Every quarter, Netflix announces its growth in paying subscribers, and that number is always incredibly impressive. For instance, in the third quarter of 2017 it grew its subscriber base by 5.3 million to a total of 109 million subscribers. Even more impressive, it is now tied in market penetration with all U.S. cable TV combined.
But focusing on user growth is to ignore Netflix’s true genius: its maniacal obsession with keeping its already-existing users.
This obsession stems back to before it was the streaming behemoth it is today, back when it was mostly a DVD-by-mail company.
In 2006, the company launched a competition it called the Netflix Prize. It would award $1 million to an outside engineering team that could improve its recommendation algorithm by 10 percent (it ended up awarding the prize in 2009).
It wasn’t doing this just for the fun of it, or even for marketing purposes. Its team had noticed a subset of users who would sign up for Netflix, binge watch a TV show or a series of movies, and then, after a period of months of not ordering any new DVDs, cancel their subscription.
Netflix recognized early on that it cost a lot more to acquire a new user than to keep an already-existing one, and the only way it would thrive is if it figured out how to keep its user base engaged.
Flash forward to its evolution into a streaming video service, and it’s now not only obsessed with finding the best content you’re most likely to enjoy, but it’s optimizing the presentation of that content to increase the likelihood that you’ll click through and sample it.
In a post titled “Artwork Personalization at Netflix,” its engineers laid out, in extremely technical detail, how its team first created multiple versions of the artwork that’s displayed for each of its titles and then devised an algorithm to predict which cover artwork would appear to individual users.
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For instance, let’s say Netflix wants to recommend that you watch Pulp Fiction. Whether it shows you cover art that features Uma Thurman or John Travolta will be based on your past viewing habits. Or let’s take two hypothetical users: one who watches a lot of action movies and another who watches romantic comedies. If they’re recommended the same movie, one will be shown a still image of an action scene from the film while the other is shown a still of two characters kissing.
This optimization approach is especially important for how Netflix markets its original content, including the TV shows and movies it produces. A great piece from The Verge explains that, in marketing its new Will Smith fantasy movie Bright, Netflix is focusing less on the traditional film promotion that generates an opening weekend rush, and is instead strategizing for how it can get its already-existing subscriber base excited about the film:
The general promotion strategy doesn’t have a built-in end date. Instead, the service will refine its sense of who Bright appeals to based on who actually watches the film after it premieres. … As people watch Bright, Netflix will get a better sense of what kind of viewers are drawn to the film, and the system will hone the film’s profile appropriately. New potential audience members will likely be revealed, activating the promotional cycle all over again, but now targeting the people Netflix didn’t know would be interested in the movie in the first place. The process gives the film a lifetime of exposure, as long as people keep logging in and watching movies.
As Netflix continues to saturate certain markets, this strategy of user retention will become even more important. Like it or not, Netflix is no longer the scrappy underdog; it’s now the incumbent player, and with Disney’s recent acquisition of Fox and a bevy of new streaming services entering the market, it has more to lose than ever before. Its user retention strategy will be the moat it needs to ward off competitors.
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