Skip to main content
Social Sci LibreTexts

6.2: Audience Measurement and Bundling

  • Page ID
    209714

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

    ( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\id}{\mathrm{id}}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\kernel}{\mathrm{null}\,}\)

    \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\)

    \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\)

    \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    \( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

    \( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

    \( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vectorC}[1]{\textbf{#1}} \)

    \( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

    \( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

    \( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)

    Introduction

    The term "audience measurement" refers to the goal-oriented process of collecting, analyzing, reporting, and interpreting data about the size, composition, behavior, characteristics, and preferences of individuals interacting with particular media brands or products.

    Historically, journalists and journalistic organizations had only very crude measures of what news audiences were interested in — and how (and to what extent) they were engaging with that content. For example, journalists would often turn to their friends and family, or perhaps to letters to the editor, for cues about what people were interested in and how their work was resonating with audiences. Journalistic organizations, in turn, would hire consultants to conduct focus groups, survey their readers, or ask broadcast audiences to keep a diary of the programs they watched.

    Those methods came with significant limitations. First, they only provided partial data because they drew on small samples of people, who were often sampled in ways that made it hard to generalize findings to an entire audience. Second, and perhaps more importantly, the information was self-reported. This meant that people might say they wanted more information about international affairs because they thought that’s what they should say — after all, most of us want to seem cultured, even to strangers — when in fact the news they craved was information about Ryan Gosling’s latest film.

    Audience Analytics and Metrics

    The digitization of news has significantly changed how audience interests and consumption are measured. Specifically, digital systems enable passive, mass tracking. This means that when a person accesses a story, the infrastructure helping to serve that content — that is, the computer systems belonging to the journalistic organization and, often, other companies as well — will automatically record the fact that the content was accessed. These systems also often record additional information, including when that person accessed the content, where (roughly) they accessed it from, on what device, and how much time they spent with that content.

    Those systems and information aggregation efforts are often called audience analytics, which is effectively a form of audience measurement that was not possible before the internet age. While it comes with its own limitations — for example, this system alone cannot give journalists a clear picture of how people feel about the content they access — it differs from past approaches in that it can gather information about all members of the audience, and that information is not limited to what audiences want to report. It is a more complete record, quantitatively speaking.

    These systems can be used to automatically personalize content by linking it to past records of a news consumer’s behavior. For example, if the journalistic organization’s tracking systems know a specific audience member frequently accesses content about Ryan Gosling, it may choose to put that content in more prominent positions on its website (or suggest it as the next article for this user to read) because the system infers from past data that this individual wants to stay on top of news about Ryan Gosling.

    Additionally, those systems produce what are often called audience metrics, or aggregate measures about the audience. These include the number of unique people who were exposed to a particular piece of content, where those individuals came from (not just geographically but also the website or platform that led them to that content), and how much time the average person spent with that content, or perhaps even how far the average person scrolled down the page. Thus, a journalist or journalistic organization can have a more quantified sense of how many people read their story and how they interacted with it, instead of just assuming a lot of people did because their group of friends, who likely share the same interests, found it interesting.

    Journalists and newsrooms historically marginalized audience measurement data because they often viewed it as an intrusion on their journalistic autonomy and independence. Put another way, drawing on their role orientations and occupational ideology, they would often believe they had to give audiences certain kinds of news — regardless of how popular it might turn out to be — because it was a civic necessity to do so.

    While there was always some tension over this, the high profitability of journalism made it easier for journalists to resist perceived intrusions in the past. The combination of these new technologies and the economic challenges faced by commercial media in recent years have resulted in even greater pressure to use audience analytics and metrics to more efficiently cater to audience desires — and made it riskier for journalists to resist such pressures.

    Such systems and information do not exist to solely further economic objectives, though. Audience analytics and metrics can and arguably should be used to find ways to better understand what audiences want in order to make civically important content more appealing to them — whether in terms of its substance or simply how and where it is presented, as well as to encourage greater audience engagement and loyalty. Additionally, researchers have found little evidence that highly professionalized newsrooms like The New York Times and The Guardian are blindly making news decisions based on audience metrics alone. Nevertheless, it has become apparent that these technologies and cultural artifacts have changed how journalists think about their work and the ways in which they perform it.

    Bundling and Journalism

    For much of its history, commercial news media has been a bundled product. What this means is that a person rarely bought a single piece of news, or even just news. Instead, they bought a single product that included local news, national news, sports news, and arts news — as well as comics, classifieds, and advertisements. This allowed journalism to be produced in an efficient way insofar as it allowed journalistic outlets to make money from two mutually dependent sources — audiences and advertisers — with a single media vehicle (e.g., a newspaper). Classifieds are emblematic of this: A local business would pay the journalistic outlet a fee to list a job opening in the newspaper while local citizens would pay the outlet for the cost of the newspaper to find a new job openings. A similar arrangement existed for engagement announcements and obituaries, which were also bundled in.

    Content that was cheaper to produce (e.g., post-game reports from local high school football games) also helped subsidize more expensive content (e.g., an investigative series on local corruption). Put another way, citizens often bought the newspaper because they cared about their local sports teams and would perhaps stick around for, and benefit from, the investigative series. The journalistic organization, for its part, tended to see the investigative series as more central to its mission and as a potential status marker — such stories are usually the ones that receive major journalism awards — and viewed its cheaper and more popular content as a way to pay for it.

    This dynamic has changed considerably in recent years. Audiences are now less likely to go directly to a journalistic outlet’s homepage or app, and they are far less likely to seek out a single source to satisfy all of their information needs. Put differently, an individual may go to The Boston Globe for coverage of regional politics and policy, to BuzzFeed for entertainment news, to a local sports enthusiast’s blog for analysis of high school football, and to the British Broadcasting Corporation, or BBC, for coverage of international affairs. As such, news has become unbundled in many ways as journalistic outlets place all of their news online for free or under a 'soft' paywall knowing that individuals will only access some of the content. That, in turn, results in advertising revenue only being generated for those things that are accessed, putting pressure on commercial outlets to focus on narrower sets of content that can pay for itself.

    Moreover, journalistic outlets have lost their monopolies on some of those key dual-channel revenue sources. For example, people now go to websites like Craigslist and Indeed for classifieds, and to Facebook to discover who is getting engaged (and perhaps who has died). There is also a plethora of free and paid entertainment alternatives that far exceed what journalistic outlets have ever been able to offer.

    Because of this evolution in the news industry, the structural advantages and subsidies that enabled commercial journalism to operate as it did in the past no longer exist in such advantageous ways.


    Key Takeaways

    • Journalistic outlets have always tried to measure different aspects of their audiences and their audiences' wants, but audience analytics and metrics have enabled more quantifiable measurements of individual audience members and of audiences as a whole.
    • There is now great economic pressure on journalistic outlets to make use of audience metrics in guiding editorial decisions. However, professionalized newsrooms still draw heavily upon their conceptions of newsworthiness when making those decisions.
    • Journalistic products are no longer bundled in the ways they were before. This has both reduced their ability to subsidize expensive, civic-minded news through cheaper, more popular content and reduced the opportunities to generate revenue from non-news content.

    This page titled 6.2: Audience Measurement and Bundling is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Rodrigo Zamith via source content that was edited to the style and standards of the LibreTexts platform.