Three Ways Big Data and Machine Learning Reinvent Online Video Experience
With traditional TV viewing on the decline, we discuss several ways Big Data and Machine Learning can assist with online video, including redefining user recommendations, improving video buffering and leveraging MAM orchestration.
Let’s face it: traditional TV is fading. Viewing habits have totally changed, with spectators now favoring online video. In this competitive market where big players like Netflix and Hulu are racing for most eyeballs it might be rather difficult to encourage audiences to stay tuned to your video content.
According to NewVantage Venture Partners, big data and machine learning (ML) deliver true value to enterprises. The marriage of these techs allows getting advanced customer intelligence, automating mission-critical workflows, and, in turn, significantly improving viewer experience.
A number of online video providers have already started reaping these benefits. Let’s take a dive into some success stories.
Viewer interests and behavior tend to change rapidly, and it might be quite difficult to predict what content they will acquire and enjoy next. To address that, businesses need to leverage huge amounts of value-rich data for thorough analysis.
For example, Netflix virtually sits on data goldmines. The OTT giant uses lots of data sources to feed the machine learning algorithms of its recommendation engine.
Netflix has billions of member ratings and receives several million daily stream plays — with info about duration, time of day, and device type. The company also analyzes social data, movie metadata (actors, director, genre, parental rating, and reviews), films’ popularity, queue items, demographics, location, language, and much more.
To smartly manage this great availability of data, Netflix implements all sorts of ML approaches, including clustering algorithms, linear and logistic regression, Markov chains, and association rules.
In turn, this ML-based model allows the media provider to automatically deliver personalized offerings in terms of content, payment methods, and subscription types. According to Netflix, the results are impressive, with 75% of its viewership coming directly from its recommendation engine.
The corporation also puts viewing data to good use by personalizing movies’ artworks. What does that mean? Netflix’s ML algorithms analyze a user’s preferences, namely favorite genres and actors, to generate the most relevant imagery for a film.
For instance, if your viewing history says you like comedies, the artwork of the recommended content will likely include a comedian. And if you’re a romantic movie lover, be sure to mostly get recommendations featuring romantic scenes.
Netflix will also spot your cast preferences to feature the artwork with a beloved actor/actress.
Netflix says the steps they took in personalizing the artworks led to a meaningful improvement in how viewers find new content. The video provider also plans to expand this approach and personalize other types of artworks they use, such as synopses, metadata, and trailers.
Video providers are under constant pressure to deliver content to a vast number of viewers. While fulfilling a batch of media asset management (MAM) operations, they’re dealing with dispersed systems and technologies, communications, and work orders.
And MAM orchestration might be the optimal avenue to address this challenge. Empowered by ML, an MAM orchestrator can automate a wealth of operations — from media ingestion and transcoding to media processing and playout. Namely, such a solution can spare you the need to manually categorize video, i.e. spot adult content, violence, racism, objectionable language, etc. — to meet compliance regulations.
ML algorithms can also greatly enhance an orchestrator’s ability to flag content inappropriate for a particular region or country — for political and/or religious reasons or for being too controversial.
