Amid the ongoing march of VOD, TV isn’t without a fight. In fact, some of the smarter broadcasters are now using advanced software technology to wring far greater efficacy from their ad inventory than ever before.<\/p>\n

Case in point – NBCUniversal, which just switched on a new tool that uses machine learning to better indicate to ad buyers the best moments in which to buy ads in commercial breaks between TV shows.<\/p>\n

“It’s always been common sense that having an ad run after a contextually-relevant scene does more for it,” explains NBCUniversal EVP, head of marketing and advertising creative Josh Feldman in this video interview with Beet.TV.<\/p>\n

So NBCU has created a proprietary new tool, which it’s calling the\u00a0Contextual Intelligence Platform, that helps ad buyers identify those moments. The tool sets machine learning to work on three data sources:<\/p>\n

\n
• Closed-captioning data<\/li>\n
• Video description<\/li>\n
• Scripts of shows<\/li>\n<\/ul>\n

  “We put it into the machine, and then we know our episodic program schedule for the entire quarter,” Feldman says. “So, if an advertiser comes to us looking for a very specific sentiment, we can place their ad in the A-pod position running right after that.<\/p>\n

  As well as mining its own data sources, the NBCU system also ingests the script and close captions of the advertiser’s creative, as well as target keywords and sentiments. The platform also runs sentiment analysis to ensure that the placement doesn’t just match the right themes, it also matches the right vibe.<\/p>\n

  For example, Feldman says, if a brand wants an ad to run in a commercial break following a wedding scene, the platform can find just the right moment.<\/p>\n

  In a way, the tool helps TV broadcasters sell ads the way they always have – with the power of contextual placement – but, through its data crunching, seemingly with more power than ever before.<\/p>\n

  The platform is in beta with up to five advertisers and a full launch planned early in 2019.\u00a0Says Adweek<\/a>: “Focus groups for ads placed with the platform thus far have shown an average bump of 19 percent in brand memorability, 13 percent in likability and 64 percent in message memorability.”<\/p>\n

  Feldman adds: “I think there’s going to be quite a demand for this product once everybody fully understands the full capabilities of it.”<\/p>\n

  And he thinks the tool will benefit viewers as well as advertisers. “If you’re watching a certain type of show, instead of just ads haphazardly running as they do across all of television right now … we can program breaks to make that entire show more of a single flow for a viewer, which is a better viewing experience overall.”<\/p>\n

  This video is part the Beet.TV preview series \u2018The Road to CES 2019.\u201d The series is presented by\u00a0dataxu<\/a>.\u00a0 For more videos,\u00a0please visit this page<\/a>.\u00a0<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

  Amid the ongoing march of VOD, TV isn’t without a fight. In fact, some of the smarter broadcasters are now using advanced software technology to wring far greater efficacy from their ad inventory than ever before. Case in point – NBCUniversal, which just switched on a new tool that uses machine learning to better indicate […]<\/p>\n","protected":false},"author":15,"featured_media":58104,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"mc4wp_mailchimp_campaign":[]},"categories":[7740],"tags":[5395,7760],"_links":{"self":[{"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/posts\/58099"}],"collection":[{"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/comments?post=58099"}],"version-history":[{"count":0,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/posts\/58099\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/media\/58104"}],"wp:attachment":[{"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/media?parent=58099"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/categories?post=58099"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/tags?post=58099"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}