisers with traditional ratings, levels of audience engagement reflected in social TV metrics represent an alternative narrative that is being successfully used, to some extent, to make the case for the value of their audience (TV by the Numbers 2010). Social TV analytics provide a currency for the variety of niche programs, platforms, and advertisements that are essentially invisible under the traditional Nielsen system (Steinberg 2010).

Diffusion: Growing Adoption of Social Media Analytics

The diffusion stage occurs when new products are adopted and gain legitimacy. It would seem that this is the stage that applies to the current state of social TV analytics. TV networks and advertisers have started using social TV data in their decision making. For example, CBS analyzes Twitter activity when making decisions about TV show renewal and cancelation, whereas SyFy’s Defiance sponsor Dodge uses social data to measure the success of their advertising partnership (Shively 2014). The CW network used social TV data to justify the cancelation of higher rated show, The Tomorrow People, over Beauty and the Beast. As network President Mark Pedowitz pointed out, “[The Tomorrow People] just didn’t have the same engagement on the social media side that Beauty and the Beast has” (Brown 2014). Viacom, which owns cable channels including MTV and Comedy Central, recently partnered with social analytics firm MassRelevance to create a measurement platform called Echograph, which allows Viacom to offer their advertisers social activity data such as reach, engagement, and influence in a step toward purchase guarantees based on social media impact (Poggi 2014b). One production executive recently went so far as to claim social engagement’s superiority over TV ratings as an indicator of content success for advertisers (Williams 2015). Such statements and activities suggest that marketplace participants are embracing social TV data as a meaningful representation of audience value.

Reinstitutionalization: Social Media Analytics as Supplementary Market Information Regime

At the reinstitutionalizaton stage, new norms or practices are essentially “taken-for-granted as the natural and appropriate arrangement” (Greenwood et al. 2002, 61). The process of the institutionalization of social media analytics is ongoing, and so the final outcome is still to be determined. Right now, the process seems to be at the diffusion stage, in which it is still uncertain whether this new market information regime will take permanent hold. Will social TV analytics become an accepted and stable means of measuring, valuing, buying, and selling television audiences, or is this a passing fad, a case of industry wide experimentation (and ultimately, rejection) of an alternative approach to measuring and valuing television audiences? This question cannot yet be fully answered.

However, it seems reasonable to predict at this point that the existing institutional structure—traditional demographics-based, exposure-focused audience ratings—will not be dislodged, but rather a secondary, supplementary market information regime based on social TV analytics will take hold, and will likely persist as an additional source of value in the audience marketplace. From an institutional theory standpoint, this situation would seem to reflect an important caveat that the process of institutionalization can have more than a binary set of outcomes (new institutional structure either displacing or failing to displace the old). It is also possible that a new institutional structure can co-exist in some way with the old.

Considering this institutionalization process in terms of the evolution or reconfiguration of market information regimes, this analysis illustrates that incumbents can engage with the process in an effort to preserve the status quo. These efforts can take the form not only of bolstering the legacy system, as Nielsen has been doing, but also of absorbing the emergent market information regime in what could be perceived as an effort to contain it within a supplementary role. In the case of social TV analytics, and its potential threat to the legacy system of measuring and valuing television audiences, Nielsen has made a number of strategic moves that can be seen as efforts to both integrate into social TV analytics while also helping to preserve the primacy of traditional ratings.

With the 2012 acquisition of social TV metrics company SocialGuide, Nielsen began what would become a long line of purchases and forays into the realm of social TV analytics. Such acquisitions allow Nielsen to subsume would-be competitors into their own operation. These strategic moves help strengthen Nielsen’s position as the legacy incumbent, thereby helping to institutionalize social TV analytics in its position as a supplementary market information regime. By evolving into a “one stop shop” for both currencies, Nielsen is able to discourage clients from making an either–or decision in relation to their primary market information regime and, in so doing, increases the likelihood of continued use of traditional ratings data (in which the firm has a substantial investment).

Perhaps most importantly, Nielsen also has partnered with Twitter to create the “Nielsen Twitter TV Rating” (NTTR), a benchmark metric based entirely on Twitter data to measure “engagement of programming” as a “complement and companion to Nielsen TV ratings” (Sladden 2012). Nielsen’s efforts to diversify into social TV analytics (which would be a logical defensive posture in the face of a potential disruption to/decline of its legacy market information regime) have clearly been accompanied by a discursive effort to explicitly position social TV analytics as supplementary to traditional Nielsen ratings.

Another significant action involves a series of studies conducted by Nielsen illustrating the connection between traditional ratings and social TV activity. One study, for example, found that the volume of tweets caused statistically significant changes in live TV ratings among 29 percent of episodes sampled (Nielsen 2013). Subsequently, Nielsen conducted a study analyzing the impact of social TV on time-shifted viewing. Here, the results showed that a 10 percent increase in NTTR impressions corresponded to a 1.8 percent increase in the +7 audience rating metric (Nielsen 2014). By conducting research that explores the relationship between social media activity and traditional television ratings, Nielsen is essentially working to maintain the centrality of traditional ratings in the audience marketplace. The underlying message of these studies is that the real value in social TV analytics is as a tool to better understand how to use social media to enhance traditional television ratings.

The latest update to NTTR provides an additional example of the alignment of new approaches to audiences with the old, as Nielsen is now releasing demographic data as an accompaniment to programming-related tweets. Demographics are the traditional method for parsing television audience metrics, and so in the social media space as well, Nielsen now offers the age and gender of tweet authors and readers (Friedman 2014a). This development is significant in that it connects the new social-media-based performance metrics with the traditional demographic categories that have served as a source of value under the legacy exposure-based market information regime, in an effort to maintain continuity in the institutionalized (and long-criticized) practice of valuing audiences on the basis of their demographic characteristics.

In some ways, these activities are evocative of Winston’s (1986, 18) “law of the suppression of radical potential,” which he compellingly applied to a range of new media technologies. The “suppression of radical potential” refers to the consistency with which institutional forces (such as large media organizations with a vested interest in maintaining the status quo) have effectively limited the extent to which new media technologies can realize their full potential to disrupt established conditions (Winston 1986). In this case, Nielsen seems to have effectively blunted the full disruptive potential of social TV analytics through a variety of strategic maneuvers. Although, it also may be the case that social TV analytics have simply not been embraced by enough marketplace participants as an improvement over the status quo, or that embracing them would be too disruptive to other participants in the audience marketplace.

In sum, the process of reinstitutionalization, in this case, appears to be one in which an additional market information regime, which perhaps had the potential to dislodge the incumbent, appears unlikely to do so. Rather, it occupies a secondary position in the audience marketplace. Indeed, TV industry stakeholders appear to have embraced an additional source of audience value, while also not abandoning the legacy market information regime.

Conclusion

This article utilizes institutional theory as a framework for exploring the process surrounding the introduction and potential adoption of a new audience measurement system. As illustrated, the process thus far appears to correspond quite well with Greenwood et al.’s (2002) theoretical framework for the process of institutional change. However, the transformation in how audiences are measured and valued in the television audience marketplace is ongoing; thus, this analysis provides a perspective on a transformation process that has yet to fully resolve itself.

Based on the trajectory exhibited thus far, the final outcome is speculated to be one in which social TV analytics operate as a supplementary market information regime to the traditional, exposure-based Nielsen ratings that have longserved as the dominant currency in the television audience marketplace. These conclusions illustrate that the television industry is, perhaps for the first time in its history, at a point in its evolution at which it can—and perhaps even must—embrace additional value criteria beyond those reflected in traditional Nielsen ratings. Historically, multiple value criteria arise during times of industry turbulence (Stark 2009), and so emerging co-existence (even if only in a supplementary capacity) of social TV analytics with traditional Nielsen ratings provides a powerful indicator of the extent of the turbulence and disruption that has characterized the television industry in recent years.

One point that has arisen within the context of this analysis that has received little attention in the institutionalization literature is that the process of institutionalization of a market information regime occurs in both the norms/procedures and the organizational dimensions (see above) of our understanding of institutions. Institutionalization within the norms/procedures dimension involves the legitimization and adoption of social TV analytics as an alternate, but accepted, means of conceptualizing, measuring, and valuing the television audience. The organizational dimension refers to the question of which organization will be the main provider of this information.

As noted earlier, a key characteristic of market information regimes (particularly in the realm of audience measurement) is the dominance of very few—and often only a single—provider, to ensure that all marketplace participants are operating under the same perceptions of current marketplace conditions and performance. The norms/procedures and organizational dimensions are, to some extent, interconnected, in that the presence of multiple competing providers of a certain type of market information can potentially undermine the institutionalization of the value criteria they are measuring and reporting, due to the uncertainties and inefficiencies marketplace participants face in navigating such a complex landscape of market information providers. As such, marketplace participants considering embracing a new market information regime may be discouraged if they are confronted with multiple, competing—even conflicting—sources of this market information, each of which carries its own access costs. Indeed, it may be the case that the supplementary market information regime role that social TV analytics appear to be filling in the television audience marketplace is a result of the prevalence of multiple competing, conflicting data providers. However, consolidation does appear to be taking place at the organizational level, with many upstart social TV analytics firms being purchased by either social media platforms (e.g., Twitter’s purchase of BlueFin Labs) or incumbent measurement firms (e.g., Nielsen’s purchase of SocialGuide), going out of business, or shifting their focus. And, in the end, as this analysis has illustrated, Nielsen appears to have successfully negotiated the turbulent environment in such a way that, despite the fact that additional value criteria (and associated measurement systems) appear to be taking hold in the television audience marketplace, the company has maintained its dominant institutional position.

As the industry and marketplace continue to evolve, there are a number of avenues for future research. Specifically, future research should focus on understanding the cultural implications of this apparent reconfiguration of television’s market information regime landscape and thus, the audience commodity. Even if social TV analytics are ultimately going to be limited to serving in a supplementary capacity, the cultural consequences of their usage could still prove to be profound, affecting television production in significant ways. For example, the institutionalization of social TV analytics has the potential to enhance content diversity in television production, broadening the range of types of programs that constitute “hits” beyond those types that attract the largest numbers of eighteen- to forty-nine-year-old viewers. Alternatively, the net effect could be a narrowing of focusing on producing only those types of programs that appeal to the types of audiences that actively engage in social TV activity around television programs. The net effect (if any) on television program diversity of the institutionalization of this supplementary market information regime has yet to be determined.

Toward these ends, future research should engage in comparative analyses of the characteristics of “hit” programs across the two market information regimes. Are the social TV “hits” lists more or less diverse (in terms of genre, programming source, audience base, etc.), or more or less volatile, than their traditional ratings counterparts? Answering such questions could provide insights into which types of genres or program sources might become more prominent as a result of the institutionalization of social TV analytics, into whether content diversity and service to specialized audience interests might increase or decrease, and into whether these systems introduce more or less stability and/or uncertainty into the process of forecasting audience behavior.

Future research should also engage in a comprehensive assessment of the methodologies via which social TV analytics data are produced, to identify procedures or assumptions that might affect the representation of different types of television content, and thus the preferences of different types of television audiences. Just as it has been found that certain audience segments and content types are better served by certain means of measuring television audiences than others (Napoli 2011), it may also be the case that the methodological choices being made in the production of social TV analytics may have similar cultural repercussions. Finally, a comprehensive understanding of a new market information regime can only be achieved by also examining how it is being used. How, specifically, are social TV analytics being integrated into, or disrupting, programming and media-buying routines? How well do content providers and media buyers understand these metrics and how they are produced? Ethnographic research that explores these questions could further enhance our understanding of the ongoing evolution of the television audience marketplace.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Allie Kosterich is a Doctoral Student in the School of Communication & Information at Rutgers University. She is interested in the intersection of organizations, institutions, and digital technology, particularly within the context of media industry evolution.

Philip M. Napoli is a Professor of Journalism & Media Studies in the School of Communication & Information at Rutgers University. Professor Napoli’s research focuses on media institutions and policy. His books include: Audience Evolution: New Technologies and the Transformation of Media Audiences (Columbia University Press, 2011); Audience Economics: Media Institutions and the Audience Marketplace (Columbia University Press, 2003); and Foundations of Communications Policy: Principles and Process in the Regulation of Electronic Media (Hampton Press, 2001).

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