by Cory Barker
Importantly, this study considers these reactions while looking at a specific audience of Netflix: 13- to 33-year-old users, also commonly known as “millennials.” Previous research has indicated that millennials are the largest and most coveted demographic group for media producers. However, relatively little is known about their relationship to narrowcasting, and previous research has made predictions regarding their ability (or lack thereof) to be critical to media platforms and suggested content. Ultimately then, this study seeks to add to our knowledge of this rising group and their relationship to a growing media trend.
Netflix, Algorithms and Data Mining
Years after the 2006 Netflix Prize, Vice President of Product Innovation and Personalization Algorithms, Carlos Gomez-Uribe, revealed that the company employs more than 800 engineers responsible for developing recommendation algorithms and maintaining the personalization of the site.4 At the heart of the operation are data algorithms, which analyze user behavior in an effort to make specific content recommendations. This requires that user actions are recorded and then analyzed. Netflix Engineer Director Xavier Amatriain states:
We know what you played, searched for, or rated, as well as the time, date, and device. We even track user interactions such as browsing or scrolling behavior. All that data is fed into several algorithms, each optimized for a different purpose. In a broad sense, most of our algorithms are based on the assumption that similar viewing patterns represent similar user tastes. We can use the behavior of similar users to infer your preferences.5
While Netflix uses individualized datasets to make recommendations, these are often based of larger trends among its 40 million users. Most of the data collected is used primarily for internal purposes to deliver personalized content to each account; however, the large amount of data also reveals overall trends. For example, Netflix engineers learned that while personal ratings of each movie are important, they are more likely aspirational rather than reflective “of daily activity.”6 Therefore, the majority of recommendations are based on actual content viewed and the searching/browsing patterns of users, rather than what is explicitly noted by individuals. This is reinforced by research on “algorithmic culture” that argues algorithms and other digital information archiving is more indicative of users’ true identity than what they articulate on digital media.7 The larger practice of collecting user information is known as “data mining,” a term used by social and information scientists in their practice of identifying, collecting, and then analyzing big data.8 The desire for data mining develops from demands to thoroughly investigate how users interact with new technology, media, and digital platforms that are now commonplace in the twenty-first century. As such, data mining has received much attention and debate surrounding its use and role in user privacy.9
This debate shows up frequently in conversations surrounding Netflix. Without a doubt, Netflix has revolutionized how site data is collected and analyzed. The Netflix Prize was aimed at doing just that. However, this collection of data is also viewed by some as an invasion of privacy, or as a way to hide the recording of specific user behaviors.10 Despite requiring all users to agree to an End User License Agreement spelling out the company’s collection and recording rights, a growing group of critics argues that these practices are problematic and potentially harmful to both users and the greater media industry. Felix Salmon of Reuters suggests that while Netflix states this data collection is used for making personalized recommendations, the actual content provided on the instant-streaming platform challenges this belief.
The original Netflix prediction algorithm—the one which guessed how much you’d like a movie based on your ratings of other movies—was an amazing piece of computer technology, precisely because it managed to find things you didn’t know that you’d love. More than once I would order a movie based on a high predicted rating, and despite the fact that I would never normally think to watch it—and every time it turned out to be great. The next generation of Netflix personalization, by contrast, ratchets the sophistication down a few dozen notches: at this point, it’s just saying “well, you watched one of these Period Pieces About Royalty Based on Real Life, here’s a bunch more.” Netflix, then, no longer wants to show me the things I want to watch, and it doesn’t even particularly want to show me the stuff I didn’t know I’d love. Instead, it just wants to feed me more and more and more of the same, drawing mainly from a library of second-tier movies and TV shows, and actually making it surprisingly hard to discover the highest-quality content.11
Salmon indicates that Netflix’s data mining is falsely described as making the user experience better, when in reality it actually hinders the user’s ability to see desired content. These concerns echo similar debates surrounding the emerging trend of narrowcasting.
Narrowcasting
In the early 1960s and 1970s, those studying television and the growing cable industry spoke frequently about the potential for content specialization and personalization of the television experience for every audience.12 Although still early in its theoretical foundation, narrowcasting began to take shape as an ideal and goal for future cable businesses that sought to deliver individualized content to small segments of the public. Whereas early business models of cable desired to deliver generalized content to large segments of the population, the drive towards maximizing profits lead business leaders to dream of creating specialized series, movies, and television experiences for each audience member. Despite this optimism for the future, narrowcasting as a practice developed in film and television in the 1980s, decades after the original vision.13
Narrowcasting is defined as the process of reaching audiences by identifying facets of users’ identity to target programing and genres. This is the process of creating or displaying content in a way that targets small, specific portions of the audience. Throughout the late 1980s and 1990s, networks and advertisers turned their attention to producing specific televised content for targeted audiences. Economically, the goal was to identify a small subset of the larger audience, research and build an understanding of their needs and interests, and then create content specifically for those groups. In return, these groups were thought to be more loyal, more attentive and more willing to share messages from those series and media producers. Narrowcasted content was levied as a way to forge stronger bonds between media producers and small subsets of the population, thus encouraging the perception of a one-to-one relationship emerging within the mass medium.
Furthermore, early proponents of narrowcasting argued that this trend could assist producers in developing programing that showcased underrepresented or marginalized groups.14 Beretta E. Smith-Shomade notes that narrowcasting was “heralded as a viable prescription for global understanding,” a path for producers to include previously taboo or unpopular topics and genres for smaller, accepting audiences.15 Likewise, Susan Tyler Eastman, Sydney W. Head, and Lewis Klein add that narrowcasting emerged as a viable and economically appealing trend because the practice acknowledged that some facets of the population were currently underrepresented or left out of mass-appeal broadcasts.16 Narrowcasting reinforces previously successful economic models of identifying unmet needs of a population and subsequently creating and tailoring services to that group.17 As a result, narrowcasting is boasted as a democratic part of the future of media, one where more groups are represented, and more space is created for a variety of audiences (including those traditionally left out or ignored).18
However, narrowcasting has also been critiqued for its inability to live up to the democratic promises first associated with its integration into media production models. Despite its ability to recognize and build programing for parts of the population who are traditionally left out, researchers argue that the audience is still spoken of as a homogenous group, especially considering how ratings are calculated and advertising dollars are spent.19 The priority, particularly when it comes to television, is still to generate small numbers of programs that target large facet
s of the population, rather than many programs that target small facets of the population.20 As a result, narrowcasting’s presence in traditional media formats, including television, radio, and film has been subject to critique from academics and industry professionals. Many acknowledge that although tailoring media content to specific segments of the population is a regular practice, the democratic potential of narrowcasting has not yet been met.21
However, narrowcasting’s appeal is not limited to traditional media formats; today’s digital media is filled with examples of platforms, websites, and services designed for and dedicated to small, specific audiences. Peter Ludes notes that in digital technologies and media, narrowcasting is often associated with audience fragmentation, where audiences are broken up into smaller subsets.22 This results in tailored advertising campaigns to those small audiences, thus making the small groups more valuable than large, diverse ones. Although strikingly similar to narrowcasting in description, researchers have identified two ways that fragmentation differs. First, narrowcasting is related to content creation for entertainment and informational purposes. Fragmentation relates to advertising or narrowing down audiences for delivery to an outside content producer (generally in marketing).23 Second, fragmentation is the result of identifying small subsets of the population, while narrowcasting is the process of separating audiences from each other.24 While each practice might reinforce or affect the other, they are viewed as two distinct practices.
A frequent area of inquiry in narrowcasting in digital media has been looking at how audiences are identified and targeted in political campaigning.25 These studies emphasize that online platforms including social media, tracking software, and electronic communication enable major digital organizations to record substantial information about users, and sell large datasets to advertisers, who then can create marketing materials (e.g., ads and pop-ups) tailored to specific groups identified as possible supporters.26 Digitally, narrowcasting has been identified as a global paradigm shift in economics and production.27 Five of six of the largest, most successful international media conglomerates have adopted some type of narrowcasting strategy in their creation of digital content, including Internet and mobile television.28 This includes the creation of specialized websites, advertisements, and videos available to audiences based on their user profiles and other collected information from their digital experiences. Helen Wood notes that the prevalence of narrowcasting in digital content has created a demand for new ways of identifying and measuring audiences.29
This has also resulted in increased revenue for digital media providers who implement narrowcasting into their platform. TechWeb argues that revenues from narrowcasting increased 90 percent from 2005 to 2009, and are continuing to rise each year.30 Advertisers are willing to pay for their content to be delivered to smaller audiences because the rate of success is higher. Similarly, platforms offering narrowcasting platforms such as Netflix and YouTube have seen their own profits increase because of the convenience offered to potential users. As a result, WirelessNews.org named digital narrowcasting its 2007 trend to watch, recognizing that companies that branded themselves as narrowcasting and personalizing content for users were going to become more profitable and popular within the next ten years.31
Millennials and Media
Among the groups narrowcasting sought to identify and create content for, millennials and American youth emerged as a targeted demographic. Although scholars debate the classification of millennials as under-represented in the mass media, without a doubt, new narrowcasting platforms and content such as Netflix sought to attract the coveted demographic. Jennifer Gillan notes that American youth are now used to content and recommendations made based on their demographic data, as this trend emerged long before online and digital streaming.32 American youth growing up in the 1990s found that content was regularly narrowcasted towards them, meaning created to engage and attract the group in an effort to gain popularity and advertising dollars.
The millennial generation was born between 1981 and 2001, and is sometimes called “Generation Y” or “Digital Natives.” One of the largest generational groups in history, the demographic has not escaped academic or mainstream criticism for its relationship to media and digital platforms. A frequent assessment of the group is that its expectation of narrowcasting and targeted media content has produced a narcissistic or self-absorbed generation.33 Marketing Weekly News reported that millennials expect their digital platforms to present recommended content (including news, advertising, and entertainment) that fit with their interests and personality. Narrowcasting is no longer just a technique used to make the platform stand out; it is now an expected part of the media experience for millennials. This in turn, means millennials expect to see programing and recommended titles that reflect their own lives, thus producing a narcissistic label.
Similarly, other research has identified millennials as lacking critical media skills necessary to help them understand long term consequences of framing, agenda setting, and even narrowcasting. D.T.Z. Mindich suggests that as millennials engage with less traditional sources of news (newspaper, radio, and television) and increasingly rely upon new media (Internet-enabled technologies), they become complacent (in comparison to other generational groups) and accepting of content presented to them.34 Rather than seek out information, they are too reliant upon media producers tailoring content to their interests, therefore making them less likely to think negatively or even critically about media practices.
However, other scholars suggest that millennials have found new ways to articulate their uncertainty about the changing media landscape. Although they still rely upon these platforms to provide tailored content, when qualitatively interviewed about their media consumption, they reflect discourses of active disengagement and an understanding of how to critically engage with the content presented to them. For example, Debora S. Vidali notes that millennials are keenly aware that tailored news and platforms present personalized media while leaving out other content deemed not of interest or not likely to be engaged with.35 This awareness manifests when millennials discuss their reactions to media platforms, but is likely ignored in quantitative surveys designed to learn about millennial media habits. As a result, this essay will use qualitative journals as a means of understanding how millennials engage with narrowcasted content and the Netflix platform. In addition to exploring millennials reactions, the study will look for evidence of millennial critique or predictions of long-term consequences.
Methods
To investigate how millennials interacted with Netflix and understood narrowcasting, a journal analysis was completed. Previous research has identified journals as being a successful tool for learning about millennials media habits.36 Nick Couldry, Sonia Livingstone, and Tim Markham have previously examined how journals are useful in understanding the engagement of a user with information media.37 Journals are a critical tool to understand user experiences because they allow for immediate recording of reactions to media content, rather than waiting for an appointment with an interviewer. This decreases the risk of forgetting emotions or changing reactions to fit with an interviewers behavior. Similarly, Laura Harvey used journals to qualitatively study how individuals reacted to legal information.38 Journals are also important in recognizing the contextual experience of media viewing. Niall Bolger, Angela Davis, and Eshkol Rafaeli suggest that journals allow users to describe the contextual experience of a media interaction, such as what else they are thinking about, working on, or paying attention to during a media engagement.39 Ultimately, journals allow users to document experiences, examples, and past stories that relate to the topic at hand.
Respondents were selected for the project based on their enrollment in a general education course at a large northeastern university. The project was offered as an extra credit assignment for students who have a Netflix account and an alternative assignment was provided for students who opted not to participate. In total, 27 millennial-age students par
ticipated and submitted journal entries on their experiences with Netflix. Students were asked to write a one-time journal entry as a response to a Netflix themed prompt. The prompt asked participants to login into their Netflix account and look at recommended titles, content, and categories. Then, they were asked to reflect on these recommendations and the knowledge they had of Netflix’s practices. No prior instruction on Netflix was given to participants, meaning their reflections and information provided was based on individual knowledge and experience.
Specifically, this study looked at how students explained Netflix’s practices and business model. The goal was to identify what information millennials had about the narrowcasting in the platform and what they thought the implications that practice would have on technological development and other facets of society. By providing an open-ended prompt, participants were encouraged to elaborate in a variety of ways including providing examples, making comparisons, or reflecting on long-term effects. As a result, the findings in this study provide insight into millennials understanding and reaction to narrowcasting and the Netflix platform.
Findings
Millennial journal entries revealed that the group is largely critical of the recommendations made through the Netflix platform, as well of the process and data collected by the company to develop targeted programing. Three topics were addressed by the entries and are explored below. These include: (1) identifying Netflix’s method; (2) reflecting on the user experience; and (3) making future predictions about the role of narrowcasting in media.
