When you co-mingle television-viewing data from set-top cable boxes with data from smart TV\u2019s, you get closer to tracking fragmented consumption. But what complicates measurement of IP-delivered video content is an understanding of what IP-connected devices are actually associated with people in a given household.<\/p>\n

That\u2019s a person-level frontier that FreeWheel plans to tackle, according to Claudio Marcus, the GM of the company\u2019s Data Platform. In this interview with Beet.TV, Marcus recaps FreeWheel\u2019s progress on TV advertising planning and attribution, calling measurement \u201ca huge challenge.\u201d<\/p>\n

On the attribution side, FreeWheel<\/a> took on the role of TV advocate in 2016 when it conducted research \u201cjust to understand the impact that TV advertising has on different types of clients\u2019 business.\u201d There followed secondary research in which FreeWheel hired a firm to find any study that touted cross-channel or multi-touch attribution.<\/p>\n

\u201cThe more interesting part was actually the secondary research,\u201d Marcus notes. That\u2019s because out of some 164 studies, \u201conly twelve actually included TV. So here is TV, the biggest communication channel in the world and it basically wasn\u2019t getting its due credit when it came to cross-channel attribution.\u201d<\/p>\n

Since then, FreeWheel has provided to \u201cqualified attribution firms\u201d household-level ad exposure data, free of charge, \u201cso that they could include that in their cross-channel studies and their ability to measure television more effectively. That\u2019s been a big success.\u201d<\/p>\n

FreeWheel has also been working with the programmer members of the audience-targeting consortium OpenAP to understand de-duplicated reach across those members. \u201cBecause as things get more and more fragmented, advertisers want to be in those new places where there are audiences, whether it\u2019s different channels or different platforms. But they also want to make sure that they\u2019re not hitting the same people that they were hitting by going in traditional linear TV.\u201d<\/p>\n

Earlier this year, FreeWheel licensed VIZIO smart-TV data from Inscape<\/a> and built a nationally representative model \u201cwhich is really a co-mingled data set between the Comcast set-top box data and the smart-TV data.\u201d Among other things, the data helps programmers that sell on national basis. \u201cThey want something that\u2019s more nationally representative. They want to be able to forecast national impressions. They want to be able to measure on a national basis,\u201d Marcus says.<\/p>\n

There are disparities in data gathered from boxes and smart TV\u2019s. Within Comcast Cable\u2019s footprint, there\u2019s an average of about 2.6 cable boxes compared to about 1.1 devices in homes with smart TV\u2019s. \u201cWhat that means is that there\u2019s some viewing that you\u2019re missing.\u201d<\/p>\n

Meanwhile, smart TV\u2019s have their advantages over boxes. \u201cIt has lower latency, it covers OTT as long as it\u2019s content that has been previously fingerprinted on linear,\u201d Marcus says.<\/p>\n

As IP-delivered content proliferates, so do devices that receive the content. Problem is, not all of the devices that connect to WiFi \u201care associated with people in a specific home\u201d because some belong to visitors.<\/p>\n

\u201cAs we think about moving TV measurement from a household level to an individual level, it helps us to understand who\u2019s home and who\u2019s not. So that we can start to get better models when it comes to what\u2019s called viewer attribution.\u201d<\/p>\n

This video is part of the Beet.TV preview series titled \u201cThe Road to Cannes.\u201d The series is sponsored by 4INFO<\/a>. Please visit this page<\/a> for additional segments.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

When you co-mingle television-viewing data from set-top cable boxes with data from smart TV\u2019s, you get closer to tracking fragmented consumption. But what complicates measurement of IP-delivered video content is an understanding of what IP-connected devices are actually associated with people in a given household. That\u2019s a person-level frontier that FreeWheel plans to tackle, according […]<\/p>\n","protected":false},"author":17,"featured_media":60815,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"mc4wp_mailchimp_campaign":[]},"categories":[7977],"tags":[5267,7100,7696,7993],"_links":{"self":[{"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/posts\/60762"}],"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=60762"}],"version-history":[{"count":0,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/posts\/60762\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/media\/60815"}],"wp:attachment":[{"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/media?parent=60762"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/categories?post=60762"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dev.beet.tv\/wp-json\/wp\/v2\/tags?post=60762"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}