Netflix used data analytics to transform itself from a DVD rental company in 1998 to a streaming industry titan nowadays. This is an intriguing story that we will explore in the third part of our data science educational series as an illustration of how data science can be used in real life.
Netflix realised the power of data from the outset, particularly when it comes to understanding customer preferences. This led them to create a recommendation system that is likely one of the most robust, not only encouraging their competitors but also setting them apart from the competition and propelling them to where they are today.
Netflix's success was not an overnight event, but rather a process that required a year of careful consideration of how to transform user data into golden information, to increase engagement and revenue. Diving into this story will make you look at the data you generate or interact with in new ways.
Transitioning from a million-dollar recommendation system to a streaming services titan
Netflix decided to work on improving their recommendation system to make more money and stay in business. This more personalised content suggestions system used information about the user, like what they've watched and rated, to guess what other films they might like.
In 2006, Netflix even held a contest where the winner would get $1 million if they could make their selection algorithm 10% more accurate. Eventually, a team won the Netflix Prize after successfully boosting Netflix's algorithm by slightly more than 10%. Even though they had some problems at first putting it to use with their larger dataset, it ended up being worth the money they spent.
The following year, when they switched to streaming, user data became an even bigger part of their growth. They took full advantage of the opportunity to gather a lot of information about things like watching habits, device use, and even times when viewers paused, rewound, or quit shows. With this level of detail, Netflix was able to better understand what its viewers liked. This helped them not only to make suggestions but also to choose what material to buy, make, and promote.
Data science-guided content creation
One could say that Netflix's most innovative use of data science came to light when they started making their own shows.
They were given extensive insights into viewer preferences through granular data collection, which gave them the ability to predict which shows would be successful. A great example is the development of "House of Cards"; using data analysis, they discovered that there was considerable overlap among fans of the original British series, fans of the lead actor, Kevin Spacey, and fans of the director, David Fincher. This confluence of characteristics, backed by data, led Netflix to confidently invest in the series without a pilot, which was a risky move in the industry but has proven to be incredibly profitable.
Onwards toward greater things
With the development of many advanced technologies for analysing data that use complex machine learning models, it's hard to imagine Netflix giving up on their quest to use data to grow their business. That's why they keep improving their use of data science by using machine learning and artificial intelligence to make better content suggestions, improve streaming quality, and even have an effect on creative decisions. One can only imagine what is yet to come but it goes without saying they have done an incredible job using data as they did.
If you've ever wondered about the magic of data science, now you know!
May this inspire you in your endeavours, and please share and contact us if you need assistance turning your data into gold! Alternatively, you can learn how to do it yourself by keeping an eye on our upcoming training.
FAQs
One could say that Netflix's most innovative use of data science came to light when they started making their own shows. They were given extensive insights into viewer preferences through granular data collection, which gave them the ability to predict which shows would be successful.
How did Netflix use data science? ›
Netflix utilizes data science to identify trends and predict which shows and movies will captivate audiences. By analyzing user behavior and preferences, the company can make informed decisions on what content to produce and how to market it.
How did Netflix use data science to improve its recommendation system? ›
Netflix's recommendation system is a sophisticated blend of machine learning algorithms, that analyze user data and reviews to create personalized suggestions. With 1,300 clusters aligning with viewing interests, users receive customized content recommendations within 90 seconds of opening the platform.
How did Netflix use big data and analytics to generate billions? ›
Recommendation Engine: Netflix developed a robust recommendation system powered by machine learning algorithms. By analyzing vast amounts of user data, including viewing habits, ratings, and preferences, they personalized content recommendations for each individual, increasing customer satisfaction and retention.
How does Netflix use data science for better user experience? ›
Optimizing Streaming Quality
That's data science at work. Netflix uses adaptive streaming, which ensures a smooth viewing experience. If your connection is slow, it reduces video quality to prevent buffering, and when the connection improves, it bumps up the quality, all without you even noticing.
How data science is boosting Netflix? ›
One could say that Netflix's most innovative use of data science came to light when they started making their own shows. They were given extensive insights into viewer preferences through granular data collection, which gave them the ability to predict which shows would be successful.
How does Netflix use data analytics to make decisions? ›
Netflix uses data analytics to make decisions about creating or purchasing content. By collecting and analyzing data on user behavior, such as the location of a user, content watched, user interests, and search data, Netflix's algorithm provides personalized recommendations based on user interests .
How did big data become important to Netflix's success? ›
Big data has helped Netflix massively in their mission to become the king of stream. Big data helps Netflix decide which programs will be of interest to you and the recommendation system actually influences 80% of the content we watch on Netflix.
How does Netflix use data to increase its revenue? ›
Using advanced data and analytics, Netflix is able to: Provide users with personalized movie and TV show recommendations. Predict the popularity of original content to before it greenlights it (or not) Personalize marketing content such as trailers and thumbnail images.
How does Netflix use the data they collect? ›
If you sign up for an ad-supported plan, Netflix will use your date of birth, gender and general location information (based on your IP address) to tailor advertising for your demographic.
But Netflix's strength wasn't putting DVDs in the mail. Rather, it was the company's use of predictive analytics. Netflix software engineers developed algorithms to steer customers away from high-demand blockbusters … and toward its plentiful, lesser-known library titles. This strategy was a huge success.
What is Netflix analytics strategy? ›
Analytics at Netflix leverages a diverse set of skills (Problem Framing, Data Engineering, Data Science, Consumer Research, Visualization Engineering, and more) to connect dots across domains and respond to complex business challenges with innovative analytic solutions.
How has technology helped Netflix grow? ›
Beyond just content production, Hastings also spearheaded the use of big data and algorithmic recommendations to enhance user experience. Netflix's technology could analyze viewing patterns and preferences, allowing it to suggest shows and movies tailored to individual tastes.
What is the competitive advantage of Netflix data? ›
In conclusion, Netflix's competitive advantage and market dominance can be attributed to its vast content library, personalized recommendation algorithm, commitment to innovation and technology, global reach and expansion strategy, investment in original content, subscription-based business model, and strong brand ...
How does Netflix work with data? ›
The longer the film, the more data you use. The resolution you use also affects the amount of data you use. According to Netflix, you use about 1GB of data per hour for streaming a TV show or movie in standard definition and up to 3GB of data per hour when streaming HD video.
How is computer science used in Netflix? ›
We develop computer vision (CV) algorithms, in connection with audio, and natural language processing (NLP) algorithms to analyze and transform our raw media sources into a diverse set of assets such as artwork, video trailers, and metadata, and also enhance the productivity of the creative process.
