NETFLIX AND ITS USE OF AI, ML, ANALYTICS

Netflix and Chill. Isn't that what we do after a long day of work, stress, and deadlines? Sitting across the television with your legs stretched, favourite pillow, comforting blanket, and a steaming bowl of Maggi, maybe, is the heaven we all want to accede to. It feels we are all set to relax and enjoy but wait; something's amiss. What to watch? Should we go back to the same old shows we always watch or start something new. Starting new means picking a new series, and man is that a tough job!

Here is precisely where Netflix has put its brains to recommend shows that you will grow to love and watch dearly. Easy, right? But the question is, who told Netflix what you want to watch? How does Netflix know what you want to watch when you are unaware of it? This is the magic that AI, Machine learning, and data science combined.

The process of watching shows and movies on Netflix does not begin with them being recommended to you or you selecting which ones to watch. It starts with which shows does Netflix shortlist to feature on their platform. If you have wondered how Netflix decides which TV series from which part of the world to be made available, the answer is simple. It is not based on which shows it thinks people might like, nor is it based on whose licence the OTT streaming service can get easily. It is a well-researched decision backed by data and algorithms.

Analytics at Netflix

Before delving into analytics at Netflix, let us understand what analytics is, and what it does. The primary role of analytics is to help organisations be more specific in marketing and customer relationships. Analytics is crucial to organisations as it helps them gain better insights to make more informed decisions.

Owing to its large number of subscribers, Netflix has access to a tremendous amount of data. This data is what helps Netflix win among its competitors. The data collection does not end with who watched what and whether they completed the entire series. Data collection is deeper than it seems on the surface. So what does Netflix keep a record of when you are busy shuffling through shows on the platform? Netflix knows the what, when, and where of your bingeing. They also track when you hit pause, rewind, or forward and at what frequency. They know whether you will continue watching a particular show or leave midway. They want to keep you hooked to the screen, and data analytics help them do that.

A lot goes behind the screen to help viewers stay glued.

The recommendation system

With millions of users worldwide, Netflix uses its data and critical machine learning algorithms to build a user's watching pattern. They use a near real-time recommendation system based on an ML algorithm to suggest new shows and movies to its users. Each recommendation list is as unique as each user. The recommendation system considers previously watched content by the user and people with similar preferences as the user.

Netflix categorises similar users under one category. They use the ML model to create a relationship between this unstructured data. Collectively this data is used to create interest groups. The spatial relationship between different users keeps up the recommendation game at Netflix. Algorithms like Personalised Video Ranking, Trending Now, Continue Watching, and Similarity Ranker power the recommendation system.

Also, the Post-play feature, which automatically starts the next episode and recommends movies as soon as the credits start rolling, helps strengthen the recommendation system.

Countless times, you must have just scrolled through Netflix without picking a show/ movie to watch. When you are unsure of what to watch and are on the verge of closing the application, Netflix turns into a knight in shining armour. The recent 'Play me Anything' feature is to help recommend shows to the users based on their interests. This feature is strategically placed after scrolling through the most trending shows on Netflix. If a user has still not found anything to watch, there are high chances they might exit the application. To eliminate this possibility, the Play me anything feature has been introduced. Nothing on Netflix is random. Play me anything feature, too, is based on algorithmically curated content, watch history, and likes.

This feature, like Captain Raymond, specifically requests us to have a good time.

Can you imagine that personalised movie thumbnails exist as well? The Office can feature Michael Scott as the leading thumbnail guy or a cute picture of Jim and Pam, all based on what you want to watch. Netflix creates the most probable scenario for picking a particular movie and show. Artwork and Imagery Selection is crucial in depicting the exact motivator for movie/show selection to the users. Aesthetics and Visual Analysis (AVA) goes through each video to decide on the specific frame to be used as artwork. For each frame, metadata is created to assort the highest quality, click-worthy images. A lot of quantitative and qualitative analysis is done before finalising the images, such as what images to show to whom, the actor's expression, positioning, lighting, etc.

At times, misleading thumbnails have been reported as well. A particular actor might have less screen time, or a movie might not be what the thumbnail suggests. This happens because of an over-do of ML algorithms. In statistics, such a consequence is termed Type 1 false-positive error. An example of this could be the use of Anya Taylor Joy's picture to promote Peaky Blinders. The British show featuring Cillian Murphy in the lead role was represented by The Queen's Gambit lead even though she did not feature in the series until the last season. This change was to drive the watchers of Queen's Gambit to Peaky Blinders.

Buffering, what?

Youtube, Amazon Prime, Hotstar, etc. All these OTT platforms have one thing in common: buffering. None of them can ensure consistent streaming quality throughout. Netflix does this. Netflix has forever guaranteed a glitch-free, buffering-free best streaming quality. I cannot even remember the last time I had to stop watching Netflix because of a streaming quality issue. You might think that this depends entirely on your internet connection, but that is not wholly true. Netflix has a large customer base, and they have carefully understood the viewing patterns to predict the increase and decrease throughout the day. They cache the regional servers in the users' vicinity to ensure no loss of streaming. The data for the same is collected via ML algorithms.

Netflix has also created a supervised quality control algorithm. This algorithm quality checks the sound and video quality along with subtitles and reports shortcomings if any. In case of discrepancy, the content quality is checked manually.

Rating system

Remember before 2017 when Netflix wanted you to give scaled ratings of a show. Well, they have moved past it now. It was wise to move from the rating scale to a thumbs up- thumbs down system. The response to this new rating system is naturally more as it requires less thought and therefore helps Netflix stay relevant. All this rating allows Netflix to create metadata and study its interrelationship.

The percentage match label below a movie or series is Netflix's own little cupid game where they predict how fitting that show and the user are. This is based on the information generated from the spatial maps and mathematical relations among various interest groups.

Location

Most Netflix Originals are shot on location, which the Netflix AI suggests. It helps optimise content from an operational and financial standpoint. The use of analytics at Netflix is quite extensive and aggressive. Shows like Emily in Paris are an instant hit because Netflix can correctly predict that people will love the location and content. Cost projection and bottleneck elimination during filming are also dependent on data science.

In conclusion

Netflix has outdone itself in the amalgamation of AI, data science, and machine learning in the 'right' way. The trio has helped users get personalised ad-free streaming services and Netflix to generate huge revenue. Netflix's use of AI, ML, and analytics is a case study in the making. Their product-driven approach has helped them save $1 bn annually.

They have focussed on their content and its positioning using analytics and are winning the game.

Netflix has made our choices so easy! All we have to do is turn on Netflix and start watching!

Remember, If Netflix says you might like it, you definitely might like it.

Cool, cool, cool, cool, no doubt, no doubt.