How Big Data Will Shape the Hypercompetitive Online Video Space

In the competitive online video race for eyeballs, big data is a big deal.

Both Netflix and Hulu report 75 percent of of their audience viewing is based on recommendations. These players are constantly tweaking their algorithms, casting the net further to gain valuable insights to encourage audiences to stay tuned.

Just last month, Hulu acquired Video Genome Project, one of the largest video content databases. Hulu will use Genome’s data to advance its personalization capabilities ahead of its expansion into live over-the-top (OTT) TV early next year.

As online video giants team with data scientists and analytics platforms, they are harnessing an array of powerful data to understand general trends and personal differences.

This could be gathered from a user’s device, to their most recent tweets. The competition is fierce and with evolving artificial intelligence (AI) technologies, they’re rapidly uncovering new insights. So, where is the industry headed — and what can big data do for the smaller players?

Netflix engineers are focused on fine-tuning the service’s recommendation algorithms. The original proprietary system, which was called CineMatch used basic defined categories, individual customer ratings and saved lists along with combined data from all subscribers.

The company hires freelance video experts to hand-tag video metadata using human judgement. As one analyst explained, “It covers everything from big picture stuff like storyline, scene and tone, to details of whether there is a lot of smoking in the movie.”

The OTT video giant also employs computer vision, or more specifically, “extracting image metadata”, for example in deciding images and text placement when creating recommendations.

Using similar techniques, Hulu’s Video Genome Project (VGP) dynamically aggregates metadata around video content. VGP has over 8 million records, and automatically tags video component data, separating genres on a granular level from basic sci-fi into alien or zombie.

Layering the catalogue with huge amount of data enables OTT video providers like Netflix and Hulu to dynamically categorize these. And as ratings become less important, “user actions” — what a user played, searched for, rated, browsed and scrolled past, combined with time of day, device and geo-location and larger audience trends — tell algorithms which of these categories are successful, and what to recommend to a viewer when they next log on.

Netflix recently discovered a series binge is typically followed by a period of series abstention. According to exclusive USA Today reports, 59 percent of viewers take a break for around three days before launching into a new series.

Netflix studies also reveal 61 percent of viewers went on to select a film. After “Narcos,” a crime web television series, a popular option was “Pulp Fiction,” switching Colombian cartels for fast-talking Los Angeles mobsters. Netflix also found viewers switching pace, turning over from “Stranger Things” and “American Horror Story” for some light relief with “Zootopia” or “Mean Girls.

Posted on 7wData.be.

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