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Why Netflix’s Recommendation Engine is Successful 27 October 2021


Did you know that the most successful OTT platform – Netflix, started its journey as a DVD distributor based on a subscription model? Not only that, in the beginning, it was only limited to the people of the USA, and they barely managed to survive. Fast forward to nearly two decades, Netflix has a revenue of 25 billion USD. The question is, what secret ingredient did Netflix come across that made such a huge difference? 

The answer is Recommendation Engine! 

We are not saying it, Netflix themselves made headlines in 2006, when they held a contest with 1 million US dollar prize money for whoever could beat their recommendation engine. 


What makes Netflix Recommendation Engine Unique?

Unlike other recommendation engines, Netflix’s recommendation engine relies on Machine Learning algorithms and Artificial Intelligence to decode their viewer’s behaviour on the website. 

Now, it is the year 2021, and customers demand a personalized home page as many other leading OTT platforms provide. So, whether you are a baby boomer in the industry or an established OTT platform with a set number of users, you still need a recommendation engine. Explore Alie – an AI-based recommendation engine that uses multiple algorithms to produce the most accurate recommendation, and it can be integrated into any website. With Alie, you will be able to increase your revenue and the time being spent on your website. 




Below are the  steps that Alie follows to create a personalized experience in an OTT platform – 

  1. Cluster Customers

Irrespective of whether a customer is new to the website, there is always a little data available with them, such as what they are scrolling through? Which trailer are they watching? What was the Keyword that they clicked through? Based on such information, Alie clusters each customer into different groups, which helps it to generate recommendations based on what other people are browsing.


  1. Understand the Business

As mentioned, Alie is an Artificial Intelligence-based Recommendation Engine, which makes it more intelligent than its competitors. So, before it starts recommending, it understands the target audience and what content does the website have . It then clusters the content on the website according to genre or other metadatas. 


  1. Generate Personalized Recommendations

Alie focuses on each customer individually and has a separate database for them. It knows each customer’s likes and dislikes, at what time they usually log in, which devices they are using, and the rating customers give. All of this helps Alie to generate the most accurate recommendations.


  1. Consider the Demographic Differences

When Alie clusters the customers into different groups, it may happen that a customer is listed in five different groups. When this happens, Alie takes into consideration the demographic criterion of that user. Sometimes, it also recommends movies or series based on what is trending in a country.


Example of how Alie Recommends on OTT Platform – 

*Considering only the aspect of different devices in this example*

Jenny logs into the OTT platform from two different devices – A laptop and her mobile phone. 

Jenny uses the Laptop to watch cartoon movies with her kids, and she uses the mobile phone to watch thriller movies. 

Alie understands this, and so when Jenny opens her Laptop, the recommendations are usually for movies for kids, whereas when she opens her mobile phone, the suggestions are for thrillers.



Wrapping up, we would like to recommend that if you have an OTT platform – no matter if you are a newbie or an established website, you should integrate Alie with your business. You can simply take the 14-day free trial and see what big difference a recommendation engine can make to your OTT platform.



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Kritika Verma is an Associate Content Writer and works with Muvi Marketing Team. She is an inbound marketing professional and ensures high-quality traffic on the Muvi website through her blogs, articles, and more. She has an engineering background but always had a knack for writing. In her free time, she is either on Quora or on (Mostly losing).

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