{"id":3879,"date":"2016-10-26T04:00:00","date_gmt":"2016-10-26T04:00:00","guid":{"rendered":"http:\/\/jwplayer.com\/press-releases\/jw-player-launches-innovative-video-recommendations-engine-increases-publishers-ad-revenues-up-to-50\/"},"modified":"2016-10-26T04:00:00","modified_gmt":"2016-10-26T04:00:00","slug":"jw-player-launches-innovative-video-recommendations-engine-increases-publishers-ad-revenues-up-to-50","status":"publish","type":"press-releases","link":"https:\/\/jwplayer.com\/press-releases\/jw-player-launches-innovative-video-recommendations-engine-increases-publishers-ad-revenues-up-to-50\/","title":{"rendered":"JW Player Launches Innovative Video Recommendations Engine, Increases Publishers Ad Revenues Up to 50%"},"content":{"rendered":"
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Publishers That \u201cFlick the Switch\u201d Turning on JW Player Recommendations on average see lift of between 20% to 50% in views & ad revenue within the first month of implementation<\/strong><\/p>\n

NEW YORK \u2013 October 26, 2016 \u2013 JW Player<\/a>, the world’s most popular video player and leading digital and mobile video solutions company, today launched JW Recommendations<\/a>, an algorithmic and data-driven engine empowering publishers to present relevant videos that keep viewers engaged longer, while generating up to 50% more views and ad revenue.<\/p>\n

JW Player’s powerful recommendations engine drives greater engagement and revenues by collecting more signals with high quality viewer interactions to help uncover relevant connections and make stronger recommendations. With minimal effort but huge impact, publishers can now easily leverage JW Player’s Recommendations engine by “flicking a switch.”<\/p>\n

“JW player has built an enormous network of independent publishers that includes more than 2 million websites with over 1.3 billion unique viewers each month,\u201d said JW Player Co-Founder and CEO Dave Otten. \u201cThis network uniquely positions JW Player to provide highly relevant video recommendations that leverages video consumption knowledge across our massive network. No other company has the footprint and scale to provide such an offering to network independent publications.”<\/p>\n

The engine indexes a publisher’s content library and selects the best videos for each viewer based on clusters of viewer activity, content traits and what’s trending in the publisher’s network and across more than 2 million websites. These data-driven recommendations update in real-time, so higher engagement is achieved. The benefit is not only that videos suggested to viewers are personalized and more engaging, but also that they require much less effort than a human-curated playlist.<\/p>\n

Recommendations also provides play lift across viewership:<\/p>\n