2016, Jump Cut: A Review of Contemporary Media
Jump Cut, No. 57, summer 2016
how one company leveraged its big data to change the entertainment industry
In early 2015, Netflix boasted over sixty million subscribers in fifty different countries and announced its plans to be operational in every single country by the end of the following year. The company, as its name suggests, offers its members a large array of film and television content, through either a DVD mail-subscription service or a digital streaming membership for a low monthly cost. While its DVD library is certainly more exhaustive than its digital one, Netflix’s streaming service still offers its members thousands of different titles depending on the region and is so popular in North America that in 2014 it accounted for 34% of all traffic using downstream bandwidth (Govind).
If there is a thesis that summarizes Netflix’s history and development, it is that the company has always been a dangerous game changer for its competition in the entertainment industry. First, its Internet-based movie rental service challenged brick-and-mortar stores, eventually leading to the demise of its primary nemesis—Blockbuster. Now, Netflix is posing a threat to subscription television networks, such as Showtime and HBO, as it has recently begun to commission original content similar in quality and style. However, Netflix Originals have the added advantage of being released an entire season at a time, as opposed to the linear schedule that its competition uses—a major plus for the on-demand generation who loves to watch as much as it wants, when it wants. Furthermore, plans are already in the works for Netflix to challenge movie theaters as the dominant exhibition hub for new release films as the company continues to strike exclusive deals with major Hollywood talent.
A second thesis about the company’s growth and success, however, might consider Netflix’s unusual focus on big data. By definition, big data is data that surpasses the processing capacity of conventional database systems and is received at such a high volume that it requires an elaborate system of collection and analysis to fully understand it. Using programs such as Hadoop, Pig, Python, Cassandra, Hive, Presto, Teradata and Redshift, Netflix is able to process 10+ petabytes of data along with 400+ billion new events on a daily basis in order to learn about its users’ viewing habits (Wylie). This data contains numbers about how many people are using the service at a particular time, what texts those viewers are watching, how viewers rate the programs they’ve watched, and what sort of device they’ve accessed Netflix from (there are over 1,000 options). The company, however, also logs a plethora of surprisingly minute and individualized activity. It tracks at what precise point in a film a viewer paused or stopped watching. It tracks when a viewer fast-forwards or re-winds through a scene, and through algorithms, its data scientists can learn what kind of scene that was (something sexy? something violent? something starring Nordic hunk Alexander Skarsgard?) It also tracks what types of programs subscribers watch on different days of the week, what programs are popular in particular zip codes, and what color browsing poster a viewer is most likely to select from the home recommendation screen.
From Netflix’s standpoint, the collection and analysis of big data is key to its business success. The company depends on being able to deliver a highly personalized recommendation system to its subscribers and to make well-informed content acquisition decisions. But its dependence on big data has also significantly changed the way that media texts are consumed, produced, exhibited, and valued, causing profound disruptions in the entertainment industry. But surprisingly, ittle academic attention has been paid to the role of big data in the entertainment industry. Part of the problem is that companies like Netflix protect their proprietary data by restricting its access to those who work inside of the company (with the exception of the Netflix Prize, as discussed later). Indeed as Boyd and Crawford write, most media companies prohibit access to their data sets by non-employees while others will sell some data for a fee.
“This produces considerable unevenness in the system: those with money—or those inside the company—can produce a different type of research than those outside. …."
"[T]hose without access can neither reproduce nor evaluate the methodological claims of those who have privileged access (673).”
In other words, because Netflix refuses to release its algorithmic formulas and data sets, academic researchers have had very limited access to data about the company’s viewers so as to evaluate how viewer behavior is affecting industry shifts. That doesn’t mean, however, that nothing can be known about relations between big data, Netflix, and industry changes. Indeed, by drawing on tech reports, recent literature, interviews and public talks with Netflix and other industry executives, I explore here big data’s role throughout the major stages of Netflix’s corporate development. My goal is to understand how data collection has helped the company disrupt almost every aspect of the media production cycle and to assess what the potential effects on the industry might be. While my research is necessarily limited by Netflix’s data restrictions, I nonetheless attempt to provide a foundational production-economy perspective on one of today’s game-changing entertainment companies.
In the beginning: big data, red envelopes and Cinematch
Netflix was the brainchild of two Silicon Valley business partners, Reed Hastings and Mark Randolph, who wanted to create “the next Amazon.com of something” in the late 1990s (Keating 13). To many the duo seemed an unlikely pair in the business venture world, with Hastings possessing a colder “supercomputer mind” that thrived on perfecting business plans, while Randolph excelled as a more charismatic leader who used his talents to sell his products to others (15).
Eventually, the pair agreed to develop an Internet-based, movie subscription service that rented DVDs to customers through the mail in its now famous red envelopes. To first get their start-up off the ground, however, Hastings and Randolph had to figure out how to convince customers to abandon their local, familiar video store for one that only existed in cyberspace and took several days to deliver a movie. Their plan revolved around four basic principles: boast the largest selection of videos in the world, let subscribers keep their movies as long as they wanted without ever suffering a late fee, deliver products reliably and quickly, and eventually, create a highly personalized recommendation system that could outperform the video store clerk.
Hastings’ penchant for computer algorithms also meant that big data would be central to Netflix’s business plan from the very start. For example, when the data revealed that Netflix’s ability to deliver a film to consumers within 24 hours had an undeniable correlation to an increased rate of new customer sign ups in that same area, it dramatically changed its distribution plan. Rather than continue to ship all of its DVDs from a single warehouse in San Jose, California, the company developed a software program that plugged in each of its customers’ addresses to see where it should build not just one, but multiple distribution centers to allow for most subscribers to get their DVDs within 24 hours of Netflix shipping the order. At the time, the multi-center fulfillment structure was somewhat radical in the business world, but it ultimately proved a success as the faster delivery times eventually led to word-of-mouth advertising and a reduced cost associated with signing up new subscribers (Keating 56-57).
Such meticulous attention to shipment speeds explains why Gian Gonzaga, Director of Data Science at Netflix, argues that during the mail-order stage of the company’s development, it wasn’t really an entertainment company:
“it was a logistics company that just happened to sell entertainment.”
Its primary business questions centered around which titles it needed to stock, how many of those titles it needed in its inventory, and what the best way was to get those titles to the right person in the right amount of time.
But within three years of going live, Netflix realized that shipping speeds and a growing catalog would simply not be enough. When Netflix first started, it did not provide its viewers with a recommendation feature. All it offered was a search engine that could locate films by keywords, provide links to movie ratings and synopses, and allow users to enter a favorite film in order to find similar titles. When Netflix’s catalog offered less than a thousand titles, this system worked reasonably well. But as their catalog grew, Hastings said a recommendation system became “critical” because “people have limited cognitive time they want to spend on picking a movie” (Thompson). The company also knew that it would need to create an engine that could outperform the video clerk, who was able to give customers ideas about what they might like to watch in order to keep business active.
To do this, Netflix started by asking subscribers who rented a movie to rate it and then used this data to make future recommendations by comparing highly rated films to other ones in their catalog with similar attributes (such as genre, theme or talent). The only problem, according to Hastings, was that this early system gave “a mix of insightful and bonehead recommendations” and ultimately proved ineffective at predicting customer preferences (Thompson). For instance, its engineers could not figure out why Pretty Woman and American Gigolo rarely appealed to the same audiences “even though both were movies about prostitution, starring Richard Gere, and set in a major U.S. city” (Keating 191).
To improve their recommendation system, the company turned to its big data engineers who began to cluster customers together who rated movies similarly and to then present “films highly rated by cluster members to others in the same cluster” (62). By doing this, Netflix’s recommendation system, called Cinematch, could tease out some fairly nuanced connections that few Blockbuster video clerks could ever know. For example, it found that people who enjoy the historical war movie The Patriot also tend to like Pearl Harbor, but it also discovered that those very same folks also like the science-fiction movie I, Robot and the emotional drama Pay It Forward (Thompson).
To make further advances, Netflix placed even more trust in the power of data scientists when it announced an open-door contest in 2006 that offered one million dollars to anyone who could improve the Cinematch system by 10%. In the Netflix world, that translated into consistently predicting a subscriber’s movie ratings to within one half to three-quarters star on Netflix’s five-star system (Keating 187). Over 40,000 teams or individuals from 186 countries joined the three-year contest, using Netflix’s data set of one million subscriber movie ratings to test their equations (188). In the end, a team called Belkor’s Pragmatic Chaos took home the grand prize; they, like the other data miners, had begun to look at relations between movies and viewers in more nuanced ways. While Netflix never actually implemented their final algorithm, the contest produced valuable insights into viewing behavior and algorithm construction. For instance, the competing teams used the data sets to see if subscribers were more generous when rating movies on weekends rather than on weekdays and what effect rating a lot of movies at once had on the process. They were also able to demonstrate that subscribers who liked, say, Woody Allen films, tended only to care for a certain type of his films and did not recommend his other works. Interestingly, they also showed that a small subset of films (that were usually ironic or polemic in nature) were simply beyond reliable predictions. (The most troublesome film proved to be Napoleon Dynamite, as subscribers were sharply divided on whether that film was the product of creative geniuses or mass-produced crap; no model generated was ever able to reliably predict why people would rate it the way they did). (Keating 193)
Today, Netflix’s recommendation system is even more sophisticated and, according to Kelly Uphoff, Director of Experimentation and Algorithms for Growth and Targeting at Netflix, it is now responsible for generating 75% of all movie and TV choices that users make on the site. In part, the system has grown more sophisticated since movies are no longer just tagged by actors, directors, settings or genres. Netflix now hires independent contractors to watch collectively every movie in its catalog and at least three episodes of every TV series. These reviewers then pick from more than 1,000 tags to describe the texts they’ve watched, including its genre, setting, time period, sexual suggestiveness, gore, romance level, mood, plot conclusiveness and even the protagonists’s moral integrity. Netflix then uses these tags to classify its films into micro-genres that are sometimes so specific they border on the absurd. In fact, in early 2014, the company had already generated 76,897 possible micro-genres to recommend to viewers, such as Emotional Fight-the-System Documentaries, Period Pieces About Royalty Based on Real Life, and Children and Family Movies Starring the Muppets (Madigral). (To see the micro-genres Netflix has generated, you can log into your account and then type in the following address: https://www.netflix.com/browse/genre/1. The 1 at the end of the URL is the number that corresponds to a particular micro-genre. In this case, the 1 will link to "African-American Crime Documentaries,” but changing the number at the end of the URL will change the genre it links to. For example, agid=76102 links to “Gritty Zombie Horror Movies.”)
As Alexis Madrigal writes, such a detailed tagging system, when combined with millions of users’ viewing habits, “becomes Netflix's competitive advantage.” This is because the company's main business goal is to gain and retain subscribers, and recommending precise genres to people on their home screen is “a key part of that strategy.” In fact, the company’s data shows that member retention positively increases when it places the most tailored or specific rows of genre recommendations higher on the user’s home screen instead of lower (Madrigal).
In total, the sophistication of Netflix’s Cinematch system, which was driven by the complex analysis of big data sets, helped revolutionize the way people rented movies. The company’s ability to help viewers find films they wanted and to deliver them overnight lured millions of subscribers away from the video store where so many others had failed. Indeed, the arrival of Netflix is largely credited with the demise of Blockbuster, the once prolific chain of brick-and-mortar video rental stores. Its costly retail space, poor customer service, late fees and limited selection simply could not compete with Netflix’s lower-cost model, broader catalog, and highly sophisticated recommendation system. Blockbuster was also too slow to recognize the threat that Netflix posed. With the early failure of Hollywood Video’s e-commerce site, Reel.com, Blockbuster believed that the online video rental business was not a viable threat. “With this reassurance, Blockbuster remained conspicuously slow in its own attempts to develop online strategies” and remained reluctant to invest in a rent-by-mail business model (McDonald). By the time Blockbuster launched its own digital streaming and online rental service in the mid-2000s, a limited marketing budget, long waits for popular DVDs to become available, and a hastily configured distribution system that slowed delivery times crushed customer retention rates (Keating156). In December 2004, Blockbuster also made the “fatal” mistake of eliminating all of its late fees in order to build customer rapport and to directly compete with Netflix, but in the process the company forfeited approximately $400 million in late fees during a cash-strapped period—a financial hole from which it never recovered (McDonald). In the end, the corporate Goliath simply could not compete with the smaller, nimbler David, and it finally filed for bankruptcy in 2010.
Revolutionizing entertainment in the streaming age
While Netflix’s Cinematch would eventually make reliable recommendations, it also allowed the company to mask some of its weaknesses. If a popular title was low in stock, for example, Cinematch would stop suggesting the title until more copies of the DVD became available. The problem of stocking the right of amount of inventory to meet viewer demand was always a challenge. It became exacerbated when Netflix entered its second and third stages of corporate development, which occurred when it launched a video streaming service in January 2007 and began developing its own original content in 2012. These new endeavors profoundly changed the company’s business model by shifting its focus away from that of a logistics company and more towards that of a media channel and studio. As a result, Netflix had to think much more carefully about what its subscribers wanted to watch, which titles it should license for streaming, and which titles it should create.
To better understand the challenge of this shift, consider that during its mail delivery age, Netflix was able to simply purchase physical DVDs, which in essence bought them a license to use those discs in perpetuity. So long as the disc didn’t break, the company could send that movie out to as many people over the course of as many years as it liked. Netflix also did not need to strike complicated deals for its DVD purchasing practices because it was allowed to purchase a disc as soon as it was released to the public, no matter who released it.
Streaming licenses work much differently because they operate under the assumption that (in this case) everyone with a Netflix subscription can watch the title, making a single streaming license for a single work dramatically higher in cost than a set of DVDs (Gonzaga). For example, when Netflix agreed to purchase the streaming rights to all seven seasons of AMC’s dramatic series Mad Men, it paid out nearly $1 million for each of the show’s 92 episodes (Nordyke and Rose). Given that one can purchase all seven seasons of Mad Men on DVD for roughly $70 dollars, Netflix could have purchased over 1.3 million box sets of the series (or roughly 260 million individual DVDs) for the same price without ever having to worry about those discs disappearing from its catalog once the license expired.
In addition to the higher price tag, studios are also much pickier about whom they will release streaming rights to. Also, they tend to sell groups of products together in exclusive deals as opposed to just singular titles. Thus, users in the United States may notice that Netflix carries most Showtime titles in their streaming library, but carries almost no HBO titles, which are only streamed through Amazon Instant Video or HBO Go, even though Netflix can offer titles like HBO’s Game of Thrones through its DVD service. The higher costs of streaming licenses and companies’ preference to bundle titles in exclusive deals means that Netflix’s options are significantly more limited, presenting the company with the challenge of satisfying many different types of viewers with a more narrow catalog since it simply does not have the budget to purchase as widely as it does in the DVD market. To help them navigate these new challenges, the company, of course, still relies on its data scientists, but their job is now perhaps slightly easier.
Using big data for content development
One of the best things about the streaming era from a big data perspective is that users are now doing everything online and every interaction with a site can be logged, tracked and analyzed. In fact, Netflix’s data scientists now argue that people don’t really need to rate movies anymore in order for it to make accurate recommendations for them. According to Gonzaga, the company can now monitor
“what shows and movies you watch and how you watch them to figure out which selections were memorable and how to duplicate that experiences with [other] films available in their streaming library.”
In other words, the company can now analyze a viewer’s behavior to understand that she enjoys watching comedies on Tuesday nights and dramas on Saturday evenings, that she rewatches movies featuring Jodie Foster, suggesting Foster is a favorite actor, and that she stops watching films once high levels of nudity appear. It can then use that data to make reliable recommendations on different days of the week, accordingly.
Such a nuanced level of understanding of its users also allows Netflix to analyze large-scale viewer preferences and use that knowledge to purchase texts that best match subscriber demands – either through content licensing or commissioning original content. The use of big data sets, Gonzaga notes, “set us up to have more success in product development” since, for instance, the company can see when we “have already licensed all the good WWII documentaries and [when] our users have burned through them.” Thus, he stated, when the data scientists see there is a gap in the catalog between what we [Netflix] offer and what our viewers want to watch, we now have the power to actually go and create our own content to fill that gap and feel confident that it will find an audience
Making a WWII documentary, however, is not as financially risky as many of the deals that Netflix has recently engaged in, some of which have seemed foolish at worst, or radical at best – but all of which are changing how film and television series are made. Take for example the company’s recent decision to sign Adam Sandler and his Happy Madison Productions to an exclusive four-year, four-movie deal. Historically, Sandler has been known for his comedies that employ adolescent and physical humor, with his top grossing films including The Waterboy (1998), Big Daddy (1999), Anger Management (2003), and The Longest Yard (2005). But in the last decade, many of Sandler’s films have been considered box office flops, including Blended (2014), which grossed just $46 million over its lifetime, That’s My Boy (2012) with $36 million, and Men, Women and Children (2014) with a very paltry $705, 000. Because of the downward trajectory of his box office pull, the decision to choose Sandler as one of Netflix’s cornerstones of its content development strategy perplexed many.
Nonetheless, Ted Sarandos, Netflix’s Chief Content Officer, believes that the deal is a smart one. Sandler has put out nearly one film each year for the last twenty years, most of which are in Netflix’s catalog. As such, the company had a wealth of data showing Sandler’s movies are “uniquely” popular on the streaming service across all international markets and that his movies tend to be repeatedly watched by subscribers (Kilday). Box office data also shows that while Sandler’s most recent films have tanked domestically, they still have significant global appeal. Sixty percent of Blended’s revenue, for instance, came from overseas, and Pixels (2015), his most recent release, is likely to perform similarly. Sarandos points out that as Netflix grows internationally, it needs to strike content deals with stars who appeal transculturally. Thus, while conventional wisdom holds that U.S. comedy as a genre does not travel well across cultures, in the specific case of Adam Sandler, that data is wrong (“NATPE 2015”). Sarandos believes that his deal with the star will serve Netflix’s service and it emerging markets particularly well. This logic is further augmented by the fact that Netflix will own the rights to each film, meaning the films will remain in its catalog without ever being subject to an increase in price or a license expiration date. Netflix may also sell those movies on DVD or Blue Ray if it ever sees fit.
While it is highly unlikely that Sandler’s films will win Netflix critical awards, the company now has over sixty million subscribers, making it clear that it has the muscle to strike exclusive content deals with prolific stars. This practice may shift feature film development away from traditional studios and theaters. Indeed, the Sandler deal was intentionally designed to upset how films are released because, as Sarandos notes, we now live in an on-demand society that can consume content when and where we want, but the one exception has been movie going. He states that viewers simply do not have enough weekends to watch every movie that is released in theaters. But home viewers still have to wait, on average, between 6 to 12 months before they can watch the content they missed at home. This “antiquated” system of windowing, Sarandos states, works against consumers and Netflix. So in order to change when and how movies can be watched, he had to develop a product that he could control start to finish (“Keynote”).
Consequently, theaters are scared by the threat Netflix poses. When the company announced its very first movie deal in 2014—a sequel to Crouching Tiger, Hidden Dragon—it also revealed its plans to premiere the film on the Netflix site at the same time it would be released in the IMAX format in theaters across the globe. Regal, AMC, Carmike and Cinemark quickly refused to show the sequel on their IMAX screens, as did Canada's Cineplex and Europe's Cineworld. The IMAX film company seemed content to screen the movie mostly in China, where Variety said it would have over 200 locations by the time the movie was released (and where Netflix did not yet offer service), but theatre owners’ refusal to play day-and-date releases is telling (Lang). While Sarandos admits that he struck the deal with IMAX to brand Netflix movies as “big” gorgeous films, rather than small, made-for-TV-movies (“NEXT”), theaters sensed that Netflix’s ability to strike day-and-date deals set a very dangerous precedent since it cuts theater viewers in potentially significant numbers. In order to deter film producers like the Weinsteins from striking these deals with Netflix, movie theatre owners can only boycott the films in order to protect the theatrical screening status quo. But as the Sandler deal demonstrates, Netflix has already found a way to circumvent even an industry-wide boycott by bypassing the theatre system altogether.
As best exemplified by its original television series, House of Cards (2013), Netflix is upsetting the television industry in similar ways. Directed by David Fincher, House of Cards is essentially a modern remake of a BBC series, starring Kevin Spacey as Frances Underwood, a rising politician in the cut throat world of Washington D.C. politics. Fincher had pitched the series to HBO, Showtime, AMC and Netflix, with the latter outbidding its competitors by offering to buy two seasons of the show for $100 million dollars without ever seeing a pilot. In essence, House of Cards was the first television show “developed with the aid of big data algorithms” (Satell). Those algorithms indicated that a healthy share of Netflix viewers had already streamed Fincher’s work from beginning to end, that Kevin Spacey films had always done well on the site, and that many viewers enjoyed the British version of House of Cards.
“With those three circles of interest, Netflix was able to find a Venn diagram intersection that suggested buying the series would be a very good bet on original programming” (Carr).
$100 million dollars, however, is a lot to invest in a project that hasn’t even made it to the pilot stage, and Netflix’s decision to purchase it sight-unseen marked a significant and radical disruption as to how television series are usually developed. In the traditional model, a network will hear several pitches from producers, put up a limited amount of money to develop some of those pitches into pilots, and then choose which of those pilots to actually put on the air in series form. If the show does poorly, the network can cancel the show mid-season. It is a long-standing model designed to reduce the financial risk of television development. Netflix has bucked his system entirely, largely because of the way it wants to distribute original series.
Before Netflix began making its own content, it noticed that streaming subscribers tended to watch the television series in their catalog back-to-back-to-back. This insight was then reinforced by a study it published in The Wall Street Journal in 2013, which revealed that half the users Netflix studied watched an entire season of a television series (up to 22 episodes) in one week. When it then looked at the viewing patterns of subscribers who were watching ten "currently popular" shows on Netflix and finished a season of at least one of those shows within the space of a month, 25% of viewers in the case of one serialized drama “finished the entire 13-episode season in two days, while it took 48% of them one week to do so.” The pace was similar for a 22-episode sitcom, with 16% of viewers finishing the season in two days, while 47% completed it within seven. (Jurgensen).
Netflix interprets this data to mean that the majority of streamers would prefer to have a whole season of a show available to watch at their own pace. The company then revolutionized the television industry by deciding to release its own original series whole seasons at a time, rather than making viewers wait each week for a new episode to become available. Sarandos believes that viewer-controlled, multi-episodic viewing – aka binge watching – is most appropriate for the new digital, on-demand generation. However, he admits that in the beginning, no U.S. network partner would have ever worked with the company to co-produce a series released in such a manner. In order to change how television is distributed, then, as in the case of the Sandler deal, Netflix had to buy a domestic program that it could control start to finish (Next). That program was House of Cards.
Of course, the company’s delivery strategy makes Netflix’s television development deals profoundly more risky because it means they must purchase an entire season of a show before testing its audience appeal. Controlling this risk is why Sarandos admits that “big data is a very important resource to allow us to see how much to invest in a project,” adding that commissioning decisions at the service are “70% science and 30% art” (Martinson). In the case of House of Cards, though, the risk seems to have paid off. The show has already been nominated for thirty-three Emmy Awards and has taken home four, including Best Casting, Directing, Cinematography and Sound Mixing for a drama series. Kevin Spacey and his co-star, Robin Wright, also won the Golden Globe Award for best acting in a television series in 2015 and 2014 respectively. The series’ critical acclaim also put Netflix on the map for original television programming and helped grow subscribers from 27.1 million to 33.4 million in 2013, the year the series premiered. It is highly unlikely that the Adam Sandler deal will earn Netflix similar kinds of accolades, but clearly both House of Cards and the Sandler deal are industry game changers, and Netflix has decided to continue its binge-release strategy for its subsequent programs.
For all of its data acquisition, Netflix has surprisingly refused to release one of its most basic metrics to either its content creators or the public: namely, how many people actually stream Netflix shows or movies. And this refusal is changing the way television content and artists’ values are determined.
On some levels, Netflix’s secrecy makes sense. Because its streaming business offers a subscription-based, ad-free service, it sees little reason to release ratings data to the public. In the United States, viewing numbers are typically collected by the Nielsen Media Company, an independent, third party that tells producers, networks and advertisers how many people watched a particular episode of a program on a given week. The television industry uses this data as a comparative metric to evaluate a program’s success, establish advertising rates and negotiate creative talents’ contracts when up for renewal.
Because Netflix does not earn money from advertisers and because people can watch episodes whenever they want, traditional ratings data means little to the company and thus it sees few reasons to release viewership numbers to the public. But oddly, the secrecy over how many people have watched its original programs extends to the creators of the programs themselves. For example, Beau Willimon, the show runner of House of Cards said,
"To this day, I have no idea how many people have watched [my series] on Netflix. They have never given me any data whatsoever. All they say is, 'Well, we're doing well and we'd like another season.' And that's really all I need to know" (Howard).
Jenji Kohan, the creator of Orange is the New Black, echoed Willimon when she stated that she too had never received viewership numbers, noting she’s only received “cryptic” statements like, “We’re very pleased,” which admittedly she found disconcerting at first (Manly).
While Kohan suggests that she now finds the “darkness” somewhat liberating as she doesn’t have to anxiously track her show’s performance on a weekly basis to see how long it might survive, it’s hard to imagine that the darkness is sheer bliss.
Without knowing viewership numbers, creative personnel have no idea how much to ask for financially when a series is up for renewal. And even if that data were released, the talent and their agents would still be “potentially at the mercy of Netflix who [could] argue that the data says you’re only worth this much’” without the creative side ever knowing if that internal data is accurate since Netflix is the only party monitoring the numbers (Barr). It is unsurprising then that Netflix’s refusal to release its viewing data is forcing Hollywood to adjust to a new way of evaluating a show’s worth.
As Willimon suggests, a successful television program is more than just ratings and shares: “there are a lot of different metrics on which to gauge success now” (Howard). Those metrics may include awards nominations, Twitter chatter, merchandising figures and more. In today’s market, talent agents and managers have to use a new set of data to enter negotiation territory. "In a typical network renegotiation,” said David Fox, who handled renegotiations for Two and a Half Men's Jon Cryer, “you use critical reviews, awards, ratings and ad revenue to support your argument." But with Netflix and other streaming channels, he admits, lawyers are entering unfamiliar territory and the industry needs a new metric: “that's what everyone is looking for right now" (Rose). But when a show lacks buzz or positive reviews, such as the Netflix Original Marco Polo or Hemlock Grove, both of which were surprisingly renewed for a second season, the refusal to release viewership numbers means that reps for these stars lack almost all specific data points on which to argue (Rose), further complicating the renewal process.
As such, Netflix’s refusal to release viewership numbers has prompted new technological developments in the industry. Content creators have now asked third-party monitors, like Nielsen Media Research, to create an opt-in system that independently tracks online platform viewing. According to a high-level Nielsen employee who spoke to me about this issue onbackground, companies like Netflix and Amazon Instant Video have removed the watermarks that would traditionally allow a third party to track viewing through their software. Nielsen has now developed a system that circumvents those companies’ systems, allowing it to track viewership through audio content. More specifically, when a content creator agrees to share the audio files that accompany their episodes or films, the company inputs those files into a signature audio library. Whenever a “Nielsen family” watches one of that creator’s programs through a TV-connected device, its People Meter matchs that audio file to deliver information about when a particular episode is being played by a unique viewer and how long that play lasted.
According to the Neilsen representative I spoke with, Netflix has been reluctant to allow for third-party measurement because keeping its creators in the dark about how many people watch a show works in Netflix’s favor when it comes time to strike a new content deal. The Nielsen service, which content creators buy, helps balance the power dynamic involved in those talks. But the system is not yet perfect. For one, Nielsen cannot monitor streaming on mobile devices (like iPhones) that are not connected to a TV. Likewise, the company cannot measure platform-specific streaming behavior without a company’s cooperation. Therefore, it cannot say how much show X has been streamed on Netflix versus Amazon. However as my source points out,
“If the series is exclusive to Netflix, for instance, Orange is the New Black, then that’s a bit of a no brainer.”
Unless Netflix decides to release its ratings data, systems like Nielsen’s will only become more important. Netflix has recently announced its intent to invest over three billion dollars annually in content acquisition, meaning that more and more creators will be negotiating and re-negotiating with the service, all of them wanting to know just how much they and their properties are worth. However, Netflix may have even found a way around this by purchasing multiple seasons of a series upfront to avoid the re-negotiation problem altogether. To cite just one example, the company just announced its decision to pour $150 million into The Queen, a period drama about the reign of Queen Elizabeth, which is being developed as a six-season deal. Like House of Cards, the initial investment in the property is significant and risky. But in the long run, a multiple-season deal may help to reduce the overall cost of the property, which mostly increases in the traditional model when the talent’s contracts are up for renegotiation.
Big data and the creative process
The final element I will explore has to do with the way big data may influence and disrupt the creative processes that govern entertainment media. To be sure, Netflix’s decision to release whole television seasons at once has encouraged complicated, serial storytelling, which, historically traditional networks have not favored. As Kevin Spacey stated at the 2013 Edinburgh Television Festival, Netflix’s distribution model has encouraged a serial, storytelling that takes “a long time to tell” and possesses “sophisticated, multi-layered” story arcs with “complex characters” who “reveal themselves over time” in relationships “that take space to play out” (Plunkett and Deans). Of course, such narrative complexity did not start with Netflix; ABC’s Lost and AMC’s Breaking Bad are just two examples of serial narratives that aired before House of Cards. But Spacey is right to suggest that Netflix has certainly been one the biggest proponents of serial storytelling because its viewers’ downtime in between episodes is dramatically shorter, meaning that artists can create more complicated storylines without having to worry that its consumers will have forgotten what happened in the last episode. And both viewers and critics alike have positively embraced this creative trend.
In the case of television, Netflix has also freed its artists from the constraints traditional networks face of interrupting programming at specific intervals for ad-breaks. At the very least, network TV has to fit ads neatly within a one hour or half-hour programming time slot. Because Netflix is ad-free and exclusively streamed, its story lengths can vary significantly from episode to episode. For example, the run times of Bloodline, a series developed for Netflix by Sony Pictures Television, range from forty-nine to sixty-five minutes,while the episodes of season one of Orange is the New Black range between fifty and sixty minutes, with the finale of season two going for a full ninety-two. The goal of such loose timing, says Sarandos, is to allow his artists to
“take as much time as [they] need to tell the story well. You couldn't really do that on linear television because you have a grid, commercial breaks and the like” (Jeffries).
While Netflix may create a freer television culture attractive to creators who don’t need to fear their show won’t proceed past the pilot stage, the company’s high levels of financial investment that it pours into its series also begs the question as to how much big data will influence the creation of that programming. How much will computer algorithms begin to dictate what and how artists must create? As Jessica Leber asks,
“Will screenplays some day be written to meet the whims of data-driven media streaming companies? Will an algorithm direct writers to produce content to appeal to niche audience profiles on Netflix?”
And what about the writing that occurs at the episode level? How will big data affect the direction a show takes? Or as Siraj Datoo asks,
“If it knows that a large percentage of viewers ended an episode early when a specific character appeared often, might the company suggest the character is removed from the second season?”
There is not enough information publically available to answer each of these questions, but Netflix executives have stated that computer algorithms indeed help dictate which shows the company develops. In fact, Sarandos has repeatedly stated that Netflix mostly uses big data to figure out how large the market might be for a particular project but not to reverse engineer what that project should look like (“Next”). This sentiment is echoed by Cindy Holland, Head of Original Content at Netflix, who states that the company’s data allows her to
“identify subscriber populations that gravitate around genre areas … [and] allows us to project a threshold audience size to see if it makes for a viable project for us" (Sweney).
Todd Yellin, Vice President of Product Innovation, also notes that the company shares all of its data with its Los Angeles programming team, “to see how it compares to the shows they are thinking about [developing]” and to see if any of those shows might fit the gaps in Netflix’s catalog and user demand (Sweney). Indeed, this process likely guided Netflix’s decision to create a fourth season of Arrested Development after Fox had canceled the show in 2006. Although it’s speculation, many Netflix subscribers who started to watch the first three seasons on the service likely had high and rather rapid completion rates for the series, suggesting high viewer demand and engagement with the text. Thus to offer fans more of the series they loved, Netflix likely commissioned an additional season to meet that demand.
Sarandos also indicates that while the company uses big data to decide how much to invest in a particular series, what series to make, and which stars should be included in the show’s cast, Netflix does not get involved much in the day-to-day writing process. “One of the things I really believe in is picking really great storytellers who have a history of delivering on great storytelling and letting them tell their stories,” adding that Netflix executives only guide their creators “with a very gentle hand” (“Keynote”). When pushed at the 2015 Cannes Film Festival as to what a “gentle hand” or “light touch” means in Hollywood, Sarandos admitted that his team will often serve as a tie breaker if there is a disagreement between creative members about what works best for a show. And they will offer guidance on issues such as how much to move a character up in the story or how quickly to resolve a story element (“NEXT”). Cindy Holland, however, has stated that one of the biggest misconceptions about Netflix is “the perception that my team isn't creatively involved in [an original] series at all.” She sums up the relation like this:
“On the one hand, we have a very light touch … our culture is about freedom and responsibility, and we believe that people do their best work when you give them both. But on the other hand, I'd like to acknowledge the amazing hard work my team does. So, that's always a bittersweet comment that we hear.”
The issue is further complicated by a story Ted Sarandos recently told at an industry conference. In it, he explains that in the very first minute of House of Cards, Kevin Spacey’s character does two important things: he kills a dog and he talks directly to the camera while he does it. Early watchers of the episode reacted negatively to this scene, as evidenced by the fact that several stopped viewing the program in the first few minutes. One of two things were happening, he noted: either viewers were turned off by the unusual use of second-person; or viewers were not expecting something so startling in the first minute and thus may have decided to stop and then return to the show, when, for example, their kids were no longer in the room. “My job was to figure out which one it was, and then to ignore them both.” Any regular television network, he stated, would have cut that first scene based on the viewing data Netflix received, but he argues, Netflix kept the scene as it was to maintain the artist’s freedom and integrity (“Keynote”).
The fact that Sarandos admits that his job is to figure out why people tuned out of House of Cards suggests that this data is important and will indeed affect which direction the show takes in the future. If, for instance, it turns out that viewers were turned off by the use of second person, it would stand to reason that the second and third seasons would employ less of the device (as they actually did.) For now, however, there is not enough information that is publically available to determine how much the algorithms are influencing the writing of a series, especially in subsequent seasons, or the ways in which algorithms will prove to be vastly different in comparison to other, more traditional types of audience focus groups.
Finally, critics have wondered what the big data approach will mean for the diversity of television shows.
“If businesses perfect the job of giving us exactly what we want, when and how will be exposed to things that are new and different, the movies and TV shows we would never imagine we might like unless given the chance? Can the auteur survive in an age when computer algorithms are the ultimate focus group?” (Leonard).
As more and more media companies develop online delivery platforms, the collection and reliance on big data will only increase, and these questions will only grow more prominent. Yet, as Sarandos argues, and it’s a comment I agree with, the use of data algorithms has so far generated content that has added to rather than detracted from the diversity of the television landscape. For instance, Orange is the New Black is a prison-based dramedy with a predominantly female cast and is one of the most ethnically and physically diverse shows on television. Likewise, Narcos is set in Colombia, is directed by the Brazilian José Padilha and stars another Brazilian, Wagner Moura, in the lead role. As Netflix develops more stories to succeed in the global market, as well as in specific territories such as Latin America, viewers will have greater exposure to internationalized texts through their Netflix subscriptions.
According to Neil Hunt, Chief Product Officer at Netflix, his company spends roughly $150 million and employs roughly 300 people just to improve its video recommendation system each year, but the investment in the utilization of big data is what drives so much of the company’s success (Roetggers). Its use of big data to increase its shipping speeds, make personalized video recommendations and effectively stock its catalog helped the company succeed in the online movie rental business, ultimately playing a pivotal role in the demise of its major competitors. As the company moved into the digital streaming age, Netflix’s data scientists have continued to leverage that data in order to make smarter content acquisition and distribution choices, but in the process, the company has also disrupted several aspects of the film and television industry. More specifically, its business models have caused a shift in how media texts are valued, how contracts are negotiated, how texts are purchased, and how those texts are consumed and distributed in ways that by-pass or challenge the status quo.
As PM Napoli writes, the challenge for media scholars now is to further explore how media content production and its acquisition is increasingly reliant on sophisticated algorithms. The challenge will be for media scholars to examine not only what sorts of algorithms are being produced and how they are affecting the decision making processes of media companies, but also to understand “the assumptions, priorities, and inputs that underlie their construction.”
Acknowledgement: Thank you to Matthew Hargus for his feedback on the initial draft of this piece.
1. Netflix had released a 2012 television series, called Lilyhammer, on its site before House of Cards. Lilyhammer, however, was also released on Norwegian broadcast television and was not heavily influenced by the service in terms of its creative direction.
Barr, Merrill. “Can Netflix Thrive Without Reporting Ratings and Value.” Screenrant.com. Jan 28, 2015. Web. Sept 2, 2015. http://screenrant.com/netflix-tv-ratings-originals-contracts/
Boyd, Danah and Kate Crawford. “Critical Questions for Big Data.” Information, Communication & Society 15.5 (2012): 662-679. Web.
Carr, David. “Giving Viewers What They Want.” New York Times. Feb 24, 2013. Web. Sept 20, 2015. http://www.nytimes.com/2013/02/25/business/media/for-house-of-cards-using-big-data-to-guarantee-its-popularity.html?_r=0
Datoo, Siraj. “How Netflix Uses Your Data to Work Out What You Want It to Commission.” The Guardian. March 7, 2014. Web. Sept 15, 2015. https://www.theguardian.com/media-network/media-network-blog/2014/mar/07/netflix-data-house-cards
Gonzaga, Gian. “Building a Big Data Culture in the Entertainment Industry.” Data Science Innovation Summit. San Diego, CA. Feb 12, 2014. Lecture.
Govind, Nirmal. “Entertainment Science at Netflix.” Big Data Innovation Summit. Boston, MA. Sept 25, 2014. Lecture.
Howard, Annie. “'House of Cards' Creator Beau Willimon: "I Have No Idea How Many People Have Watched the Show on Netflix.’" The Hollywood Reporter. June 14, 2015. Web. Sept 1, 2015. http://www.hollywoodreporter.com/news/house-cards-creator-beau-willimon-801280
Jeffries, Stuart. “Netflix's Ted Sarandos: The 'Evil Genius' Behind a TV Revolution.” The Guardian. Dec 30, 2013. Web. Oct 4, 2015. https://www.theguardian.com/media/2013/dec/30/netflix-evil-genius-tv-revolution-ted-sarandos
Jurgensen, John. “Netflix Says Binge Viewing is No 'House of Cards.'” The Wall Street Journal. Dec 12, 2013. Web. Sept 1, 2015. http://www.wsj.com/articles/SB10001424052702303932504579254031017586624
Keating, Gina. Netflixed: The Epic Battle for America's Eyeballs. New York: Portfolio, 2013.
“Keynote: Ted Sarandos Netflix - MIPCOM 2014.” Online Video Clip. Youtube.com. Oct 14, 2014. Web. Sept 3, 2015. https://www.youtube.com/watch?v=4QRI8wIum00
Kilday, Gregg. “Netflix's Ted Sarandos Explains Adam Sandler, 'Crouching Tiger' Deals: "Putting Our Money Where Our Mouth Is.” The Hollywood Reporter. Oct 3, 2014. Web. Sept 1, 2015. http://www.hollywoodreporter.com/news/netflixs-ted-sarandos-explains-adam-737840
Lang, Brent. “As Theaters Boycott Netflix, Collapsed Windows Seen as Inevitable.” Variety. Sept 30, 2014. Web. Oct 1, 2015. http://variety.com/2014/film/asia/as-theater-chains-boycott-netflix-collapsed-windows-seen-as-inevitable-1201317673/
Leber, Jessica. “House of Cards and Our Future of Algorithmic Programming.” Technology Review. Feb 26, 2013. Web. Oct 1, 2015. https://www.technologyreview.com/s/511771/house-of-cards-and-our-future-of-algorithmic-programming/
Leonard, Andrew. “How Netflix is Turning Viewers into Puppets.” Salon.com. Feb 1, 2013. Web. Sept 2, 2015. http://www.salon.com/2013/02/01/how_netflix_is_turning_viewers_into_puppets/
McDonald, Kevin. “Digital Dreams in a Material World: The Rise of Netflix and its Impact on Changing Distribution and Exhibition Patterns.” Jumpcut 55 (2013). Web. Oct 1, 2015. http://ejumpcut.org/archive/jc55.2013/McDonaldNetflix/index.html
Madrigal, Alexis. “How Netflix Reverse Engineered Hollywood.” The Atlantic. Jan 2, 2014. Web. Sept 1, 2015. http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/
Manly, Lorne. “Jenji Kohan, Creator of ‘Orange Is the New Black.’” The New York Times. Dec 26, 2013. Web. Sept 1, 2015. http://www.nytimes.com/2013/12/29/arts/television/jenji-kohan-creator-of-orange-is-the-new-black.html
Martinson, Jane. “Netflix’s Ted Sarandos: ‘We Like Giving Great Storytellers Big Canvases.’” The Guardian. March 15, 2015. Web. Sept 1, 2015. https://www.theguardian.com/media/2015/mar/15/netflix-ted-sarandos-house-of-cards
Napoli, PM. “On Automation in Media Industries: Integrating Algorithmic Media Production into Media Industries Scholarship.” Media Industries 1.1 Web. Oct 1, 2015. http://www.mediaindustriesjournal.org/index.php/mij/article/view/14
“NATPE 2015: A Chat with Ted Sarandos, Mitch Hurwitz, and Vince Gilligan.” Online Video Clip. Youtube.com. Jan 21, 2015. Web. Sept 1, 2015. https://www.youtube.com/watch?v=Zdy8-FDV7c0
“NEXT: In Conversation with Ted Sarandos.” Marche du Film. Online Video Clip. Youtube.com. May 15, 2015. Web. Sept 20, 2015. https://www.youtube.com/watch?v=pm7_2sSYP2g
Nordyke, Kimberly and Lacey Rose. “Netflix Spends $1 Million Per Episode of 'Mad Men' for Streaming Rights.” The Hollywood Reporter. April 5, 2011. Web. August 30, 2015. http://www.hollywoodreporter.com/news/netflix-spends-1-million-episode-175019
Plunkett, John and Jason Deans. “Kevin Spacey: Television Has Entered a New Golden Age.” The Guardian. Aug 22, 2015. Web. Sept 15, 2015. https://www.theguardian.com/media/2013/aug/22/kevin-spacey-tv-golden-age
Rose, Lacey. “How Much Is That Netflix Show Worth? Stars Want to Know (Analysis).” The Hollywood Reporter. May 1, 2014. Web. Sept 1, 2015. http://www.hollywoodreporter.com/news/how-is-netflix-show-worth-699297
Roetggers, Janko. “Netflix Spends $150 Million on Content Recommendations Every Year.” Gigaom.com. Oct 9, 2014. Web. Oct 4, 2015. https://gigaom.com/2014/10/09/netflix-spends-150-million-on-content-recommendations-every-year/
Satell, Greg. “What Netflix's 'House of Cards' Means For The Future Of TV.” Forbes.com. March 4, 2014. Web. Sept 20, 2015. http://www.forbes.com/sites/gregsatell/2013/03/04/what-netflixs-house-of-cards-means-for-the-future-of-tv/
Thompson, Clive. “If You Liked This, You’re Sure to Love That.” The New York Times. Nov 21, 2008. Web. Sept 15, 2015. http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html
Uphoff, Kelly. “Quasi-Experimentation at Netflix (Beyond A/B Testing).” Predictive Analytics Innovation Summit. Chicago, IL. Nov 12, 2014. Lecture.
Wylie, Kevin. “Content Analytics at Scale @ Netflix.” Linkedin.com. June 24, 2015. Web. 19 Sept 2015. https://www.linkedin.com/pulse/content-analytics-scale-netflix-kevin-wylie