Netflix is an on-demand video subscription service offering unlimited viewing for a monthly fee in virtually every market in the world except China. The company currently has over 137 million accounts across these markets in which it competes with other online streaming services and linear TV networks, . As Netflix seeks to continue to grow across markets in this increasingly competitive entertainment space, it faces several challenges that almost necessarily require the use of machine learning to address. These challenges include, but are not limited to, increased consumer demand for personalized content, growing diversity of customer needs and preferences in content, and tightening competition on content licensing.
To satisfy an increasingly broad set of customer tastes in a cost-effective and personalized way for its diverse customer base, Netflix needs robust analysis of an incredible amount of data, which can only be conducted with artificial intelligence. Netflix’s management is currently implementing the use of machine learning in the areas of content acquisition and production, and customer experience, which includes content recommendations and streaming quality.
The company has seen that even its largest titles, which are viewed by tens of millions of people, account for a very small percentage of overall streaming hours so it understands that it is the combination of pieces of content that attract and retain customers. Since tastes are broad, even within a single market, Netflix offers a wide breadth of programing in order to maximize the size of its customer base . To satisfy this broad range of customer tastes in a cost-effective way, Netflix uses machine learning to determine expected hours of viewing for each piece of content, estimate the cost per hour viewed, and compare it with that of similar content deals . Additionally, the firm uses predictive models to understand customers, such that there is a large enough set of content that meets their preferences without necessitating the renewal of any specific title . This cost-effectiveness is particularly important as increased competition bids up licensing and renewal agreements.
To further reduce reliance on outside studios and ideally reduce content costs, as well as strengthen brand loyalty, Netflix has focused on building out its own original content . This content production is another area in which Netflix leverages machine learning . The company uses machine learning not only to identify potential projects its customers will like, but also to make cost-effective business and technical decisions. In the pre-production decision process, Netflix combines and analyzes various data sets to predict the cost of numerous attributes of the production process, such as content, location, and schedule, and optimizes decisions given resource constraints such as time, cast, and locations. To extract as much value as possible from these production investments, Netflix tries to make content accessible to as many viewers as possible. In order to do this, Netflix needs to prioritize markets given the previously mentioned constraints. Machine learning informs this strategy by predicting which languages a piece of content will me most popular in, in which locations, and amongst what groups .
In addition to having the right content to acquire and retain customers, Netflix needs to deliver the right content to the right customer. The firm uses machine learning to address these challenges. To direct customers towards the content they want to watch, Netflix relies on a recommendation engine that uses “implicit and explicit” data to identify trends. Implicit data is information that users share with Netflix, such as like/dislike feedback, and explicit data is behavioral data that includes which shows users watched and how fast and frequently they watched those shows. The engine recommends shows to different users for very distinct reasons based on identified tastes . In an effort to further personalize the user experience and maximize successful recommendations, Netflix uses machine learning to match the tastes of a customer to the form in which content is recommended to them. This makes it so that different customers are not only promoted the same content because of distinct reasons, but also promoted the same content in distinct ways. Netflix does this primarily by promoting, for the same content, different images that appeal to different preferences .
In addition to what Netflix is currently doing, the firm needs to ensure that it is searching for new data sources to refine its algorithms. Competitors like Amazon have data unrelated to video that can inform their recommendation models. Netflix should seek to partner with companies like Data Wallet to acquire this additional data. Netflix should also strive to seek causality with its algorithms and ensure they are not self-reinforcing to eliminate bias.
How can Netflix ensure its algorithms identify causality?
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