How Netflix Uses Analytics To Select Movies, Create Content, & Make Multimillion Dollar Decisions

In 2006 Netflix announced the Netflix Prize, a competition for creating an algorithm that would “

substantially improve the accuracy of predictions

about how much someone is going to enjoy a movie based on their movie preferences.” There was a winner, which improved the algorithm by 10%. However, Netflix never did implement the algorithm, saying:

But Netflix didn’t shun all algorithm and data efforts.

To the uninitiated, it may seem that Netflix’s analytics go only as far as views. They may also think that the show

House of Cards

was chosen because Netflix “thought subscribers might like it.” But the truth is much, much deeper. The $100 million show wasn’t green-lighted solely because it seemed like a good plot. The decision was based on a number of factors and seemingly almost entirely on data.

The reality is that Netflix is a data-driven company. Saying that Netflix chooses new content based on “whoever they can get a license with” is a very thin and vague statement. Netflix does need licenses from studios, but they don’t just pick movies and television shows at random.

Read on to learn more about the future of television programming and how analytics is helping Netflix become a better business and service.

Analytics at Netflix

The core job of analytics is to help companies gain insight into their customers. Then, the companies can optimize their marketing and deliver a better product. (Without analytics, companies are in the dark about their customers.) Analytics gives businesses the quantitative data they need to make better, more informed decisions and improve their services.

So how does Netflix use analytics?

– Joris Evers, Director of Global Communications

As of July 2018, Netflix has

130 million worldwide streaming subscribers

. Having this large user base allows Netflix to gather a tremendous amount of data. With this data, Netflix can make better decisions and ultimately make users happier with their service.

Traditional television networks don’t have these kinds of privileges in their broadcasting. Ratings are just approximations, green-lighting a pilot is based on tradition and intuition. Netflix has the advantage, because being an internet company allows Netflix to know their customers well, not just have a “persona” or “idea” of what their average customer is like. Let’s look at an example.

If you’re watching a series like

Arrested Development

, Netflix is able to see (on a large scale) the “completion rate” (for lack of a better term) of users. For example, the people at Netflix could ask themselves “How many users who started

Arrested Development

(from season 1) finished it to the end of season 3?” Then they get an answer. Let’s say it’s 70%.

Then they ask “Where was the common cut off point for users? What did the other 30% of users do? How big of a ‘time gap’ was there between when consumers watched one episode and when they watched the next? We need to get a good idea of the overall engagement of this show.”

They then gather this data and see user trends to understand engagement at a deep level. If Netflix saw that 70% of users watched all seasons available of a cancelled show, that may provoke some interest in restarting

Arrested Development

. They know there’s a good chance users will watch the new season.

But the data gets deeper than that. Here’s a look at some of the “events” Netflix tracks:

When you pause, rewind, or fast forward

What day you watch content (Netflix has found people watch TV shows during the week and movies during the weekend.)

The date you watch

What time you watch content

Where you watch (zip code)

What device you use to watch (Do you like to use your tablet for TV shows and your Roku for movies? Do people access the Just for Kids feature more on their iPads, etc.?)

When you pause and leave content (and if you ever come back)

The ratings given (about 4 million per day)

Searches (about 3 million per day)

Browsing and scrolling behavior

Netflix also looks at data within movies. They take various “screen shots” to look at “in the moment” characteristics. Netflix has confirmed they know when the credits start rolling; but there’s far more to it than just that.

Some have figured

these characteristics may be the volume, colors, and scenery that help Netflix find out what users like.

Why does Netflix want to know when the credits roll? They probably want to see what users do afterward. Do they leave the app or go back to browsing? Notice how Netflix now offers movie recommendations (they have personalization algorithms that aim to accurately predict what users will watch next) soon after credits start (or, for television shows, they

automatically play the next episode


Because if users leave the app after watching a show, that may mean they are more likely to cancel. Allow me to explain:

Through their analytics, Netflix may know how much content users need to watch in order to be less likely to cancel. For instance, maybe they know “If we can get each user to watch at least 15 hours of content each month, they are 75% less likely to cancel. If they drop below 5 hours, there is a 95% chance they will cancel.”

So now that they have this data, they can ask themselves “How do we help users watch at least 15 hours of content per month?” One idea: enable post-play, which automatically plays the next episode of a TV show unless the user opts out. For movies, show movie suggestions (based on the rating of the movie just watched) right after the credits start rolling and allow users to press play right from that screen. Netflix can add this feature to their web and mobile apps and, again, through analytics, see the results.

This is only a theory of how Netflix came to the decision to implement post-play and an example of how analytics can help Netflix make decisions. I don’t have any inside information.

So all of this data and the large user base allow Netflix to quickly see trends and formulate opinions. Later, we’ll get into the factors that made them green-light

House of Cards


The Recommendation Algorithm

As part of the on-boarding process, Netflix asks new users to rate their interest in movie genres and rate any movies they’ve already seen. Why do they do this right up front? Because helping users discover new movies and TV shows they’ll enjoy is integral to Netflix’s success.

If people run out of movies they want to watch and have no way to find new movies, they’ll cancel. It’s important that Netflix puts a lot of focus on making sure they have an accurate algorithm for this rather than having users rely on outside sources to find new movies.

Is the recommendation algorithm accurate and successful?


75% of viewer activity is based on these suggestions

, I’d say it works pretty well for them.

But now that more users are moving to streaming, what they actually watch is more important than ratings. When it was DVD-by-mail, Netflix users had to wait, and the rating was a “thought process.” Netflix engineers Xavier Amatriain and Carlos Gomez-Uribe explain:



As we can see, the algorithm is evolving. There are entire teams (Netflix has over 800 developers in total) working on it. It’s not static because user behavior and the Netflix product are changing.

For a deeper description of the algorithm,

check out this post

written by the people who design and work on it.

The New Thumbs Up/Down Rating System

In April 2017,

Netflix debuted a new rating system

. Previously, users would rate movies and TV shows on 1-5 stars. But after their product teams ran some tests, they found a new, simpler “thumbs up-thumbs down” test beat the original star-based rating system. In their

Q1 2017 Letter to Shareholders

, Netflix wrote:

How Big Data Factored into

House of Cards

In 2011 Netflix made one of the biggest decisions they’ll ever make. It wasn’t anything material, but rather it was about content. They

outbid top television channels like HBO and AMC to earn the rights for a U.S. version of

House of Cards

, giving them 2 seasons with 13 episodes in each season.

At a cost of $4 million to $6 million an episode, this 2-season price tag is over $100 million. Netflix has undoubtedly made other big money investments before (shipping centers, postage costs, etc.), but nothing like this on the content side. So why did they make such a big bet, and how did analytics factor into the decision? Let’s get into it.


Before green-lighting

House of Cards

, Netflix knew:

A lot of users watched the David Fincher directed movie

The Social Network

from beginning to end.

The British version of “

House of Cards

” has been well watched.

Those who watched the British version “

House of Cards

” also watched Kevin Spacey films and/or films directed by David Fincher.

Each of these 3 synergistic factors had to contain a certain volume of users. Otherwise,

House of Cards

might belong to a different network right now. Netflix had a lot of users in all 3 factors.

This combination of

factors had a lot of weight in Netflix’s decision to make the $100 million investment in creating a U.S. version of

House of Cards

. Jonathan Friedland, Chief Communications Officer,


“Because we have a direct relationship with consumers, we know what people like to watch and that helps us understand how big the interest is going to be for a given show. It gave us some confidence that we could find an audience for a show like

House of Cards


Swasey says it’s not just the cast and director that predict whether the show will be a success. “

We can look at consumer data and see what the appeal is for the director, for the stars, and for similar dramas,”

he says. Add this to the fact that the British version of

House of Cards

has been a popular DVD pick for subscribers. Combining these factors (and the popularity of political thrillers) makes it seem like an easy decision for Netflix to make. The only question was how much they were willing to invest. We’ll get into the early ROI numbers a little later.

After the Green Light

Now that Netflix has made the $100 million investment, they are in part responsible for promoting it. And with the data they have, they can make a “personalized trailer” for each type of Netflix member, not a “one size fits all” trailer. Let me explain…

Before a movie is released or TV show premiers, there’s typically one or a few trailers made and a few previews selected.

Netflix made 10 different cuts of the trailer


House of Cards

, each geared toward different audiences. The trailer you saw was based on your previous viewing behavior. If you watched a lot of Kevin Spacey films, you saw a trailer featuring him. Those who watched a lot of movies starring females saw a trailer featuring the women in the show. And David Fincher fans saw a trailer featuring his touch.

So now that the first season has run, let’s look at some of the early metrics. These won’t determine immediately whether the

House of Cards

investment can be considered successful, but rather the trajectory that it’s on.

What do you think the average success rate is for new TV shows? In other words, if a television network green lights a new TV show, what are the chances it will be profitable or won’t be cancelled after a couple of seasons?

Your guess?

The answer is 35 percent, on average.

When a network green lights a show, there’s a 35 percent chance it succeeds and a

65 percent chance it gets cancelled

. At the time of this writing, Netflix has 7 TV shows, of which 5 have been renewed for another season. If this rate can continue for years, the Netflix success rate will be about 70 percent.

So why does Netflix renew shows at a higher rate than conventional television networks? Does the data make the difference? Is the success rate legitimate or can you not compare an Internet television network to conventional TV networks?


House of Cards

been a success? It has brought in

2 million new U.S. subscribers in the first quarter of 2013

, which was a 7% increase over the previous quarter. It also brought in 1 million new subscribers from elsewhere in the world. According to

The Atlantic Wire

, these 3 million subscribers almost paid Netflix back for the cost of

House of Cards


And what about current subscribers? Does having

House of Cards

make them less likely to cancel their subscription?

Yes, for 86% of them.

A survey

showed that 86% of subscribers are less likely to cancel because of

House of Cards

but only if Netflix stays at the $7.99 price point. While this may seem impressive, you should take this survey with a grain of salt. As the author points out:

What can be safe to say is that

House of Cards

gives all Netflix subscribers one less reason to cancel. How big or how small the reason is arbitrary.

Orange is the New Black

In the next section we’ll take a step back and look at the big picture of how analytics is helping Netflix.

How Netflix Decides on Movies to License

By now, you probably can guess that Netflix doesn’t blindly pick which movies to stream. Licensing movies from studios is expensive, so Netflix uses data to help them decide. There are only a limited number of movies to license. For example, a popular new release may not be available immediately, but a year later it might be. There is a vast number of movies available for Netflix to pick from, just not


movie available. So Netflix has to find which ones its users will enjoy the most.

As John Ciancutti, former VP of Product Engineering (now at Facebook), says:

Jenny McCabe, Director of Global Media Relations,

puts it another way


There you have it… That last sentence tells it all. They need to know what people watch and what people like in order to decide on new titles. If no one watched anything, they’d be in the dark. Now you can see that their analytics is a big help in deciding what movies and TV shows to select. They are not, as McCabe put it, a “broad distributor,” possibly stating a differentiation from Hulu.

At a $7.99 per month per member pricing plan, Netflix cannot afford to add every box office hit. They need to be smart about their decisions and take full advantage of their analytics. Being cost efficient and making users happy is a skill that is central to Netflix’s success. Let’s use an example of how they might combine smart economics while also maximizing user happiness.

In other words, instead of getting

The Dark Knight

, they could get other movies with the same actors and director. They could add


(directed by Christopher Nolan),

Brokeback Mountain


A Knight’s Tale

(starring Heath Ledger),

Thank You for Smoking

(starring Aaron Eckhart),

Stranger than Fiction

(starring Maggie Gyllenhaal), and

The Machinist

(starring Christian Bale) for (or near) the price of one license to

The Dark Knight

. What route would you choose?

Again, this is just a hypothetical, but it’s probably safe to say that this is a common situation Netflix faces. Let’s look at another example.

Along with these tactics, Netflix also

studies piracy sites to help them decide what content to purchase

. One show they picked up as a result is Prison Break, which has been heavily pirated.

Use Analytics Directionally

When asked to name the 3 things he learned from Reed Hastings, Netflix co-founder

Mitch Lowe

said focus, analytics, and pour money into the things that are working best.

When discussing analytics, he says:

When pressed to give an example, Lowe says:

Now, let’s take a step back, look at the big picture, and see Netflix’s aspiring goal.

Netflix’s Goal to Become the HBO of Internet TV

Netflix’s data and analytics are a big asset for them. It helps them build a better service for users and become a more cost efficient business by reducing waste and avoiding “shots in the dark.”

In their own words, Netflix wants

“to become HBO faster than HBO can become Netflix.”

They’re adding shows at a rapid pace,

with the goal of adding at least 5 new shows per year

according to Ted Sarandos, Chief Content Officer. As of February 2013, he had $6 billion available to him to choose content for Netflix streaming. This money goes to pay for licensing fees from cable companies and studios, but $300 million is for original content, according to



Some of that original content will not be just TV shows, but also

exclusive documentaries and stand-up comedy specials

. Comedian Aziz Ansari will

debut his standup special,

Buried Alive

, on Netflix

. It’s slated to debut November 1st. And on October 14, Netflix will

debut another stand-up special and documentary series by comedian Russell Peters

. If you’re interested, Wikipedia has a great page that

lists out all current and future Netflix programming


HBO has a slew of original content in addition to their licensing of movies commonly not on networks such as TNT, TBS, USA, AMC, etc. In April 2013, HBO premiered the Louis CK standup

Oh My God

. Clearly the HBO model has been successful for Time Warner, its owner.

As of April 2013, it has been estimated that

Netflix surpassed HBO in subscribers

. This means they matched their goal of “becoming HBO faster than HBO can become Netflix.”

Netflix, like HBO, has no plans to eventually be a distributor of original content only. CEO Reed Hastings has said “If we do our job right,

there’s always a reason to be a Netflix member on the original side, in addition to the license side


In Conclusion…

Now you see how Netflix makes informed decisions based on data. Clearly, data cannot make every decision; there are some situations where intuition has to take over. For instance, data could not predict that a show like

Breaking Bad

would be a success. The creator was a former writer on

The X-Files

, and dramas are 50/50. In these cases, decisions are heavily based on the people and team behind the idea of the show. Whether Netflix can make a successful show like this (one with little to no data) is yet to be seen.

What analytics and data can do is give you insight so you can run a better business and offer a superior product. People with data have an advantage over those who run on intuition or “what feels right.”

Do you have data to help you make decisions? If not, Netflix provides a good case for why you should do so.

Let me hear your feedback in the comments.

About the Author:

Zach Bulygo is a blogger, you can find him on Twitter



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