There’s no doubt that Netflix has single-handedly changed the meaning of entertainment. Not only does this platform have one of the most extensive subscriber bases across the world, but it is also one of the top companies with an iconic growth story. In fact, within one year, Netflix grew by a whopping 25%. Today, this dynamic video-streaming company is competing head-to-head with Disney, an age-old player that is the most-valued entertainment company worldwide.
But, what is the secret to Netflix’s success?
You must have noticed that when you visit the Netflix application or website, the platform recommends movies that are almost always to your liking. This is one of the primary reasons why people keep coming back to the platform, despite the emergence of new and less pricey streaming services. And, this is where data science plays a major role in the success of this exemplary streaming-service.
In today’s article, we are going to discuss the role of data science in Netflix’s success and what systems this platform uses to stay relevant for its users.
The role of data science in Netflix’s business model
The value of data science services has grown exponentially among every industry in the past couple of years. Many businesses strive to accumulate user data to reach and engage their potential customers more efficiently. Arguably, Netflix is the prime example of a company that uses data to define its success.
Not only does Netflix receive a million ratings, searches and views a day, but it also witnesses millions of hours of content streamed per month. Yes, that’s a lot of data. But, this is also the reason behind the streaming service’s success.
Before we delve into how Netflix uses data to become a prominent player in its niche, let’s first get a brief background about it’s journey started.
Reed Hastings and Marc Randolph started small with a DVD rent-by-mail website, which surprisingly was a great success. As their company grew, they wanted to venture into new markets; however, they didn’t have sufficient funds for new releases. So, with an aim to de-emphasize popular titles, the founders ventured into various algorithms that diverted the user’s attention from popular titles to their own interests instead. Basically, they told the users to watch old content that was sure to be of their liking instead of looking for brand new movies that they may or may not like.
Back then, investors didn’t understand or necessarily agree with this approach as they assumed that only popular titles that were box-office hits would result in successful media platforms. But, in 1997, Netflix changed the dynamics entirely by making use of data to change the way people consumed media.
Once they had a set model, which they proved worked wonders, there was no stopping them. That is when they realised the importance of data, which is why, in 2006, when Netflix wanted to tap into the streaming market, they started off with a competition for movie rating predictions with prize money of $1 million. They were looking for someone to increase the accuracy of their platform by 10%. That’s when the BellKor team created a solution that increased the accuracy by 10.06% using data analytics.
Using this solution, Netflix went on to collect data for six years, following which they created their first show; House of Cards, which was a phenomenal success worldwide.
Since then, nothing has been able to stop or hinder the growth of this platform. Using the same methodology to create content, Netflix has managed to earn a success rate of 80% on an average compared to the 30-40% success rates of content on TV shows.
The secret behind Netflix’s success
In a nutshell, the secret behind Netflix’s success is its ability to collect and use data effectively. In fact, according to Netflix, the platform earns over a billion in simply retaining their customers as their popular recommendation system accounts for 80% of the media that is streamed on their website and application.
In addition to this, Netflix also makes use of big data to determine what type of original media content should they create. For example, Netflix invested quite a huge fund in ‘Orange Is the New Black,’ as they knew it would be a huge hit due to their previous hit ‘Weeds.’
To further understand how Netflix leverages the humongous amount of data it colleges, let’s look at the most popular systems the platform uses.
The recommendation system
Of course, the recommendation system isn’t exclusive to Netflix, and industry bigwigs like Amazon and Google have been using it for a while now. Essentially, as the name suggests, this system is used to provide users with recommendations based on their previous interactions, preferences and likings.
The system takes the user’s information as an input and then processes this information to predict the likeliness of how much the user would like another product. Ideally, a recommendation system is created using a host of various machine learning algorithms.
In the case of Netflix, the recommendation system goes a step further by attempting to predict the similarity between different content and if the viewer would still enjoy the media. Suppose a user is new to the platform, and the system has no prior information. In that case, the system takes into consideration various factors like the location, age, gender of the viewer to make generic recommendations.
Interleaving for personalisation
Netflix is known for its multiple ranking algorithms, which are also commonly known as the various categories present on a user’s homepage. For example, the Top Picks algorithm has a list of content that is recommended based on the user’s previous interactions, while the Trending Now algorithm uses the recent popularity trends to determine which content to display.
All of these algorithms come together to build custom homepages for over 100 million different subscribers present on the platform.
With such a large volume, it’s practically impossible to use the traditional form of A/B testing to determine what works and what doesn’t. Hence, Netflix uses Interleaving in the first stage of their experimentation to determine which algorithm the user prefers.
When it comes to traditional A/B testing, ideally, two groups are formed, and one group is shown the rankings of algorithm A and the other algorithm B. On the other hand, Interleaving displays a blend of both algorithms; A and B, to a single set of users. With the help of this, both the options can be displayed side-by-side, and the user is free to pick based on their liking.
Based on the reaction of the users, the platform calculates the relative preference, depending on which the content displayed to the user gets further personalised.
Conclusion
Over the past few years, data science has proven time and again that data can completely revolutionise an entire industry. The Netflix success story is a prime example of this. In fact, the use of big data and analytics is so prominent at Netflix; it should be known as an analytics company instead of a media house! In a nutshell, Netflix is a prime example that by deriving insights from data, companies can make smarter decisions and deliver maximum ROI to their customers.
