Future Tense

The Corrupt Personalization of Netflix

The company embodies one of the most seductive myths of the algorithmic age.

Algorithmic culture blurs the lines between our genuine interests and a set of commodities.


This essay is adapted from What Algorithms Want: Imagination in the Age of Computing, by Ed Finn, published by MIT Press. On Tuesday, March 28, Ed Finn will discuss What Algorithms Want at an event in Washington. For more information and to RSVP, click here.

David Fincher’s new television project for Netflix is a moody and bloody serial-killer drama that explores the depths of human psychology. The show is going to be a hit—and Netflix knows it. I’m not saying this because Fincher’s work is great (though it is), but because Netflix has designed a system where it can count on millions of people who will think that they, individually, organically, and of their own free will, have chosen to love this show that was recommended especially for them.

The intimacy of Netflix is what makes this possible. The company has built its business on the motto “everything is a recommendation”—the company likes to brag that it tailors every aspect of its interface for each individual user. It’s a playbook the company perfected with House of Cards, which was a tremendous gamble for the business of television when Netflix launched the show in 2013.

Netflix commissioned House of Cards in large part on algorithmic insights: It had significant statistical evidence to suggest that its users would embrace a reboot of a BBC political drama starring Kevin Spacey, with Fincher at the directorial helm. Eager to outpace HBO as a producer of original content, the company bid $100 million to secure the rights to House of Cards for two 13-episode seasons, making it television’s most expensive drama at the time. Unlike a traditional pilot model, the company invested in a cultural monopoly (now crumbling as the show is licensed by arch-enemies such as Comcast) to own a unique creative offering. When the company’s chief content officer, Ted Sarandos, was questioned at the Sundance Film Festival about just how significant algorithms are to the decision process, he responded that it’s a 70 percent data, 30 percent human judgment mix, “but the 30 needs to be on top, if that makes sense.”

This was a radical departure from the network TV business model, in which a show competes against a few other offerings in the same time slot. But the calculation for Netflix is very different, particularly when we remember that “everything is a recommendation.” Netflix knew it did not have to spend millions advertising the show because it already has a direct line to its millions of users. The company promoted House of Cards to them with 10 highly targeted trailers: Kevin Spacey for the Spacey fans; artful shots for the David Fincher fans; and scenes featuring female characters for viewers who had just seen something with strong female leads, like Thelma and Louise. House of Cards was an algorithmically produced show not just in its initial framing but in its production and rollout.

After making the initial decision to invest in House of Cards, Netflix used algorithms to micromanage distribution, not production. Netflix knew that with this combination of talent and investment, its own delivery system could generate success—like the algorithmic stock-trading firms that pay little attention to the intrinsic value of a commodity so long as they can predict the fluctuations in its price.

The most powerful signal of this new creative mode was the release schedule of the show, with all 13 episodes appearing online for streaming at midnight. As Fincher put it:

The world of 7:30 on Tuesday nights, that’s dead. A stake has been driven through its heart, its head has been cut off, and its mouth has been stuffed with garlic. The captive audience is gone. If you give people this opportunity to mainline all in one day, there’s reason to believe they will do it.

This new shift in the temporal battleground of commercial television was the logical extension of Netflix’s long march against traditional retail models—starting with the time spent in line at Blockbuster and concluding with the time spent in front of commercials waiting for a scheduled broadcast to begin. The move to release the full season at once also gave Netflix the opportunity to test the hypothesis by observing how users dealt with the invitation to gorge themselves on high quality content. When Season 1 was released, one user watched all 13 episodes immediately after they launched, pausing for only three minutes during the entire period. Season 2 was released on a Friday night, and 2 percent of all U.S. subscribers had watched the 13 episodes by the end of the weekend.

And that pie is only getting larger as Netflix expands internationally. Like Amazon, Microsoft, and other digital streaming services, Netflix now commissions its own shows so that it can offer exclusive content and conquer the new arena of online global streaming video. Last year CEO Reed Hastings announced the launch of streaming to 130 countries, making the service a “new global Internet TV network.” Hastings’ words at the announcement hit all the highlights of an abstracted entertainment experience:

With this launch, consumers around the world—from Singapore to St. Petersburg, from San Francisco to Sao Paulo—will be able to enjoy TV shows and movies simultaneously—no more waiting. With the help of the internet, we are putting power in consumers’ hands to watch whenever, wherever and on whatever device.

No more waiting, and no more geographical boundaries. From the revolutionary red of its paper mailers to the company’s triumphant announcement of service “whenever, wherever and on whatever device,” Netflix has competed and won based on the aesthetics of abstraction. It should come as little surprise, then, that when the company began to evolve from an Amazonlike titan of logistics and supply-chain management (moving DVDs around the country better than anyone else) to content creator, it would continue to depend on algorithmic analysis.

House of Cards thus embodies one of the most seductive myths of the algorithmic age: the ideal of personalization, of bespoke content assembled especially for each one of us. In fact, the content, or at least the costly, aesthetically rich content we care about, like Fincher’s show, is still fairly limited. There is only one House of Cards, but there are as many ways to market the show as there are to target Netflix viewers. This is what information theorist Christian Sandvig calls “corrupt personalization”: the ways that algorithmic culture blurs the lines between our genuine interests and a set of commodities that may or may not be genuinely relevant, such as products “liked” by our friends on Facebook even if they did not knowingly endorse them.

Netflix has made the process of getting people hooked on its hit shows such a science that it actually released a graph titled, “Do you know when you were hooked? Netflix does.” Sarandos called this “how fans are made,” and it’s central to the company’s strategy of releasing full seasons at once. After all, the shift from broadcast to algorithmic entertainment leads to a reinvention not merely of content but of user behavior. House of Cards and the company’s countless other offerings are cinema as a service, a subscription for content available whenever and wherever we want it. In this vision of the future, a whole series of players has been banished from the field: national and local cable companies, advertisers, Nielsen ratings and the live national audience they measure, and even the local network affiliate news crew. What remains after we switch on our own, personalized version of Netflix is just us, an atomized viewing audience, interacting directly with the algorithm and experiencing a totally customized library of entertainment. It allows us to more fully embrace the particular kind of abstraction Netflix is promoting, watching the show in our own private temporal stream, while Netflix watches us.

I just know you’re going to love this new show.

This article is part of Future Tense, a collaboration among Arizona State University, New America, and Slate. Future Tense explores the ways emerging technologies affect society, policy, and culture. To read more, follow us on Twitter and sign up for our weekly newsletter.