How Netflix Using The Power Of Data Science Technology

“Netflix and chill” has been the buzz word in recent times. A consumer survey by Flyx in India reveals that there has been a rise in both purchases of OTT subscriptions and viewing hours amongst Indian users. Over 50 per cent of respondents told that they had bought new subscriptions during the epidemic. It was also found that there had been a 5 times increase in those spending 16+ hours weekly and a 4 times boost in those spending 12-16 hours weekly on similar OTT platforms.

The study showcased that while Amazon Prime Video was the largely subscribed to platform closely followed by Netflix, the most trendy platform amongst users was Netflix with a 60% majority.

Data is behind the victory of many businesses, including Facebook, Uber, Twitter, Youtube, and essential others. Even the leisure world hasn’t been spared with its desirable quality. Netflix’s success story isn’t any different.

How Netflix uses Data Science?

Netflix gathers data from more or fewer millions of its subscribers and leverages that to its data analytics model to understand and determine different ends of buyer behaviours and buying patterns. Using such information it gives recommendations of movies and shows based on subscribers’ choices.

Reportedly, Netflix has affirmed that 75 per cent of its viewer activity is based on personalized recommendations. The online channel also uses several data points to develop a detailed profile of its users.

Moreover, Netflix also observes the time and date a user watches a show. It also keeps the record of scenes that users have watched repeatedly.

Through the use of historical Data, it predicts bandwidth usage to help decide when to reserve local servers for quicker load times during maximum demand.

Additionally, Python, which is the most excellent tool for many of the data scientists, is extensively used with Netflix’s broader Personalization Machine Learning Infrastructure.

This is done to train some of the ML models for critical aspects of the Netflix experience, including recommendation algorithms, artwork personalization, and marketing algorithms. The Python-focused Machine learning models are the nucleus of forecasting spectators size, viewership, and other demand metrics for every content.

As data exists in voluminous amounts, data science serves with ample possibilities to giants like Netflix to cater to their services with better prospects. In a world full of opportunities, data science is an indivisible and priceless part of Netflix’s success. Upgrading your skills in data science will definitely increases one's chance of getting high paying job in corporate.

The secret sauce of personalized recommendation:

Netflix uses Ranking Algorithms to provide a ranked list of movies and TV Shows that appeal the most to every single user. However, with the presence of various ranking algorithms, it is often difficult to accommodate all of them and test their performance simultaneously.

While the traditional A/B testing on a reduced set of algorithms could not identify the best algorithms with smaller sample size and also consumed much time, Netflix decided to innovate its algorithmic process.

To speed up the experimentation process of its ranking algorithms, Netflix implemented the interleaving technique that allowed it to identify best algorithms. This method is applied in 2 stages to make available the best page ranking algorithm to provide seamless, personalized recommendations to its users.

In the first stage, experimentations to conclude the member partiality between the two ranking algorithms is carried out. Unlike the A/B testing, where the two groups of viewers are exposed to the two ranking algorithms, Netflix makes use of interleaving to blend the rankings of algorithm A and B. 

Netflix provides its subscribers with augmented content based on this interleaving method that is highly responsive towards ranking the algorithm superiority.

For performing appropriate predictions, Netflix treats recommendations as a series classification problem. It takes participation as a sequence of user-moves and performs anticipations that output the next set of actions.

An example of a sequence problem is Gru4Rec. And in the case of appropriate sequence prediction, the contribution consists of the contextual user actions and the current context of the user.

Conclusion –

It’s not just Netflix. Numerous prominent companies are using the power of data and science to expand their business and customer base. This causes a massive demand for skilled data scientists.

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