N Netflix has over 183 million subscribers, and these numbers are growing both consistently and exponentially. Even though Netflix took some bold and controversial steps in the past years, as its decision to flat out block VPNs in the year 2016, and a much-opposed prize hike in the same year — -thus losing a huge chunk of its subscribers — -it still came out as the largest subscription streaming platform.
How?
All the credit goes to data science: big data and algorithms.
“As Thomas Redman said, “Where there is data smoke, there is business fire.”
– Thomas Redman
Introduction:
Netflix is the largest subscription streaming service today. It uses data science to not only gain new subscribers but also to enhance the user experience of the existing ones. Netflix knows what its subscribers want to watch, even before the subscribers themselves. Thanks to data science.
It uses various traditional business intelligence tools, along with big data technologies to come up with revised algorithms that enhance the user experience, and thus its profit. Shows like House Of Cards and Orange Is the New Black was not gambled, but the results of well-synthesized data. Personalized watch lists and artworks are the secret ingredients.
Some mind-boggling facts
- Thanks to its algorithms, Netflix saves an estimated 1 billion dollars every year!
- Back in 2006, a DVD mailing company launched the Netflix Prize. It offered 1 million dollars to anyone or any group who could create the best algorithm for determining how the subscribers would like any show or movie based on the previous ratings.
- 80% of the content streamed on Netflix is based on a recommendation system.
- It made swooping 20 billion dollars in 2019 alone. Credits? Data science.
- 49% of its users are paying for their subscriptions; that is, they are not sharing passwords and accounts. The majority of the users pay for their subscriptions.
After going through all these facts, you probably might be wondering, how come an average DVD mailing company grew up to be such a huge success — the largest and most significant of its kind. Keep reading, and you’ll find out soon.
“Big Data is not about the data. It is about analytics.”
– Gary King (professor at Harvard)
How Netflix harnesses excellent user experience and dollars from mere stacks of data
Although data is a primary need, data without proper processing is mere piles of useless information. The real game begins when you process this data to yield useful insights that bring both customer satisfaction and profit.
Netflix uses various traditional business intelligence tools (like Teradata and MicroStrategy) and combines them with big modern data technologies such as Hadoop, Hive, etc. It thus forms algorithms that predetermines what the users are most likely to watch. It also uses much open-source software for the same. So basically, what matters more than just having the data is knowing how to channel it to give favorable outcomes, both for your business and for the customers.
“Big data means both more and less to a data science team. It’s more storage than you’re used to handling, so you need to look to alternatives. It’s more processing than you’ve ever had to do before. It’s more skills that you need to find correlations and surprises. A lot of tools that can handle this kind of analysis don’t work like you might expect them to. It’s a new world, and you need to explore it in ways that work for you.”
– Justin Ward (manager of the data science and engineering team, Netflix)
Personalization is the Key!
Netflix has almost 200 million subscribers, who generate an immense amount of data every single hour of every single day. It would be foolish not to use all this insane amount of data for one’s profit.
Unlike the television networks that have only a vague idea of what their viewers want or like, Netflix has a robust individual database for all its subscribers. All thanks to data science. It won’t be wrong to say that there are 183 million-plus versions of Netflix. This personalization not only enhances user experience but also helps predict what series or movie will earn them most subscribers and thus profit.
Personalized Video Ranker
For the very same genre, there are entirely different video recommendations for different users. This is because Netflix personalizes the entire Netflix collection for every subscriber. Because they know, every single person has a unique taste. Even the contents of the trending section are personalized.
Artwork Personalization at Netflix
The primary purpose of Netflix’s personalized recommendations system is to put up the most suitable titles in front of the right audience at the right time.
But the role of recommendations doesn’t end here. The title should be made compelling enough for the viewers to click on it and watch what it has to offer. This is where the artwork comes into play. The artwork gives a kind of visual proof to the audience about what the title holds and hence helps them to decide whether or not to watch the show or the movie. Netflix doesn’t just offer a product to 183 million-plus people. It provides the product in 183 million different ways.
The artwork you see is there because it contains something that YOU may find appealing. It may highlight the face of an actor you are likely to recognize, or it may show a scene that you may find interesting.
For instance, let us consider Dead Poets Society — a person having a watch history of several romantic movies maybe shown an artwork highlighting Ethan Hawk. At the same time, a person having a search history of art or history movies perhaps shown a scene from the cave. And, a person having a watch history of high school drama, maybe shown a view from the classroom.
Now, the question is, how do they achieve it? Through AVA, short for Artwork Visual Analysis.
AVA (Artwork Visual Analysis)
It is an algorithm that goes through available videos and selects images that suits them best. AVA takes into consideration several things such as scene lighting, what is highlighted in that scene, object placement, background, etc.
How is it personalized?
To personalize and provide content to such a broad audience is not a piece of cake, especially when that audience is growing exponentially every day.
As mentioned above, there is various software that is used for creating algorithms that personalize Netflix for all its different subscribers, yet here are some points that its technology pays attention to while personalizing it:
- What kind of content you watch.
- At what day and time you watch it.
- On what device the show or movie is being watched.
- Was the content paused, rewind, or fast-forwarded?
- Searching, scrolling and browsing behavior
- Your location, etc
Pre green light. Post green light. And the role of personalization.
By now, you must be sure of the fact that each and everything Netflix does is based on algorithms and data science.
Netflix’s smash-hit series such as House of Cards, Orange Is The New Black, The Crown, etc., were not gambling, but the outcomes of synthesized data insights.
Netflix knew that these are going to be extremely popular even before they aired. How? You’ll soon find out.
A more in-depth analysis of House of Cards
Since it was launch in 2013, House of Cards has held and maintained an 8.8 IMBD rating. Not only this, but Netflix also gained 2 million subscribers in U.S.A alone and a rest 1 million internationally within three months of the launch of House of Cards.
But unlike the popular and safe trend of that time, Netflix didn’t launch a pilot. Instead, it commissioned two seasons of the show in advance even before its first episode aired!
Sounds like big gambling? A business risk?
It was not. Netflix had data. It knew that its 33 million users had loved director David Fischer’s work, The Social Network. It also knew that films starting Kevin Spacey did well on its platform. The icing on the cake is that the British version of this show did well on Netflix. And so, they were confident of this show’s success.
Till now, data science was not used for personalization. Soon, the personalization part will come into play. Personalized trailers: Now that Netflix has invested money in the making of soon going to be hit show and has given green signal to its making, it’ll now make personalized trailers.
Yes, Netflix made ten different cuts of the trailers for House of cards, keeping personalized preferences of the users in mind. All of this, when combined, gives the ultimate user experience and ensures that Netflix retains its position on the top.
Netflix: The Jack of all trades?
Netflix not only uses data science to produce, stream, and recommend personalized shows and movies but also launches and then streams as diverse content as possible.
Apart from numerous popular Netflix originals such as The Witcher, Stranger Things, etc., it has bought streaming rights to popular old shows such as Seinfeld.
It also streams documentary movies such as American Factory, which was nominated for Oscars.
Netflix has also signed exclusive mega-deals with superstar producers like Shonda Rhimes, Ryan Murphy, etc.
This vast diversity of content is not just for flexing, or because it can buy or produce all this content. This is because it serves an audience of almost 200 million, and knows who likes what. Again, thanks to data science.
“Everyone with a phone has a screen and access to the internet. That is our addressable market. The world’s taste, and the world’s time, is what we’re after.”
– Reed Hastings (CEO of Netflix)
But how do they manage to achieve this?
Again, Data Science!
Netflix very wisely uses the following (to name a few) and many others to ensure excellent user experience as well as its consistent growth:
· Durid
Apache Druid is a high-performance real-time analytics database. It’s designed for workflows where fast queries and ingest matter. Druid excels at instant data visibility, ad-hoc queries, operational analytics, and handling high concurrency.” — druid.io
Durid helps provide Netflix with real-time data and analytics about various things that are needed to achieve high-quality user experience.
Durid provides excellent performance for any amount of workload.
· Python
The twitter handle of Python tagged Netflix and tweeted that Python helps Netflix reroute nearly one-third of the internet traffic in under ten minutes.
Netflix uses Python for many things, including the following:
- To maintain information security.
- Analyze traffic distribution and operational data.
- Prototype visualization tools, etc.
· Metacat
Netflix operates on a vast pool of data. This data is poured upon several different platforms such as Amazon S3, Durid, Redshift, etc. To maintain interoperability, that is, to keep smooth exchange and use of information among these various platforms, it needs Metacat.
Metacat provides centralized Metadata access for all data stores.
Conclusion
Data science plays a significant role in not only the basic functioning of Netflix but also helps it maintain and grow its subscribers. Netflix is the epitome of using data science to achieve business excellence.
Coming from a humble beginning of rent by mail DVD company, to becoming the largest subscription streaming service, Netflix has come a long way. It was not sheer luck that brought this success at its doorstep. Proper synthesis of data is what did the magic. All hail data science!
But it is not always smooth sailing. More often than not, technologies bring with them specific issues. But Netflix has successfully tackled all the problems and came out reasonably well.
Although you might not have as many resources or data like Netflix, you can too use data science to give your business a better direction and growth.
I’ve always taken life as a journey from one experience to another. So far it has been a road full of interesting events and people. Join me on my Journey through LinkedIn, Instagram & Youtube
I hope this helps & all the best for your future endeavors! Thanks for reading this article! Leave a comment below if you have any questions.
