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How do you watch movies and videos online? On YouTube on your PC? Netflix in the mobile app? Or maybe a better way to phrase it would be how many streaming services do you use to watch your recommended movies and videos?
Video streaming companies like Netflix — also known as over-the-top (OTT) media services — have come to dominate the entertainment business, transforming not only consumers’ viewing habits but how the industry operates.
Much like Spotify in the music sphere, OTT media services make use of a now-common approach; a sort of “winning formula.” It looks like this: relatively inexpensive monthly subscription fees after a free trial, then expansive libraries of recommended content and highly personalized user experiences.
Here’s a look at how streaming services generate personalized video recommendations for users, why it’s important to get content recommendations right, and how your business can use this knowledge to improve your customer experience.
When talking about high-quality personalized recommendations, people often focus on one aspect: how the recommendation algorithm works. For many, the algorithm — a set of step-by-step procedures or rules to follow — takes on an almost mythical form. For others, it’s the impenetrably complex brainchild of a bunch of Silicon Valley geniuses. Either way, it’s seen as a deeply guarded secret of a company’s success.
But these algorithms aren’t as mythical as you might think. Algorithm functionality isn’t restricted to elite organizations that have the smartest computer scientists. And you don’t need to compete with Google to hire the best engineers. This technology is available to any business though a movie recommendation API that you can easily integrate with your products and services. What’s more, this technology can help businesses boost revenue by attracting and retaining highly engaged, satisfied customers.
It’s no surprise that Amazon is one of the best companies at this. It’s arguably Jeff Bezos’s obsession with the customer and his vision for Amazon to be “Earth’s most customer-centric company” that led to this emphasis on the user experience. Recommendations are essential today because customers expect and demand highly personalized experiences related to every product or service they use.
It makes a difference beyond just a great customer experience, too. Research by McKinsey found that organizations that do personalized customer experiences right enjoy 40% higher revenue from it.
This is particularly important for video and movie streaming services. With so much choice — think of Netflix’s vast library of movies and TV shows or YouTube recommendations — recommendations have become an essential way to provide a winning customer experience. Otherwise, users would keep browsing, until, overwhelmed by choice overload, they decide to do something else: cook, go for a drive, play Wordle. Or check out a competitor’s video content.
To illustrate just how important all of this is, it’s worth quickly outlining a business case for recommended videos. Movie and video recommendations are essential because they:
So how does a recommendation engine work? We could get technical about data science, but that discussion’s not going to add a whole lot of value. What’s worth knowing at this point is simply that companies like Algolia provide movie recommendation APIs.
But since you asked, here’s a high-level overview of the basics.
There are two ways to generate video recommendations:
This approach considers a customer’s preferences (likes, dislikes, activity) and recommends similar content based on their movies. If a customer is watching Jurassic Park over and over, the movie recommendation system would suggest related videos, like maybe another dinosaur movie or franchise. To make this work efficiently, you need to tag and characterize your content effectively, e.g. include the genre, sub-genre, director, actor, and show length. This way, the recommendation engine can serve up playlist content that your customers will be keen to watch.
This technique makes use of past interactions around the type of movies a user likes, but it also takes into account the preferences of other users. By making connections between multiple users’ preferences, it can predict what other viewers might like. And when you do this at scale, you can come up with some pretty accurate and interesting suggested videos and movies.
Sounds great, but isn’t it complicated to build a recommendation engine from scratch?
No, not if you choose the right solution to take care of the technical aspects for you.
Algolia has perfected a movie recommendation API that’s a breeze to implement and adapt so that your business can begin providing personalized video recommendations right away. With Algolia Recommend, your developers will gain a simple and robust API to deliver the recommendations experience your customers expect.
To learn more about how a movie recommendation API works and how to improve video and movie recommendations for your customers, contact us today.