How YouTube Connected TV Is Transforming Television
As YouTube Connected TV grows, and arguably emerges as the world's predominant channel, is the model for TV monetization changing as a result? Evan Shapiro, CEO, ESHAP, discusses YouTube's impact on the CTV landscape with Eric Berger, CEO & Co-Founder, Common Sense Networks, in this clip from Streaming Media Connect 2023.
Shapiro says that YouTube has remained the world's biggest video platform and is now the largest television channel in both the US and globally. He asks Berger, “How do you see YouTube taking over connected television and changing the model of television itself, both in advertising and other forms?”
Berger says that due to regulations such as the Children's Online Privacy Protection Act (COPPA), advertising data for market segments such as children under the age of 13 cannot be tracked and that this age cutoff may increase to 16 with “COPPA 2.0,” which is closer to the age-based data tracking regulations already in place in Europe. “Data becomes a kind of currency in terms of being able to use [it contextually] and to align it as much as possible [with viewing habits],” he says.
Shapiro asks him to further elaborate on this.
“The problem with contextual today is people are using keywords, or [it is] based on some of the metadata, [which is] created by creators,” he says. “In the case of platforms like YouTube, the contextual match isn't that strong. And so the better you can be at finding the actual match between the content that's on the screen and the advertising, the better it is from the consumer experience and the higher the engagement is. A lot of people have proven this. We’ve proven it as well.”
He breaks down the differences between contextual and behavioral approaches for better optimizing CTV advertising. “Behavioral is based on past behavior. So how many times have you looked for a pair of shoes and then it follows you all over the internet, but you've already made the decision not to buy it or you bought it,” he says. “And contextual is much more in the moment. And so [with] the power of YouTube and the combination, from our perspective, of thinking about this being 16 and under, not just 13 and under from a contextual [perspective], it's really important to get the data piece and to scale as much as the sea of content as you can. And so we've taken three years of content data, from evaluating content, and we've been working with Deloitte on supersizing these data sets using machine learning so that we can effectively catalog the sea of content that is appropriate for kids and be able to slice and dice it through over 300 different types of metrics.”
“Without having to collect any data from the end user,” Shapiro says.
“Without any user data whatsoever,” Berger says. “And with an eye on something like YouTube's dominance and the importance of being able to transact on a platform like that.”
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