Netflix’s combats its main threat, subscriber churn, by creating a personalized experience that customers are willing to pay for. This strategy has allowed the company to become the largest streaming company in the world with 137 million subscribers throughout 190 countries providing them with a library of 14,835 worldwide (5,579 in the US) original and licensed titles. But how can Netflix provide a personalized streaming experience to its millions of subscribers with its vast library? The answer lies on the introduction of machine learning to the company’s innovation and improvement processes.
Machine learning has been instrumental at creating a competitive edge for Netflix on two main avenues: (1) content suggestion and (2) content acquisition and creation.
(1) Content Suggestion
Two years ago, Netflix started experimenting with sophisticated supervised (e.g. classification and regression) and unsupervised (e.g. clustering and compression) machine learning algorithms that aimed at aggregating large sets of data and identifying patterns in consumer behavior to improve user experience while decreasing monthly churn rate (lowest in the streaming industry at 9%/year in 2016).
Netflix has estimated that users spend 60 to 90 seconds browsing on its interface for new shows to watch before they lose interest. These machine learning algorithms help users navigate through Netflix’s vast library, translating into 80% of watched content coming from algorithmic recommendations and annual savings of well over US$1 billion from decreasing churn rates.
Historically, Netflix relied on customer reviews (ranking from 1 to 5 stars) to predict consumer preferences. After the introduction of machine learning algorithms on their streaming content, Netflix started collecting and analyzing a wide arrange of data on many metrics considered to be better predictors of subscriber behavior including:
- How many users watched an episode and an entire series
- Episode and series rating
- In-title behavior (pause, rewind or fast-forward)
- Churn rate per episode or series
- User browsing and scrolling habits
Netflix is investing heavily on providing customers with the largest and most relevant content library in the market. Therefore, it is instrumental to provide subscribers with an effective medium to facilitate the navigation and selection process of this library which is considered by Netflix to be the “moment of truth”.
(2) Content acquisition and creation
In 2013, the company started producing original content which supports the company’s long term strategy of relying less on outside studios. The data they gather using machine learning has been widely used internally to acquire and create relevant content that users will find exciting. As traditional studios shift towards their own online streaming models (e.g. Disney+), content supply will likely see a dramatic decline, which drives Netflix’s need to strengthen its internal creation efforts and acquire the right material to provide its subscribers with a relevant and exciting library.
In the short-term, Netflix is expected to increase investing in original content (US$ 8 billion in 2018) and continue developing machine learning to guide subscribers to relevant content. In the medium term, Netflix is experimenting with a groundbreaking interactive “Choose-Your-Own Ending” format to provide customers with a tailor-made title experience while learning new insights from their users.
Machine Learning Meets Open Innovation: Choose-Your-Own Ending
In an unprecedented move, Netflix is going to provide its users the ability to control the narrative of the show “Black Mirror” and let them pick the ending for some of the episodes in its 2019 season. Even though content creation costs will increase with this format, the potential benefits of outsourcing innovation to subscribers and getting insights from their behavior when they are most active can create value for the company outside the “Black Mirror” universe.
What Else Can Netflix Do?
Streaming content is largely a solo activity (60% of Netflix activity) but there is still a significant number of hours in Netflix that occur in more social settings. Deciding which title(s) to watch can be a very daunting task for a subscriber to decide alone and can get even harder with company. In order to maximize user engagement and satisfaction, Netflix must find ways to aggregate data from different users and develop models that could predict preferences of a viewer when that person is watching content alone vs. with friends, significant others and family.
- As Amazon is increasingly collecting more data on its users beyond their watching time (e.g. marketplace, grocery shopping), how can Netflix improve its machine learning capabilities or where else can the company gather more data to provide a better customer experience than competitors?
- For international audiences, their viewership data is biased towards local content or foreign content that has been translated into local language. Can Netflix develop a model that would allow them to predict if some foreign content (if translated) could be successful in a new market leading to significant savings in local content creation costs?
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