Netflix or How 1 Million Invested Saves Billions
Netflix & Spotify are two of the biggest names you come across when you talk about big data, business, innovation & art. Spotify handles music, Netflix movies. Going against powerful competitors, very well established in their respective markets, they managed to reach the top fast.
From Rentals on DVD to Creating a Marvel Universe Series
Netflix was a DVD/rentals company that peaked at 20 million subscribers in 2010. The rental service is still alive but slowly dying, Netflix reported 3.4 million subscribers to its DVD rental service in Q3 2017.
The clickbait title I have chosen refers to a story about Netflix’s challenge of 2006 that had a one million dollar prize. The competition was hosted to find the best algorithm that helps predict what a user would like watching based on previous movies that he enjoyed. The winner was decided in 2009, and the winning team’s algorithm was 10% better than the one that existed before.
However, Netflix did not use the algorithm, instead continued to research people’s watching behavior, tastes and sentiment analysis that makes Netflix the force that it is today, a force to reckon with, that entered the 100 billion $ club.
Creating Subscriber Online Personas Using Big Data
When it comes to your watching behavior, Netflix knows more about you than your partner does. Netflix records everything, it records when you stop a movie, it records when you pause a movie, it records when you skip an episode, and it predicts when you have fallen asleep.
Yes, by being an Internet company, Netflix has data collecting capabilities that many cable companies do not. Those companies however did not invest in such capabilities at all, maybe special receivers would have allowed them to collect information in time to lead them on a data-driven path. Of course, different business models make different decisions, but choosing a data-driven customer approach yields powerful growth.
The mere collection of this data is not enough. You need to employ data scientists, programmers, and build algorithms that work. After the start is done, the possibilities are endless. Netflix uses his prediction algorithms to choose new shows based on previous titles Netflix users enjoy.
The 100 million $ “House of Cards” was a result of Netflix understanding his customers and turned into one of the biggest hit series of the decade, boosting Netflix further. Netflix Originals keep viewer retention high. It seems that viewers that watch over 15 hours of content a month are less likely to cancel subscriptions, and this is how Netflix binge-watching reached high levels, as the platform offers the option to skip credits when watching series.
Most of Netflix’s decisions are data-driven. As a fun fact, Netflix complied a list of scary movies that were too scary for many people to finish. I don’t find them that scary at all.
Future Growth & Conclusion
For a company like Netflix, balancing content with viewer retention is difficult after reaching over 100 million subscribers. Netflix needs to balance the creation of new content with how many new customers join its service, and in time, as Netflix hits its top subscriber level, the only option to be able to create more and more content is to increase the subscriber fee.
While Amazon opened AWS, finding new points of growth, Netflix operates within a more limited market, where other new content-streaming platforms begin to rise.
However, Netflix’s prediction capabilities will remain an edge for now, and the new competitors do not have the same data and algorithms, and it may take them some time to be able to compete, just like Apple Music finds it hard to match Spotify’s recommendations.
Going back to our title, Netflix stated that their investment in prediction capabilities saved them over 1 billion a year in lost customers by employing churn prevention models on the data they possess.
That is quite a lot of money to save just by making use of your data.