How Netflix Save $1 Billion a Year with AI?

How Netflix Save $1 Billion a Year with AI?

Over the last 8 years, Netflix has grown worldwide in an exponential manner through its subscription-based streaming service. In April 2021, Netflix recorded 208 million subscribers, including 74 million in the United States and Canada. What’s noticeable for all the other giants is how Netflix leverages the power of innovative technologies to stay ahead in the game and transform customer experience routinely. But, what exactly is all this hype about the ‘Netflix save $1 billion a year with AI?’. Is it really a big deal to look at? Are there proven numbers to this statement?

Offering online streaming from a library of films and television series, including their in-house production labeled as ‘Netflix Originals’, Netflix has taken over the crowd as a lifesaver to tackle the lockdown boredom and being a market dominator in the world of streaming. And, currently, Netflix is using AI to dominate the market of entertainment and media. But there’s so much to it that remains a mystery.

So, let’s look at that now and try to find out the truth behind it.

The 60 Second Attention-Grabbing Rule

Upon deep diving into Netflix’s ideology, we discovered that it doesn’t only compete with streaming service providers to grab customer attention. Rather, it competes with anything and everything that tries to take away the consumers from Netflix’s services. From a customer experience perspective, streaming platforms are all about engagement in the first 60 seconds. A deciding factor involved from awareness to buying is quite crucial where a brand like Netflix sparks loyalty through personalized content. And how does it achieve that? The answer is AI- which helps in creating personalized content experiences thus satisfying the 60-second rule.

“Consumer research suggests that a typical Netflix member loses interest after perhaps 60 to 90 seconds of choosing, having reviewed 10 to 20 titles (perhaps 3 in detail) on one or two screens. The user either finds something of interest or the risk of the user abandoning our service increases substantially. The recommender problem is to make sure that on those two screens each member…will find something compelling to view.” – Carlos Gomez-Uribe, VP of Product Innovation and Neil Hunt, Chief Product Officer at Netflix

What’s the truth to ‘Netflix Save $1 Billion a year with AI’ Deal?

Netflix’s top-notch AI ‘recommendation system’ is the matter of the moment, a tool that basically saves $1 billion every year to the company under the indirect cost savings label. This AI-based recommendation system is responsible for reducing the customer attrition rate at Netflix and how? By adopting a personalized recommendation approach rather than a Popular recommendation approach. This means while most platforms may show videos based on their popularity, Netflix’s AI recommendation engine filters the movies and series based on each customer’s past viewing history, watch behavior, preference, and what they love. Now, one would be thinking if this is what makes the statement about ‘Netflix save $1 billion a year with AI’ so yes, the answer may be affirmative. And how so?

Interested to Read More? Go ahead with Future of AI in Streaming Platforms

Statistically, Netflix found that the take-rate on top personalized video recommendations was 3-4 times more than those of the most popular videos. Subsequently, viewers were exposed to 4 times as many videos using personalized recommendations compared to a list of the most popular titles. Thus, these recommendations not just help them retain their customers and lower their cancelation subscription rates but also help them surface what different needs to be produced in-house that isn’t usually on the traditional TV.

All -in all, the number stated here is just an estimation from Netflix’s financial savings which is claimed to be through its AI recommendation engine which cannot be put in hard numbers, Netflix has become even serious regarding its algorithm optimization and improvement. So, we may expect to see even better results and ideas in the future.

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