We’ve all spent time browsing through the different movie options that Netflix presents to us. If you have a background in data science, you would know that various processes are at play for what looks like a rather simple movie recommendation. Using processes such as data mining, predictive modeling, and machine learning, the data scientists at Netflix analyze historical data and identify patterns that can be used to predict future outcomes.
Fun fact: Netflix credits 80% of its user retention to its recommendation strategy that keeps viewers hooked onto the platform!
To do this, Netflix has to break down a host of actions taken by the viewer when watching or choosing a show. Here are some of the data points that data analysts will collect, clean, and interpret in order to derive insights which will then be used to create and modify their algorithms:
- No. of times the show was paused
- The device used to watch it
- The time and date it was viewed the most
- How many people completed the show
- Keyword searched for the most
What’s more, Netflix even keeps a record of the scenes viewers rewinded the most. All this information, once collected, helps data analysts make a detailed profile of what you, as a viewer, may be interested in.
But it doesn’t end here.
Now that the data scientists at Netflix know what kind of shows you are likely to enjoy, they make sure to entice you to watch them. Netflix creates customized previews of a show for every kind of viewer. So, if you are someone who binged on a number of comedy shows or sitcoms, you’ll be likely to preview more light-hearted scenes of a similar show.
Netflix also creates various posters for a show or film. Each viewer gets to see different versions of the poster on their recommendations feed. This is also dependent on the kind of shows you’ve watched the most. With all the information in place, Netflix not only predicts which show is going to be a better fit for your taste but also aligns in which order they should appear to you when you open the app.
Here are 3 examples of how Data Science is used at Netflix
1. Using big data for recommendations
As we learnt in the section above, Netflix uses a series of algorithms to analyze the data it collects about your viewing habits. Based on this data, Netflix can predict what you are likely to watch next.
Netflix also uses big data for these rankings:
– Personalized Video Ranking
– Trending Now Ranker
– Continue Watching Ranker.
2. Posters that pop
By using AVA (Aesthetics Visuals Analysis) tools, Netflix scans an entire episode season, or film to identify the frames that can be used as promotional artwork.
3. Content development analytics
Netflix uses a projection model to predict the potential success of a project before investing in it. This model takes into account a variety of factors, including the popularity of the genre, the cast and crew involved, the budget, and the marketing plan.
The projection model has helped Netflix invest in some of its most successful shows, such as Stranger Things and Squid Game!
These are just a few ways in which Netflix uses data science. Data scientists at the organization are constantly discovering new opportunities to increase both revenue and watch-time. If you want to learn more about careers in data science, read our blog here.