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Assertive Yield B.V.

2,615 followers

4mo

Curious about how Assertive Yield B.V. is revolutionizing premium #publishers monetization? In this fun and insightful interview, Nils Lind and Rob Beeler dive deep into the future of ad tech, exploring dynamic flooring, Traffic Shaping, and AI-driven optimizations. ▶️ Watch the full interview with Nils: https://lnkd.in/drCAr3rr The AY Prebid Server empowers #publishers with: ✅ AI Traffic Shaping: Maximize ad fill rates, boost CPMs, reduce SSP throttling, lower carbon emissions, and strengthen SSP relationships, driving up to a 10% revenue increase with real-time user profiling and efficient demand paths. ✅ AI Dynamic Flooring: Optimize first-price auctions across platforms with transparency and unmatched results. Safeguard your inventory from low-quality ads and inefficiencies, ensuring robust CPMs. ✅ Identity Solutions Integration: Achieve over 15% uplift on iOS CPMs and similar benefits on Chrome with our advanced cookieless solutions and Topics API. Embrace the future of cookie-free advertising. #PrebidServer #Programmatic #DigitalAdvertising #TrafficShaping #AdOps

Transcript

And that is when there we tapped into, OK, let's re tackle the flooring aspect of things. Now we end the first price auction, let's see if we can figure out if we can't make it work with that one as well. But it's also when we started to do like traffic shaping for publishers, like when traffic shaping on the publisher side was award we had never heard of. So, so we actually call it like bit request reduction system or bit request optimization system. As well as some other things like supply shaping and so on to optimize between direct and open auction demand and squeeze out more incremental revenue in the middle. So all of these things basically, we walked on and it really took over a year to get the flooring system to work reliably within the first price auction. There are so many nuances to it and especially when a publisher has direct campaigns running, it makes it really difficult to accurately masha and make sure it is driving uplift. Right. And and so right, I think that there's a there's a part of that right of just the complexity that has continued to grow is at the point now where. You need a I You need, again, I, we go back all the way to the early header bidding days. There's a little bit of that of going. It's probably best that that was done manually, if you will, because then you learn. Yeah. And then you learn and go. Like, that was a lot of effort to go from $1. 32 to $1.35. Maybe my time would be best spent elsewhere, right? And you work through all of those things, right, with your hands in it. And then you're able to sit there and say with an AI system here to apply that. Would I would I love to hear right is just taking that from analytics to action? And the fact is that all those analytics you couldn't at this point, I think think through all the identifiers to all the environments, to everything else that's and the supply path optimization, all those particular pieces. I think we're at a point now where I'm sure there are a couple of people could be mad at me for saying it that because they are probably doing this all manually, but it's a little bit of time to let the machines do their piece and drive that particular thing. Now go ahead, please. I don't know if you I think like the flooring, the very. The initially difficult part is related to identify how are we going to measure success, right. Of course, we can say, OK, like revenue goes up, I don't know, thousand visits for example. Or because we need to consider unfilled, right, we need to consider patriots that go completely empty. Of any kind of opportunities lost. But even that is not that easy oftentimes because you have revenue shifting from one pocket to another one going from that exchange to prevent going from prepaid to add exchange going to Amazon and you have all of these changes happening that makes that measurement difficult. But once there's a good base established to measure it, there has been also a point there. Now we can reliably, with manual optimizations, measure if it is working. And then the second step of that is applying automation to it automatically make those improvements. Now. We had already built a system that is predicting basically what partners will be.

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