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DecisionNext Blog

I see what you did there, Netflix.

Published by Travis Martin February 12, 2019

"Where did the past 3 hours go?"


The last episode of 'Narcos' had come and gone, and now you're three episodes deep into 'Ozark,' a show you didn't even know existed. The culprit is Netflix - a repeat offender and occupant on your time sucker's 'most-wanted' list. The secret trick behind this all too common occurrence has bolstered Netflix to be able to compete and thrive in the modern era of streaming services. And there’s a business takeaway for us all, especially those of us in companies that make high frequency / high value decisions in commodity markets.

 

Commodity industry players...take notes.

 

While cost, ease of subscription, and original programming get a lot of credit, the secret ingredient in all of Netflix’s success is high-powered machine learning algorithms behind their 'Recommendation Platform.'

 

You may be thinking - 'How hard is it for Netflix to know that if I like to watch "House of Cards," that I will also like "The West Wing?" And, you’re right. That's not hard. But you have to be better than linking political dramas to hit this heavy metric: more than 80% of the TV shows people watch on Netflix are discovered through the platform’s recommendation system.In short, if you are a Netflix subscriber, the majority of the shows you select were served up to you by an algorithm, AND you watched the recommendation it gave you.

 

How does this all work?

 

While the concept behind an algorithm may seem complex, the Netflix process easily lays the framework. Netflix pays in-house or freelance 'taggers' to sift through its inventory of approximately 5,500 tv shows and movies. As they watch the content, they tag the content with labels. Like, a lot of labels. Need some examples? Here’s a shortlist:

  • high-action
  • actor/actress (ex. Jason Bateman)
  • ensemble cast
  • location (ex. Dallas)
  • damsel in distress
  • summer-time setting
  • voice accents
  • 17th-century costume design

 

The next piece of the puzzle involves user behavior. Was the content watched a day, a week, or a month ago? Was it seen in its entirety or abandoned after 10 minutes? Did the user hover over option A or watch its trailer before deciding to view option B? You get the picture.

 

The insights emerge once the user behavior is dumped into the machine learning algorithm with the content tags. The end result is the string of tv shows that float near the top of your browser under “Watch Next” that will steal all 'free time' you have available in the next four days.

 

Netflix leadership credits the tool for minimizing friction or delay in the user's experience. When a seamless choice rolls up on the screen, the user doesn't spend seconds or minutes scrolling through Netflix’s menus to find what fits their interests. Or, if a rather unexpected recommendation is brought forward, the user can explore an option served up by implicit, behavioral data.

 

Can I hire Netflix to help me make business decisions?

 

The recommendation platform allows for easier decisions to be made by the Netflix user. If only our business decisions could be aided like that, am I right?

 

In commodity industries, high-frequency decisions produce a lot of data... like, a lot. The value in having an intelligent tool in your back pocket powered by both your internal, transaction data (your behaviors) and external, third-party data (the tagged content insights) could be a game-changer for your business. When you combine the forecast accuracy potential with an industry expert, the value increases exponentially.

 

So you might be curious why 'The Great British Baking Show' popped up as a suggestion after you topped off that last episode of 'The Crown,' but the data advises that you should give it a shot (British accents may be trending in your queue). And the fact that Netflix has the ability to influence the next thirty minutes (errr, two hours) of your time proves the power of machine learning, and how we all need to be considering it as table stakes in the parts of our lives where forecasts and decisions play a pivotal role.

 


A machine learning algorithm in meat? How can your purchasing, pricing, and sales teams have better market visibility? Devour our case study below to learn more.

machine learning meat industry