With Netflix’s newest foray into original drama, House of Cards, being hailed as the first victory of “Big Data” programming, it’s definitely time to consider how you are working with Big Data and how to better optimize your approach. Even if you can’t get David Fincher, Kevin Spacey, and the rights to a BBC political thriller, you can still assemble the analytics you have into a Big Data strategy that will help you generate newer, better, and more-viewed content.
How Netflix Did It
Firstly, let’s consider how Netflix assembled the data they had into a strategy that has clearly worked. Obviously, Netflix tracks user data, but the extent of this data is larger than you might think. Beyond simply tracking titles watched, Netflix tracks how you watch shows, down to the amount of times you hit the pause button. Are you a marathon viewer? Do you take frequent breaks to check out other stuff? Do you leave episodes unwatched? Each of these data points factors into what Netflix recommends. Now, it also factors into what Netflix considers making for themselves and for you as a consumer.
The case of the Netflix version of House of Cards is definitely a perfect test case for the concept of using Big Data to build a sort-of network of interesting shows. House of Cards is adapted from a BBC drama from the 1990s. Viewers of the show also liked films directed by David Fincher as well as films that starred Kevin Spacey. Not only was an American remake with Fincher and Spacey a good idea, but the people pitching it had data to back the idea up. This made the decision to attach $100 million to the production of two 13-episode seasons a much easier one.
Another unique platform choice was the decision to premiere all thirteen episodes in one shot, allowing viewers to marathon the series like they would any other show on the Netflix platform. Essentially, Netflix is rewarding its viewers’s behaviors by giving them exactly the show they want in exactly the distribution method they’d like, not to mention gearing up for the premiere by finally acquiring The West Wing and letting me relive the glorious heyday of the Bartlett presidency. Anyway…
How You Can Do It
Now that you know how Netflix did it, you’ll definitely be wondering how you can do it for yourself. By harnessing the power of Big Data, you’ll be able to tap into bigger conversion rates, which will provide more eyes on your content. While building a human strategy that appeals to living, breathing people – and not just data – is important, the idea of analytics definitely isn’t going anywhere and it is absolutely a thing you need to be harnessing.
There are plenty of workarounds for dealing with your data. A great thing to try is analyzing your email newsletter list with Facebook. Through cross-referencing information from your newsletter list and the tools available via Facebook’s Power Editor, you can build targeted Facebook Ads campaigns that will provide a higher chance of good results. If nothing else, such a simple exercise is a great first dip of the toe into the huge ocean that is Big Data.
If you are dealing with a massive amount of analytic data, consider a solution like Infochimps. Infochimps sorts and processes all of your data – CRM, social media, even emails – into more user-friendly maps and databases to help you determine the next steps of your data collection.
However you do it, it’s absolutely important to consider the data. Knowing who is looking at your content, and what other content interests them, will help you to create new material to interest that audience. Not only will it keep your current audience interested, but it can also help to build a larger audience through word of mouth and other user-driven actions. Plus, those actions will create new analytic data, allowing the cycle to continue.
Between all this – programming shows based on analytics, building audiences through tracked data, and even the fact that data is so easy to collect and analyze – it all points to a key thing. Knowing how to plan for the future based on your data is important, especially when you consider that we’re living in it.
How are you planning for the age of Big Data?