Every argument clarity score on this site is built from rows on this page. Each
question and answer was assessed with names hidden, the host's own answers included, on
four things from 1 to 5:
directness (does it answer the question asked), coherence (do the ideas follow),
precision (concrete details and clear references), compression (says a lot per word). The weighted
mix (30/30/25/15) is the exchange score. A person's published score averages their exchange
scores on raw tape only, at least 8 of them, shrunk toward the cohort mean.
Full method →
Answered produced feed
D 5 · C 5 · P 4 · Cm 4 4.60
Q A couple hundred. Okay, good. So it's very much, I mean, it sounds to me, I mean, uh, understandable you're being a bit vague in terms of the ranges, but it sounds like you're, you're very much kind of an enterprise sale. Are any, are any of these folks, the six year, like an inside sales model, or are you have a no touch kind of onboarding process?
A Um, again, depends on the size, right? So like no touch. Um, we don't, we don't really have a, a zero touch model. There's no like self-serve a hundred percent, you know, a hundred percent inbound without anybody, without any sales or account CS touching them before they're, they're onboarded. Um, But there's a range of interaction pre-sale. So someone might come on board, you know, sort of like lead to from a lead to an onboarding in two weeks, and it might take someone else, you know, six months, six months, a little long, maybe three months, um, to, to, to make sure that we are, um, You know, properly valuing their business and properly assessing what kind of setup is going to help them the most. And for big enterprise customers, there's a lot of work to do.
AI assessment note: “we don't really have a, a zero touch model”
Answered produced feed
D 5 · C 4 · P 4 · Cm 4 4.30
Q You know, sorry, Chris, I'm not accusing you of doing something unjust. What I'm saying is, why did you need a three-year cohort sample size, right, to understand it was never going to work, right? Why not, if that, if we go off your current logic, why not stay in, why didn't you try it for 10 years?
A Oh, cause it was, it was clear. It wasn't clear initially that it wasn't going to work. It took some time and then it took some time to execute a bio. Like I see, uh, working with, uh, private equity firms who have to, um, you know, we're working with the money side is not necessarily easy, you know? So it's like, you can kind of break the time up into, um, Uh, we continued to grow the company. It was going well. Um, then a period where it looked like, you know, it wasn't going to be what we thought it would be. And then a period of executing the buyout that, you know, those three things, um, took time and that's, that's how you get to three years.
AI assessment note: “those three things, um, took time and that's, that's how you get to three years.”
Answered produced feed
D 4 · C 5 · P 4 · Cm 4 4.30
Q Great. And is there one, do one of those make up more like the majority of their revenue? So does 80% SAS and 20% is rev share? Something like that?
A Yeah. I mean, ultimately, ultimately it models that like SAS because the way we make money is a function of the amount of advertising someone is doing. So that's, that's a pretty stable You know, it's a pretty stable sort of input. So we, you know, the input is how many impressions, how many ads are we, are we managing for you? And then the output is we're either charging a fixed amount per ad or we're charging a rev share amount per ad. And we're primarily, um, primarily rev share focused. That, um, but they, the two model really similarly, because at some point, you know, a CPM and a rev share are actually the same thing. They're, they're a, they're transacted on a per impression basis and they can actually back up the exact same way. So it's really two sides of the same coin.
AI assessment note: “we're primarily, um, primarily rev share focused.”
Answered produced feed
D 4 · C 4 · P 4 · Cm 3 3.85
Q In turn, you know, when you say model similar to a SAS company, you know, people like SAS cause they like predictability. Right. And so they look at things like churn to see is the bucket leaky or is it truly recurring revenue? So, so if I just asked you just to try and understand this in one metric, net revenue retention annually, are you guys above a hundred percent?
A Um, I mean, in aggregate, we, we try and look at it across a few cohorts. Right. So that it's like, it makes sense. Um, And I would say yes, cause there's, you know, normally in SAS you have a lot of upsell opportunity, right? And so that's, that's fairly typical. Um, with us, there is some variability cause some publishers grow really quickly. And as they grow, that kind of looks like, um, like over a hundred percent retention, but if a publisher shrinks, they don't. And so Modeling retention in our industry is like a little more complex because you do have that variability. You also have variability across quarters. So Q four is the huge advertising season. So revenue goes up. So you don't really have retention going up. So we can look at revenue retention. We can look at like, um, just like the number of publisher retention. We can look at what is that?
AI assessment note: “And I would say yes, cause there's, you know, normally in SAS”
