When Netflix forayed into original programming by committing $100 million for two seasons of House of Cards, it did so without even watching the pilot. What made Netflix so confident that it thought this decision was a no-brainer?

Big Data!

Netflix is, at its core, a data-driven company that diligently collects information from its 50 million-plus subscribers at an extraordinary speed. Netflix made the House of Cards decision by identifying that subscribers who watched the original British version of House of Cards were very likely to watch movies starring Kevin Spacey or directed by David Fincher.

The series was a huge hit and has been renewed for a third season. Here’s the question though: is this the future? Did Netflix just get lucky? Or were they right in banking on big data?

The New Approach to Picking TV Shows

Let’s take a look at how TV shows are traditionally approved. Networks receive hundreds of pitches from writers and producers. The networks then request scripts for a few of these and then order 20 to 30 pilots. Once the pilots are produced, they are presented to executives and sometimes focus groups to predict how successful the show might be. From that, networks approve a handful.

What is the success rate for the shows that see the light of day? Despite such an exhaustive process, only about one show out of three is renewed for a second season, according to publicly available data from 2009-2012.

Netflix, which is a new entrant into original programming, has licensed five original series to date. Four of them, including House of Cards, have been renewed for subsequent seasons whereas the jury is still out on the most recent one, Turbo Fast, an animated show for kids which has only recently finished its first season.

That still gives Netflix an 80 percent success rate (at the very minimum) with original programming, compared to the 30 to 40 percent success rate for networks. These shows have primarily been picked by running data mining and other algorithms against the vast user behavior data available to determine the size of the possible audience and thereby the likelihood of success.

More Big Data at Netflix

For Netflix, big data doesn’t stop here. Not only does it use the data to identify what shows to commit to, but it can now take additional steps to ensure that the show reaches the right audience.

For example, Netflix made ten different versions of the trailer for House of Cards geared towards different audiences. Fans of Kevin Spacey watched trailers that were focused on him while people who liked female-oriented movies saw trailers that highlighted the women in the show.

In addition, Netflix uses its recommendation engine to promote new content to its subscribers. The recommendation engine uses a complex algorithm that has been built and fine-tuned around the rich stash of behavioral data that Netflix has collected. This cross-promotion of products enables Netflix to cut down on its marketing costs, especially with original content.

The Takeaway

Netflix collects a lot of data to understand how its users behave and what their preferences are. It collects metrics including what people watch, when they watch, where they watch, what devices they use, ratings, searches, when users pause or stop watching, etc.

Netflix derives meaningful insights from all of this data to personalize and enhance the user’s experience on the platform. This allows the company to provide a unique experience for every individual subscriber, specifically tuned to their tastes and preferences.

There is concern that such data-driven programming could impact quality and diversity, as directors could sacrifice creativity and instead tailor their shows solely based on what the data shows is the right thing to do.

Also, most of us might have seen that Netflix’s data is not perfect. For example, Netflix thinks that I would like Portlandia based on my interest in The Office, but that is not the case. However, there is no denying the impact that big data can have and Netflix is radically transforming the broadcasting industry with big data.

This article was first posted here

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Harsha Hegde currently works at MResult where he focuses on the company’s goal of maximizing business results for clients. He got his MBA from Carnegie Mellon University, USA and prior to that worked as a software developer for 6 years at companies including Oracle. He is passionate about the different ways in which technology can have a positive impact on our lives.

(Image Credit: Global Panorama)

In 2012, Americans watched more legally-delivered video content via the Internet than on physical formats such as DVDs. This shift in format also allows online content providers to gather large amounts of data on viewer habits.

Recommendation engine

Netflix is the de facto iTunes for online video content. It collects data on every search and every rating entered into the system. Netflix user logins allows further data enrichment with verified personal information (sex, age, location), as well as preferences (viewing history, bookmarks, Facebook likes). Third-party data providers, such as Nielsen, adds an extra layer of data on top.

In the same way Amazon crunches all that data to make book recommendations, Netflix makes movie recommendations. The better recommendation engine, the more content gets watched. Having details on when and where content is viewed also means Netflix knows when and where reminders best be sent.

Content creation

Having detailed knowledge of Netflix subscribers allowed the company take the next step of creating content. The data collected by Netflix indicated there was a strong interest for a remake of the BBC miniseries ‘House of Cards’. These viewers also enjoyed movies by Kevin Spacey or those directed by David Fincher. Now add a $100 million commitment, and you get two seasons.

Next steps

Online delivery of the video allows Netflix to log every event. Every time the viewer presses play, pause or repeat. With hundreds of millions of events, Netflix could create a playbook on how to create TV shows. When to show an explosion, when to show a sex scene, or even when to have a product placement.

Netflix is even capturing screenshots to capture characteristics such as color and scenery, and how well the audience responds to such imagery. These details could effectively govern the creative direction, on what’s being shown when. However, with so many details determined by algorithms, can it really still be described as a ‘creative direction’?

Read more here.

This week, we feature the best articles on limitations of Big Data. In particular, Tim Harford makes an excellent case on why correlation does not equal causation.

Image credit: David Erickson

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