You might not have found your all-time favorite show yet, but data science will connect you to it.
According to Netflix, most of its subscribers around the world share six common shows that they watch. Lots of its members gasp while watching Stranger Things, cry happy tears over Queer Eye, and tidy up with Marie Kondo. Since shows like these are so popular, most Netflix users are likely to see any or all of them in their Recommendations lists.
Alongside these listings though, are recommendations for other shows and movies. These are based off of data Netflix has stored on users similar to you, as well as your individual behaviors when using their service. Their data science keeps you engaged by hooking you on both popular recommendations and niche shows that satisfy more personal interests.
Machine Learning + Human Behavior = The Netflix Power Pair
Netflix serves you richer recommendations by blending machine learning and an analysis of smaller scale human behavior. They analyze thousands of data points– from what genres you enjoy to what styles of images you’re more likely to click on– to deliver what they think you want to watch.
Adding an analysis of human behavior is what helps break any loops that machine learning can create. People do totally random things, and this doesn’t cease when they Netflix and chill. Netflix accounts for the random stuff people do.
For example, let’s say that watching a British game show isn’t something a user normally does. Then this user starts watching it, despite typical behaviors. This game show selection might be a behavioral outlier for that individual, but it could indicate other shows he or she might also enjoy. Beyond that, it could also indicate that folks within that individual’s demographics also might enjoy similar content. Random human behaviors like this help Netflix learn what interests its subscribers, eventually leading them to push content that is so well-received.
Can this type of machine learning be applied to social media advertising?
Simply put… yes. And it is be applied to social ads already.
Digital advertisers are using data science and machine learning to predict how to get consumers to interact with ads. By analyzing millions of data points, including outlier and more generalized behaviors, our data science is already able to tell advertisers what creative to feature in their ads, how long to run those ads, and how to optimize their spend. This analytical blend of machine learning and human behavioral science enriches our data and allows us to make recommendations on ads in very dynamic ways.
If you want your ads to be as enticing as your Netflix Recommendations list, you can get 3 free and custom tips from our AI. We hope its powerful insights can make your ads as clickable as the links from your favorite streaming service.