While the glory days of reaching 75% of television households with just one commercial are long gone, automatic content recognition (ACR) stands to help advertisers calculate incremental reach. \u201cThat can certainly be valuable, says Jonathan Bokor, SVP, Precision Video, Publicis Media.<\/p>\n

Bokor discusses the challenges posed by using disparate datasets depending on TV\/video tactics in this interview with Beet.TV.<\/p>\n

Where ACR can come into play is determining specific household reach\u2014something not available with Nielsen\u2019s panel-based data. If a marketer runs a general market campaign on broadcast and cable and also wants to use addressable or connected TV, it would like to know people if the latter have already been reached with the general market campaign.<\/p>\n

\u201cIf we could distinguish between what is reach and what is frequency, that would be very powerful,\u201d Bokor says. \u201cIt would allow us to be more integrated in the way that we reach those audiences and reduce the amount of frequency that we have and be able to re-aggregate reach.<\/p>\n

At this point, the ACR market \u201cis a fragmented one,\u201d Bokor adds. He cites as players in the space Inscape, Samba TV and Samsung.<\/p>\n

An up-and-comer in the ACR category is Alphonso<\/a>, which was founded in 2013. Having initially focused on apps, the company has broadened its focus to gathering viewing data from companies like TiVo and directly from TVs and streaming devices through deals with manufacturers, as The New York Times reports<\/a>.<\/p>\n

\u201cIt would be really great if we had integrated, census-level ACR data across the whole range of households, but we\u2019re not quite there yet,\u201d says Bokor.<\/p>\n

Right now, addressable TV campaigns have as their basis names and addresses that are matched to operators\u2019 subscriber lists. \u201cIn connected TV, we\u2019re typically using a combination of IP address and device ID. Each different tactic carries a different data consideration,\u201d Bokor says. \u201cBut what we really want to get to is common data across all of our marketing. We don\u2019t really want to have to stitch together all these different datasets that use different identifiers.\u201d<\/p>\n

In addressable TV, it\u2019s \u201cchallenging\u201d to use a digital target that we might be employed for online video, which is based on cookies or device ID\u2019s, and convert that into an addressable TV target based on name and address, according to Bokor. \u201cWe want to get to the point where we can interchangeably use a single target definition or audience definition across multiple tactics. We\u2019re not there yet, but that\u2019s where we really want to be.\u201d<\/p>\n

This video is part of a series The New Marketplace for Television Advertising, presented by dataxu. Please find more videos from the series here<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

While the glory days of reaching 75% of television households with just one commercial are long gone, automatic content recognition (ACR) stands to help advertisers calculate incremental reach. \u201cThat can certainly be valuable, says Jonathan Bokor, SVP, Precision Video, Publicis Media. Bokor discusses the challenges posed by using disparate datasets depending on TV\/video tactics in […]<\/p>\n","protected":false},"author":17,"featured_media":50421,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"mc4wp_mailchimp_campaign":[]},"categories":[7088],"tags":[],"_links":{"self":[{"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/posts\/50419"}],"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\/17"}],"replies":[{"embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/comments?post=50419"}],"version-history":[{"count":0,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/posts\/50419\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/media\/50421"}],"wp:attachment":[{"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/media?parent=50419"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/categories?post=50419"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/tags?post=50419"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}