How Netflix Uses Big Data to Build Mountains of Money
A look behind the scenes at Reed Hasting’s binge factory
Legendary Hollywood screenwriter William Goldman (All the President’s Men) once said, “Nobody, nobody — not now, not ever — knows the least goddamn thing about what is or isn’t going to work at the box office.”
Netflix CEO Reed Hastings decided to prove Goldman wrong by building a business doing just that: predicting which movies will keep us glued to our chairs. Heaps of data fuel the fire of Netflix’ “recommendation engines” and influence which movies the company doubles down on.
Since it was founded in 1997, Netflix has become a tech juggernaut whose streaming innovations have changed how we watch, pay for, and talk about entertainment. The company became a verb. If you’ve ever Googled something or Ubered somewhere, you know exactly what I mean.
They’re fearlessly expanding internationally while creating some of the most compelling content available anywhere and being rewarded with record share prices (Netflix has a stock-market value of over $200 billion, more than Disney).
In this article, I’ll take a look at three drivers behind Netflix’s blockbuster growth, and the marketing lessons we can all learn from them.
Grab the popcorn. You won’t want to hit pause on this one.
1. Data-Driven Personalization
Netflix’s famed recommendation algorithm powers 75% of viewer activity. In other words, three-fourth of all Netflix watching is based on Netflix’s own suggestions. For a deeper description of their algorithm, check out this post written by the very people who designed it.
“Get closer than ever to your customers. So close that you tell them what they need well before they realize it themselves.”
— Steve Jobs
Even the artwork of individual movies gets a personal touch: Netflix optimizes the artwork for each member to highlight aspects of the movie that are most relevant to them. It’s always going to be tough to judge a book (or movie) by its cover, but at least now that cover is personalized.
Data scientists are Netflix’s secret weapon when it comes to understanding customer behavior and leveraging it to drive conversions, loyalty, and profits. They’re the reason why, as Netflix’s Director of Global Communications put it,
“There are 33 million different versions of Netflix.”
Lesson learned: the more interactions with fans you can track, the better. Whether your fans are entering a contest, visiting your website, or attending your event — each digital transaction leaves bits of data. If you can pick these up and piece them into a perfect picture of your fans, you’ll be able to craft seamlessly personalized experiences.
2. Relentless Experimentation and Testing
In April 2017, Netflix launched a new rating system. Previously, users would rate movies and shows with 1–5 stars. But after their product teams ran some tests, they discovered a new, simpler “thumbs up/down” rating system that consistently beat the original star-based model.
In their Q1 2017 Letter to Shareholders, Netflix wrote:
“As always, our product team has dozens of tests running in the endless quest for even higher member satisfaction. One test that won conclusively last year and has now been rolled out to all members is our new “thumbs-up thumbs-down” feedback model, replacing the 5-star model we have had from our DVD days. The amount of usage we get with this approach is over twice as many ratings.”
In fact, every product change at Netflix goes through a rigorous testing process before becoming the default user experience. Even the cover images associated with many new titles are A/B tested, sometimes resulting in 20 to 30 percent more views for that title!
Results like these highlight why Netflix is so obsessed with A/B testing. By creating a data-driven culture, they ensure product changes are driven by actual data instead of the most opinionated or vocal Netflix employees.
If you want to know more about the nuts and bolts of Netflix’ approach to experimentation, I highly recommend this article which gives you an insider perspective on their A/B test workflow:
Netflix even applies this data-driven approach to content itself. Their Netflix Originals — titles exclusively available on Netflix — are launched based on viewer behavior and empirical data.
This “big data”-approach to content creation is so successful that, compared to the TV industry — where just 35 percent of shows are renewed after their first season — Netflix renews 93 percent of its original series.
A prime example of this is…
House of Cards: A Netflix Case Study in Big Data
Historically, from pitch to pilot, launching a series involved jumping through multiple hoops, and making decisions based on historical ratings and ‘gut feel’. Inevitably, hundreds of TV series were canceled within weeks of their launch as a result of low viewership and poor reviews.
With no access to hard data on viewer behavior, executives often made bad guesses that didn’t align with what the market wanted. Incredibly costly in terms of development and marketing, these misinformed series — often months or even years in the making — were launched at a huge cost to the publishers.
Because Netflix has insight into the binge-watching behaviors of millions of subscribers around the world, they’ve disrupted the traditional process of ‘green-lighting’ new TV series and films, simply by looking at what people are already watching, and how they’re watching it — no gut feel required.
In doing so, Netflix is essentially quantifying the popularity of art before it’s even been created. Manufacturing virality, if you will.
Take award-winning series House of Cards. The $100 million show wasn’t green-lighted just because it seemed like a good plot. The decision was based on a number of variables, relying almost entirely on data.
Using combined metrics collected across their platform, Netflix determined that a significant percentage of its 33 million subscribers had streamed director David Fincher’s film, The Social Network, from start to finish, and that movies featuring Kevin Spacey were always successful with its audience.
What’s more, Netflix’s data revealed that the British version of House of Cards on its platform was a big hit in the States. And that those who had watched the British version of House of Cards had also watched other movies acted by Spacey or directed by Fincher.
Image courtesy of the author
Relying on this data, Netflix concluded that an already successful show in Britain, starring much-liked actor Kevin Spacey and director David Fincher, for an American audience, would be a big hit.
Needless to say, they were right.
House of Cards became an instant success, and six years later — despite the turmoil surrounding its star, Kevin Spacey — the show still boasts an 8.7 out of 10 rating from 450,000+ IMDb reviews, putting it in the league of other blockbusters like Breaking Bad and Game of Thrones.
Note: The success of House of Cards isn’t an isolated incident. Other Netflix originals like Orange Is The New Black, Stranger Things, and The Crown were introduced to critical acclaim using a similar process that relies on big data.
3. Put the User in the Driver’s Seat
In 2018, Netflix released a Black Mirror episode where viewers could choose the ending. This type of high-tech choose-your-own-adventure content could lead to a whole new level of engagement.
Imagine how much more intense binge-watching is about to get now that there are multiple endings to your favorite shows.
Some viewers will almost certainly want to discover all endings of a story and spend hours exploring the various storylines. Ever gotten a quiz result and immediately taken the quiz again to see the other outcomes? You’re not alone.
The benefits for Netflix don’t stop there, however. If, for example, audiences watch Stranger Things multiple times, and the majority picks the third ending, it’s worth considering those choices when developing new programming and original content. As you can see, this type of engagement opens up a whole new world of opportunities.
Add live sports broadcasting, interactive storytelling, and maybe even cloud gaming (Netflix still has the best at-scale and compression technology in the world) and countless possibilities mushroom up.
Of course, the competition is coming. Netflix stock dipped 4.5% the day Disney+ was announced. It’s getting crowded with everyone vying for those precious streaming dollars. However, in the end, I feel it’s their game to lose.
Originally published on medium.