Netflix (Nasdaq: NFLX) uses advanced algorithms to analyze a ton of customer data it uses to make recommendations on what people should want to see--and, interestingly, what they might want to see after that.
The online movie purveyor's data-collection process appears to be working--about 75 percent of users select movies that Netflix has recommended, a company executive said last week at the Hadoop Summit in San Jose, Calif.
Netflix Senior Data Scientist Mohammad Sabah detailed how his company goes about finding the information at the Hadoop Summit and, most importantly, said that Netflix has probably only scratched the surface of what it needs to know to guide its customers, GigaOm reported.
The data-collection process starts by tapping massive numbers of customer movements: more than 25 million users and about 30 million plays per day, including when consumers rewind, fast forward and pause movies and other content; more than 2 billion hours of streaming video accessed in the last three months; 4 million ratings and 3 million searches a day.
On top of that, the algorithms dig into geo-location data, device information, when the viewers are watching, metadata from third parties like Nielsen (NYSE: NLSN) and social media data from Facebook (Nasdaq: FB) and Twitter to further hone down what's being watched and what others might like to watch.
Even with all of that, there's more that can, and will, be done, Sabah said, because the process has yet to be perfected. While Netflix now collects Jpegs and the exact time that credits start rolling, it would like to look at things like volume, colors and scenery to provide more hints of what viewers like in the programs they watch.
The end game is to show Netflix customers "content they'll view to completion and then recommend the next thing they'll view to completion" while taking into account the movies are only available for streaming for certain periods of time so those recommendations have to be "relevant."
For more:
- see the GigaOm story
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