Marketecture: Get Smart. Fast.
00:11:03 · Ari Paparo, Alex Cone
[00:11:03] S0: And what are the most useful fields? Like, how many fields are there? What are the fields that we're here [00:11:07] S2: to talk So the the Core Web Vitals are in there, which there's, Largest Contentful Paint, which I realize is a mouthful. But that's really, like, designed to help people understand what a user perceives as fast. So the biggest thing on the screen, right, as it paints or like renders on the screen, we find that and time that effectively. So that's that's in time. We have interaction to next paint, which is this responsiveness thing. Like how quickly when you click something does the next thing happen? Again, real user data. And then there's this cumulative layout shift, which is about stability of the page and, like, how much has it moved. Those are the, I would say, the most popular things in there, but they're not the only things in there. There's also diagnostic metrics for each of those Core Web Vitals. So, like, things that help you better understand, like, why you might be slow. But, yeah, I think in in crux, like, Core Web Vitals have been the most popular, of the total number of metrics. [00:12:08] S0: Right. So the reason we're here in an advertising podcast is because, like, the... In addition to all the great things you could do with this data as a web developer, we... There seems to be a belief that the data is useful for ad targeting. Right? Because there are some variables here that are offered, like the ad count and the ad density. So walk us through the advertising use case.
00:12:08 · Ari Paparo, Alex Cone, Eric Franchi
[00:12:08] S0: Right. So the reason we're here in an advertising podcast is because, like, the... In addition to all the great things you could do with this data as a web developer, we... There seems to be a belief that the data is useful for ad targeting. Right? Because there are some variables here that are offered, like the ad count and the ad density. So walk us through the advertising use case. [00:12:31] S2: So you're drawing attention to the things that are new and the reason that I'm here. Though I will say, I I do think as a measure of page quality, Core Web Vitals can be a thing that that ecosystem could use. In fact, I know of a company that uses them. They're called Picnic. They're in The UK. Yeah. [00:12:48] S1: They're a portfolio company of ours. [00:12:50] S0: Oh, yeah. Look at that.
00:12:50 · Ari Paparo, Alex Cone
[00:12:50] S0: Oh, yeah. Look at that. [00:12:51] S2: I I really like Matthew. I also like his his wife that said, MIQ, Georgie. Anyway, ad tech sort of side note. But... So we're here to talk about the the ad metrics that we just added. So this is new. We're trying to measure ad experiences. We've been inspired by the ecosystems, like, seeming to care, which is a really good thing because what we're trying to do is reduce overwhelming ad experiences on the web. And we think that the best way to do that is to... For advertisers to see correlation between, like, what an ad load is like and then campaign outcomes. And if they do see a positive correlation to, like, less overwhelming sites, positive campaign outcomes, presumably, they'll bid more and, like, concentrate more demand there. And that gives publishers the ability to, like, shift into the types of experiences that I think they would like to have, but don't feel rewarded for today. But, like, this isn't our idea in the sense that, like, there's been other people doing this. We just... We happen to share the same incentive that we, like... Or the same outcome, which is, like, this could reduce overwhelmingness of ads, on the web, which is, like, a thing that we care a lot about. [00:14:07] S0: Right. So the obvious thing here is, like, too many ads equals ad clutter equals lower results. Right? So so I think we have some data. Right? So, there's been some testing. So tell us about this report that came out from our other friend of the pod, Chris Kane at Jounce.
00:14:07 · Ari Paparo, Alex Cone
[00:14:07] S0: Right. So the obvious thing here is, like, too many ads equals ad clutter equals lower results. Right? So so I think we have some data. Right? So, there's been some testing. So tell us about this report that came out from our other friend of the pod, Chris Kane at Jounce. [00:14:23] S2: Yeah. He... He's a five timer, isn't he? Does he does he have the jacket? [00:14:27] S0: You know, we were we were thinking about having him on here at this event, but like, at some point, we have to pay him. Right? You know, he's gonna get royalty checks for our giant advertising base, you know. [00:14:37] S2: Yeah. So Jounce, to my knowledge, is the first company to put out analysis that has, marketer outcomes overlaid. [00:14:49] S0: Right. Because that's matters ultimately. Right? You know.
00:14:49 · Ari Paparo, Alex Cone
[00:14:49] S0: Right. Because that's matters ultimately. Right? You know. [00:14:52] S2: I... You know, that... That's been our theory of success is if if it's not driving marketer outcomes, like, it... We're probably not gonna see incentives change. So, yes, I would say that that is that is mission critical. In fact, publishers told us as we talked to them in the lead up to this and got feedback, like, we like this idea, but, like, we'll believe it when we see it in terms of, like, getting rewarded for better experiences. So anyway, yeah, Jounce publishes, published a report on Monday that effectively says all four... There's four metrics. We haven't named them all yet. Happy to. But all four metrics that are measuring different dimensions of ad load have correlation with their, what they call, conversion score metric, which I believe, as I understand it, is effectively, like, what... Like, there's, like, a baseline of what is expected as a conversion from a site and then, like, how much you diverge either above that expected or below. So that's what he overlaid and he saw all four metrics are correlated but the that the primary metric is this average ad count in viewport. Like you can kinda pin the other metrics to this one. [00:16:04] S0: Yeah. They're all correlated with each other basically.
00:16:04 · Ari Paparo, Alex Cone
[00:16:04] S0: Yeah. They're all correlated with each other basically. [00:16:06] S2: They're all correlated with each other and like that is actually fairly intuitive if you think about it. So what average ad count and viewport measures is as a user's... Real users moving around like in a like scrolling around in a in a site, what is actually in view in terms of ads, as a count and it's to, like, two decimal point precision. So somewhere around, like, 1.2, 1.3 ads in view, it it any get, like, on average, you do start to see, like, where the performance starts to drop on various, like, and others have shared very similar data with us privately. Chris is the first one to share publicly. And that makes sense because right now you've got another ad vying for your attention as another ad comes into view. So I think that... That's one of those where I'm like, I think marketers and agencies, like, are likely to... Like, not only just because of Chris's data, but, like, because it makes this intuitive sense, like, try to see if that drives performance for them. [00:17:11] S0: Yeah. Yeah. So the interesting... Well, another interesting thing here is that the graphs that were shown and we were... This... I don't have the graphs to show on screen, but we'll put them in the notes. It's not really a linear relationship between ad density and count. It's more like a... It's a curve where it's pretty flat for a while, and then the... When you really get to excessive ad load that it... The performance drops off radically.