Why Big Data is Big Business: The Netflix Example

Take a look at the following chart:

Simply amazing, isn’t it?

The chart indicates the Netflix market cap growth over a period of 10 years: from July 2009 to July 2019.

While Netflix is a great brand that should be studied by business leaders at all levels, I believe Netflix should be studied even more by advocates of big data and artificial intelligence.

Netflix has been able to increase its market cap from a little over $2 billion to about $170 billion in a span of 10 years. While this growth is nothing short of amazing, what is even more impressive is the fact that this growth is majorly attributable to big data.

Netflix is no stranger to controversies: perhaps its most controversial move was outrightly banning the use of VPNs, which a source estimated affected about 30 million of its users in regions where it has plans to expand. Despite the fact that some VPNs are able to bypass Netflix’s ban, and hundreds of thousands of users eventually cancelled their subscription as a result of the ban, Netflix refuses to back down in order to satisfy its content partners. Then there is the constant price hikes by Netflix, which always leads to cancellations and user protests that Netflix is generally insensitive to.

Despite all this, Netflix has only grown and thrive. The chart below partially explains why:

The chart shows how much, in billions, Netflix has spent on content over the last five years.

If you take a look at its 2018 budget, a whopping $12.04 billion was spent on content. By comparison, $2 billion was spent on marketing. Netflix would rather not spend anything on marketing; by relying on a big data content creation process, they are hoping that their content will market itself.

What We Can Learn From Netflix’s Approach to Big Data

According to data from IBM, we create 2.5 quintillion bytes of data on a daily basis -- a figure expected to rapidly increase -- and 90 percent of the world’s data was created in the last two years alone. Despite this, very few companies make use of this abundance of data; in fact, it is estimated that the majority of companies only analyze 12 percent of the data they have. Not Netflix!

Using a combination of its own custom solutions and traditional business intelligence tools like Hadoop and Teradata, Netflix is able to rapidly and scalably process a lot of data; using this data, Netflix is able to understand what users want with great precision, to the extent that it is able to boast a 90 percent engagement rate with its original content. So powerful is engagement with Netflix’s big data-influenced content that it renesw 93 percent of its original shows; by comparison, the TV industry renews just 35 percent of its shows.

How Netflix Gathers Data

Netflix gathers data through several sources and means. These include:

• From social media platforms like Facebook and Twitter.
• From third party data platforms like Nielsen.
• From its own platform.

The data Netflix gathers from its own platform include data on:

• The location of the user consuming content on Netflix.
• The interaction of the user with content (pausing, skipping, rewatching, etc).
• The time/day users watch a particular content type and the role it plays in the type of content.
• The device used to watch content and how it influences the type of content watched.
• The date a particular piece of content was watched.
• Searches users are conducting on its platform.

How Netflix Uses Data

Using data gathered through these sources and means, Netflix not only determines what type of content its users are interested in but can also effectively target existing content to users.

For example, besides influencing new content creation, this data is used to:

• Determine whether to promote a particular piece of content or create similar content due to user engagement with that content (such as how many times user re-watch or skip the piece of content). More engaging content will be promoted more and content similar to it will be created. There’s no point promoting a piece of content users don’t engage with.
• Determine which particular content to prioritize with a particular user/user group based on the user’s interaction with the Netflix platform by using a content affinity algorithm.
• Determine what content genre to focus on and which producers and lead actors to more prominently feature in its content.

Conclusion

As Netflix’s exponential growth shows, understanding exactly what users want and giving it to them can be a growth catalyst for enterprises of all sizes. The best way to understand what users want, however, is by making use of big data. Studying Netflix’s approach to big data can be a good way to inform your big data strategy.