Netflix’s affection with machine learning and its current dilemma
Machine learning has been at the heart of Netflix’s business strategy and catalyst for growth since its inception in 1997. From the earliest form of its business model as a physical DVD-by-mail rental service, Netflix has started analyzing vast amount of user rating and behavior data to identify the type of movies its customers enjoyed watching in the past and developing proprietary algorithm to predict what movies the same customers will likely enjoy watching in the future. Netflix then utilized the result to recommend each individual customer a personalized playlist of movies & TV shows . In addition, it leveraged the aggregate result to make an important business decision: determining which are the most popular contents it will need to buy from the DVD distribution companies and place in the rental library to anticipate future customers demand .
Riding the tailwind of internet proliferation phenomenon, Netflix began to convert its core business model into an online streaming service in 2007 . The transformation allowed Netflix to deliver movies and TV shows instantaneously, reach new customers all around the globe, and significantly boost the number of subscribers. This new business model is a boon for its movie recommendation engine – the vast amount of fresh data helps to refine the algorithm and improve the prediction accuracy .
The increased confidence on the ability to predict customer’s taste led Netflix to a new venture – acquiring and creating its own original contents (movies and TV shows) . While past decision on which DVD titles to be bought for its library is purely transactional, selecting and dropping casts have more risk and complex relationship effects. In one example, Netflix’s machine learning engine indicated that more users clicked on “Grace and Frankie” show when the image does not contain Jane Fonda. Netflix is struggling on whether to drop her from the marketing image at the risk of irritating the actress . As experts think that similar tensions will occur more often, it’s decision today may set an important precedent on how the company approach similar types of problem going forward .
Current solution to balance the machine learning insight
The issue was stirring up internal debate within Netflix’s production and technology team. Production team “don’t believe numbers as much as people in Silicon Valley”; while technology team argued the data should not be ignored .
To resolve this impasse, the management team took on two-steps action plan. First, it cultivated the culture of open communication across different teams in voicing out the benefits and concerns of each options. Secondly, it encouraged the teams to also consider the “intangible impacts” that are not yet captured in the machine learning’s algorithm, such as the potential risk of alienating the actress, inability to partner with the actress for any future production titles featuring her, and repercussion of violating the contract agreement .
Under this framework, the two teams were able to “reach common ground”. Human judgement is introduced into the decision-making process – Netflix decided to keep the promotional image that also featured Jane Fonda .
Additional step to consider
In high level, I agree with the management’s approach to incorporate human judgement to overcome machine learning’s limitation in considering crucial “intangible impacts” that are out of scope for the algorithm. Augmented intelligence should serve as an input in the decision-making process, helping to suggest rather than dictate the final decision.
However, I think Netflix still can generate deeper insight to drive its business decision by conducting more rigorous analysis to separate the signal from the noise . Currently the algorithm captures in one specific instance, there is negative correlation between Jane Fonda’s appearance in the image and the number of click. It does not tell convincingly whether it is truly because of Jane Fonda or other factors in the images such as color tone, background settings, etc. Having more result from other tests in a controlled environment will help to separate consistent relationship from the random correlation – it might even predict causality whether Jane Fonda is actually less desirable than her co-star, Lily Tomlin and drive the business decision in the future.
Netflix has been good at providing its customers new contents based on machine learning result of what they like in the past. However, in media and entertainment industry there is a saturation point where people are bored and weary watching the similar things again and again. What should Netflix do to avoid this situation from happening? Also, how should Netflix orient its business model to ensure it can capitalize on new trends – something that did not exist in the past hence not showing up in the machine learning result, but once introduced the customers will like in the future?
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