Unlocking Revenue: How Data Overlays Are Transforming Content Monetization in Live Streaming

OK. So this is less of a situation potentially, but just more of a broad stroke with some specifics, right? Yeah, that's fine. Yeah. So, one of the things that um I have been leading is obviously the OTT CTV uh space within our company, focusing on live streaming. And what we are finding out through focus groups that I've set up and or kind of partner councils that we try to make interactive and bring some of our top uh accounts together as well as some prospects, is that data is really becoming a key. Component to how these players want to start monetizing their content, because with what's upcoming, um, with, you know, cookies going away, some of the IP addresses getting truncated, some of those identifiers for devices and or people, um, what we need to start doing is getting a little bit more data overlay for understanding the content and the audience behind it. So, one of the things that we are looking at currently are integrations with some of these partners and how to help them. So, to give you a quick example is for example, if we are currently monetizing specific content at about $20 right? If we can overlay data. And figure out how to charge it to the advertiser. We can then make $2 to $5 extra, which brings in more revenue for the publishers or the publishers' accounts, right? But also makes that that that supports the thesis that their inventory is actually more valuable because there is more data available to understand the profile of the content as well as the audience. So I think that's been opening up um a lot of doors internally to really change the or shift the thinking of how we're looking at data. Let me, let me pause.

Yeah, so the data overlay goes to them because they have their own, uh, direct sales teams, right? And they're trying to reach out to advertisers and strike up deals with, uh, you know, Honda, Pepsi, Kellogg, etc. But there are some advertisers that they don't know that are buying their inventory, right? So we're trying to overlay data and give it back to them to show them who is the full profile of the advertisers that are looking at it, but also representing the content better to the advertisers themselves so they can pay a higher dollar for it, to pay a premium for it. So it kind of goes both ways.

because we're, we're operating a marketplace, right? And, and our job is to represent the inventory at the highest value to the advertiser, but also show to the extent that the publisher or the account has direct sales to enhance their direct sales with some of the data that we can provide back to them in terms of insights and reports.

Why is, why, yeah, so why it is most interesting is because over the last couple of years, one thing that we've seen is everybody just trying to plug into as many partners that monetize their platforms as possible, right? And that's not really a sustainable business model, because if you think about it, if you have, let's just for simplicity's sake, two partners. Both of them are most likely going to be reaching out to Honda and Pepsi on your behalf, right? So that doesn't really differentiate it. It doesn't bring you the the density of demand that you're looking for. So, really the differentiator here is the data that you have on top of what everybody else has, right? So, like, some something that we have at Yahoo is first party data, consented data from Mail, as well as receipts that nobody else has. So, to really leverage that is something that we, uh, we've been talking to our partners about and kind of uncovering some of those nuggets. But listen, currently, what we're doing is trying to plug in as many partners into an auction is not really working. Let's really figure out what those differentiators are, and really the power of data and the value of data is where the conversations have been going.

It's really understanding what data they would like to see, right? Because there are, there's so many different partnerships that our teams are involved in, that they could bring in for, you know, measurement and attribution, and, uh, you know, understanding the audiences better, and mail receipts, and, you know, viewership patterns, right? How the audience kind of engages. It's really try to understand the value of the verticals of the data, and then try to figure out like how to squeeze more, more lemon juice out of that. That makes sense.

so to understand the verticals, right? Is, is the measurement component more, more important, right? Is the attribution component more important? Is the audience, uh, level at the auction, uh, more important? Is it the insights that we would provide kind of post-measurement to the publisher that's more important, and then the whole gamut of like how to run. The specific pieces of data and and what priority, right? Because there's just so much data, right? Experian data, TransUnion data, Hill receipts data, and all of them are used for different purposes. So it's, it's really trying to narrow down like what is the most important data partner, right? And how do we leverage that to get the best value for the customer.