Data and Analytics: Streaming Platforms
Both Netflix and Hulu deploy advanced algorithms powered by big data and analytics to provide user recommendation in par with user needs. The two streaming platforms have invested in advanced data and predictive analytics solutions to ensure they deliver accurate and personalized recommendations to users. Below are case studies of how both platforms use data and analytics to guide business decisions.
1. NETFLIX USE OF DATA SCIENCE: CASE STUDY
- Netflix wanted to improve movie recommendations to its audience by matching related movies to those they have watched and suggesting them to the audience. To achieve that goal, Netflix invested in data science to help push interesting and relevant film or TV show recommendations to the audience.
- After introducing an online streaming platform, Netflix invested in many algorithms to deliver a seamless movie experience to its audience. An example is the recommendation system to provide suggestions to the users by understanding their needs and providing suggestions on various films and TV shows.
- Netflix’s recommendation system inputs user information, which could be in the form of past content consumed, product ratings, user-actions, etc., and processes it to predict user preferences for products and movie ratings. Such as recommendation system also deploys many machine learning algorithms.
- For cold start cases where Netflix lacks user past data information, it uses the recommendation system to search for similarity between different films or TV shows and provides recommendations of similar films or TV shows based on the movies being watched on the platform.
- Netflix recommendation system is either content-based or collaborative filtering based. For the former, Netflix similar suggestions to what a user watches; however, for the collaborative-filtering recommendation system, Netflix provides recommendations based on user profiles that are similar.
- The key data points taken into consideration for these recommendation systems include background information of its products and customer information. Other important data sets considered to push recommendations include the previously watched genres. The imagery below shows how the two systems work.
Netflix’s Algorithmic Recommendation System
- Despite having the two recommendation systems, Netflix uses both of them via a hybrid recommendation system to suggest content to its users. Likewise, beyond the recommendation algorithms, Netflix also deploys "ranking algorithms" to provide ranked lists of movies and TV shows that appeal the most to its audience.
- With so many ranking algorithms in the market, Netflix implemented the "interleaving technique that allowed it to identify the best algorithms" that can deliver the best page ranking algorithm for improved personalized user recommendations. Moreover, to better predict contexts, Netflix uses representation learning, which leverages various user behaviors like "time and periods of watching, e.g., day, week, season, and even longer periods like Olympics, FIFA, and elections."
- Overall, the Netflix case study shows how it invested in data science to build algorithms that can make recommendations on movies or TV shows similar to those previously watched by its users, the many algorithms it uses to personalize user content, and the types of data sets and user actions it inputs for processing.
2. HULU USE OF DATA SCIENCE: CASE STUDY
- Hulu, just like Netflix uses many algorithms to power its recommendations feature for its audience. In 2017, the company started using Amazon Web Services (AWS) to support the addition of over 50 live channels for its Live TV offering.
- The media streaming giant opted for Amazon’s AWS to run its live TV services because AWS is reliable and secure enough to allow Hulu to deliver an unrivaled viewer experience, even when viewership or traffic spikes.
- Hulu is using different data science solutions to improve its platforms capability, functionality, and user experience. Among the technologies it deploys is the Video Genome Project it acquired in 2016 to improve recommendations for Live TV and VOD.
- Hulu uses its advanced data capabilities and algorithms to personalize each user’s Hulu home screen and recommendations. Hulu aims to help its audience discover new shows based on a wealth of data that ensures the recommendations and personalization are accurate.
- The company relies on a combination of audience feedback, its advanced algorithms, and human curation to create the recommendations it personalizes for each user.
- The streaming platform includes a feature for users with options to like or dislike shows for submitting direct feedback to Hulu about the audience’s interests, to help the platform refine user results for improved viewership experiences.
- The Hulu platform has tied almost everything to data and analytics.
- The streaming platform deploys an "enhanced recommendation engine that tracks what users watch and when." The data and user action feedback acquired is then leveraged to develop a stronger and more personalized experiences.
- On Hulu, data drives everything regarding user experience, "from what a customer is recommended to the order the collections are displayed on the home screen."
- Hulu’s VGP “dynamically aggregates metadata around video content.” The VGP has over 8 million records and can tag video component data automatically to define “genres on a granular level from basic sci-fi into alien or zombie.”
- The huge amount of data available on this platform helps the company to dynamically categorize video content into many genres. Hulu's VGP algorithms focus on everything, including less important ratings.
- Some notable data sets put into consideration include user actions, such as what film or TV show a user watched, searched for, rated, browsed, scrolled past, etc., combined with “time of day, device and geo-location, and larger audience trends.”
- Overall, research findings indicate that Hulu deploys data science to help it refine user viewership experiences, provide relevant recommendations to its audience, collect real-time feedback on the platform, etc., with the sole purpose of delivering a remarkable user recommendation and streaming experience.