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 5 · Cm 4 4.85
Q All right, Segment. Tell us what it does, and what's your revenue model? How do you guys make money?
A So, uh, so first what we do is it's a pretty simple business. Um, what we do is we take all the transaction data from institutions that we partner with. So, um, it can be, you know, product data, transaction based data, We take all that in anonymously. Um, that data, we then, uh, we have a process in which we cleanse, identify, and categorize that data, and then we have applications that help, you know, activate the data. So if we're going to go apply that to maybe messaging that goes into a marketing channel or something around understanding competitors with different reports, we have different Billing and pricing models based on different products that we have. Uh, the most traditional will with what most of our customers do is they pay us on a per customer per month basis. So basically account holder. Um, and that's for our kind of end-to-end platform. Uh, we also have, uh, models in which they're signing up for an annual report, uh, which they get full reports a year, or, uh, there's another product in which we just cleanse data they're paying on, like, a per, per transaction basis.
AI assessment note: “most of our customers do is they pay us on a per customer per month”
Answered produced feed
D 5 · C 4 · P 4 · Cm 4 4.30
Q you gave someone that's a great partner, a 300 connected accounts, big value for you. What would you be willing to spend to acquire those folks? And maybe don't give it in a hard number, but maybe give it in terms of what you're, what you're willing to wait in terms of payback period. So you'll spend the first month ACB to acquire them or two years of ACB or.
A Yeah, I think, I think, you know, from, from what we spend today, I would tell you we're probably spending Three to six months, depending on, uh, and I would say legal is a big part of that. You know, these are large institutions that a lot of this will go out, uh, from our outside counsel. And so these are large agreements, you know, typically when you're talking about compliance and, uh, so it, you know, that, that's a lot of our acquisition costs other than time. And so, uh, the, the, the people time is very important. Building out that business case for them is, is very important. So it's probably, you know, three to four months in terms of, Um, the return on our, our direct time. And then outside of that, it's probably another three months just on legal expenses.
AI assessment note: “we're probably spending Three to six months, depending on, uh, and I would say”
Redirected produced feed
D 3 · C 4 · P 4 · Cm 3 3.55
Q Interesting. Okay. Uh, very good. Um, talk to me if you can kind of about, about size generally. I mean, are you comfortable sharing general revenue size today?
A Uh, yeah, we are a privately held company. We do not show revenue actually kind of growth. Um, sure. You know, we, we, uh, You know, year over year, we're typically seeing, you know, 60, 50 to 60% growth. This year we'll be at a, you know, a 50% growth in terms of both onboarding from a, what we call a unique customer count. So that's what we look, that's the measurement we look at internally. But then when we, you know, same thing, we're, uh, from a transaction perspective, you know, the number of total transactions we're seeing on our platform, you know, we're growing at about 40% year over year. And from an ARR, right? That's what everybody cares about. We're, we're, we're doubling about that every year. So we're looking at a double from an ARR perspective.
AI assessment note: “we are a privately held company. We do not show revenue”
Answered produced feed
D 4 · C 3 · P 3 · Cm 2 3.15
Q Okay. And what's the breakdown of that 24 between kind of sales, marketing, engineering?
A Uh, sales and I'd call it client services. So both inside and outside, inside and outside sales probably makes about, uh, eight of them. Uh, rest, the rest is either, you know, we have a CFO, something like that, but most of the rest is, uh, both, Uh, engineers, uh, uh, I'll call data or ETL data sciences, and we, we're very big in terms of library science. Well, you know, the big thing today, you know, you talk a lot about what's happening in data science is you're taking big data sets that really don't make a whole lot of sense, right, and you're trying to make sense of what that data is by using AI. What we're doing on the library side is we're categorizing all that data, so we're seeing, you know, billions of transactions a year, and so we, we, we see those transactions and we're categorizing them both, um, by the technology that we built, but It always fails down to if we don't understand what that exact transaction means, because this is what the value we bring to the, to our clients, our customers, is that we categorize that based on real people, real people looking at it. And so we have what's called these variations of transactions. So if you ever look at your bank statement and you're saying, oh, I know this is Target, or I know this is Starbucks, but I, maybe I don't remember what this one was, right? That description of that merchant, uh, That interaction where you …
AI assessment note: “inside and outside sales probably makes about, uh, eight of them. Uh, rest”
