The Exchanges

Q Okay. And what are those, why would somebody pay, why would an employer pay on the low end and why would they pay on the high end? What are the things that drive them up Kind of the value chain.

A Definitely. So on the high end of things, you get a service called ripple scout, which is a pretty, pretty huge feature for employers. So basically what we go in and do is we type up a report using our system to kind of facilitate great matches on the students that really fit for an employer. And so an employer every two weeks, we'll get an email in their inbox with, you know, the five to 10 students that are the best possible fit for their company. And so It'll really bring these students to life. It'll go beyond GPA. Um, and so the students really get an advocate for them and then employers, you know, basically have us do all the work for them essentially. And then on the lighter end, you know, it's really for smaller companies. There's also less, uh, unlocks. Um, and so there's sort of more limited access to the database and it doesn't come with that powerful ripple scout feature. And then some of the diversity filters we have as well.

AI assessment note: “on the high end of things, you get a service called ripple scout”

Q Do they tell you that the cohort of believes you're giving them have a higher kind of job offer rate than other methods they're using?

A Yeah. So I think two of the most remarkable statistics we're seeing right now is 60% of the candidates that we connect with companies end up getting a first round interview. So if you think about like a human recruiter, they've been doing this. Their entire life, you know, Google alone employs a thousand internal sourcers, and we are matching at a more effective rate than the average human recruiter, and it's, you know, all tech-driven, so that's been really powerful, and I think a lot of why those early results have been good. I think the other sort of exciting statistic is, yeah, if you're hiring off of a job board, you know, you're often looking at a pretty, pretty abysmal acceptance rate in terms of actually going through the process, and for us, about one in every 28 candidates we match gets hired, which tends to be You know, really, really superior to the average job board. So that's been an exciting kind of, uh, I guess badge for us there.

AI assessment note: “about one in every 28 candidates we match gets hired”

Q That is great with no churn. It's hard for you to kind of get a rational LTV model in your head. Cause you could argue it's infinity currently. So instead of that, let me ask you differently. How are you adding new customers and what does that CAC look like?

A Yeah. So, uh, in terms of how we add new customers, it's twofold. Uh, we rely a lot on our current customers for referrals, but we also have an outbound sales model, um, you know, pretty classic structure, uh, with both the BDRs and account executives, um, you know, working there, kind of building out our early pods there and moving a little bit away from that sort of founder-like excitement of the, uh, of the early days. And, uh, yeah, it's, um, it's been pretty straightforward. So, I mean, our, our customer acquisition cost right now has only been About half of the annual value of the customers we're bringing on. Um, and so a lot of room to work there. And yeah, I guess LTV, you could argue is infinite, obviously is never infinite, but I really think that for us, you know, what we're doing is creating really, really deep partnerships with these early customers. And our goal will be to, you know, be working with them on, you know, implementing how these candidates, if they stick are doing five, 10 years from now and feeding all that data back into the system. So these are really long-term relationships.

AI assessment note: “our customer acquisition cost right now has only been About half of the annual value”

Q Wow. That's interesting. So no. So, so what if you guys both disagree on something? How do you get through it?

A You know, I think the beauty of so many of our decisions is that we, uh, that we do disagree with each other a lot. And I think, uh, this sort of big, uh, big expression we have at the company is strong opinions, loosely held. And so I think almost all our best decisions come from, you know, rigorous argument between Eric and myself. And the bottom line is both of us will often end up abandoning our original position and kind of let logic went out. But I think too often, It's easy to get invested in your original idea, having to be right. And I think what we really care about is the logical process and getting to the right conclusion rather than starting out in the right place.

AI assessment note: “both of us will often end up abandoning our original position and kind of let logic”

Q for your toothpaste and the, and the, and the student then picks a brand that ultimately they might use for life, like tide or things like that. So you're, you're getting these customers early, right? When they enter the job market, have you seen anyone do multiple jobs with you? They do one right out of school, then they leave and they come back and use you again or no.

A Completely. So we haven't opened the process up as much for that second job just since it's, you know, two years in, but the, the vision in the long run is to basically move candidates through every stage of that job process. And so what's amazing to see is we kind of look at our customer success data and see the experiences the candidates are getting on the platform. We have a really strong likelihood of being able to match those candidates in their next job. And because we have an effective experience when they're first looking, we sort of earn the right to Collect more and more data as they go through that process, which then makes our matches better and better as you think about that first career transition and kind of move farther out into a career.

AI assessment note: “we haven't opened the process up as much for that second job just since”

Q on all your software expenses. We all spend a lot on it. Visit nathanlacka.com forward slash Capterra. That's nathanlacka.com forward slash C-A-P-T-E-R-R-A to get started today. Totally free. Let's move away from the economics and, and, and talk more about, again, just like what you're trying to do in the world, right? So place students into jobs. What's that number today? How many students have you successfully placed into jobs?

A Yes. So the way that our process works, probably the single like biggest thing we measure, because if we integrate with an applicant tracking system, we can track full numbers of hires. But in terms of, uh, this sort of statistic where we actually handed off into a company's process, it's really that first round interview. And so we've been basically averaging about a A 1001st round interview connections a month this coming year. So working back into it, I mean, we're seeing probably between, I don't want to say like A hundred and a 150 candidates get jobs each month on the platform. So pretty substantial. And a lot of those candidates, you know, really sort of think top tier software engineering, really awesome, diverse candidates. So it tends to be a pretty powerful funnel for a lot of these companies.

AI assessment note: “we're seeing probably between... A hundred and a 150 candidates get jobs each month”

Q last year. Now that's not a knock on you because I know a lot of people brag about growth and they, and they communicate that in terms of our team has tripled, but really you've tripled expenses, right? So what's happened over the past year? Do you have some people churn and you're trying to restructure the team a little bit or, or what, what goes through your head there?

A Yeah. So I think in those early days, um, you know, the whole focus was how do you build out this marketplace? Right. And it was an incredibly kind of dynamic, uh, challenge to get the candidate acquisition side of that launched and really pinpointing and bringing on the top candidates on each campus. Um, I think what we've realized is there hasn't been, you know, a ton of change or our team's pretty similar, but as we started to branch into the employer side and we've just gotten more efficient and more effective, I think we're able to do more with less. And so, uh, yeah, we've, we've managed to kind of, Flip it pretty quickly. And I think, uh, I think in general, it's just been an efficient hiring model there.

AI assessment note: “our team's pretty similar, but as we started to branch into the employer side”

Q When did you realize that that was the model? I mean, was there a clear moment where you said, ah, that test just worked. We've got a triple down there.

A I think what we realized is that we wanted to align value as much as possible, and so if you have a placement fee, you know, attached to a candidate, which a lot of competitors in the space have traditionally, and recruiting agencies do, you're kind of incentivizing at the end of the cycle, oh, if I can find this candidate, you know, from a cheaper source, I won't do that, and so originally, it actually came from wanting to be as aligned with our candidates as possible, but what we realized is having that kind of push forward allowed recruiters that were loving it to just Use it over and over and over again. And I think early on in a startup, the focus has really been maximizing value rather than maximizing revenue. And so it was consistent with that, but then it also ended up clicking from a revenue perspective.

AI assessment note: “originally, it actually came from wanting to be as aligned with our candidates”

Q because at the current revenue, obviously level, right? You have to find investors that strategically get the vision because if they're just going to pay revenue multiple, it's going to be evaluation. I don't even know if they paid a two million dollar valuation. Uh, so, so how are you thinking about going out and building this syndicate? Like, do you, do you have strategics kind of already in mind?

A Definitely. Yeah. So, I mean, I, I think that I, you know, at this point, the, the whole focus has been on user engagement and student engagement. And I think that's really what we've Prioritize and targeted. Um, in terms of, you know, different meetings and different VCs, we've had quite a bit of inbound interest. So that's been sort of one source of kind of getting everything in place. And then we're also, you know, really strategically picking firms that have a really good reputation for working, uh, with companies who've sort of been in a similar stage. And I think for us, it's all about finding those right partners. I think ideally, um, ideally people who have experience, um, you know, really scaling, um, Smaller companies and who are willing, you know, not to just sort of follow into the round, but really, you know, take an active role as the lead investor. I think we really want to want to pick the right firm there.

AI assessment note: “we're also, you know, really strategically picking firms that have a really good reputation”

Q And he, when he was doing like due diligence, he had no issue with the 50, 50 split. Like, I mean, was there, are there any clauses where if you guys come to a complete standstill, there's a way to move the company forward? Like with a super much already or anything like that?

A Yeah, yeah. No, I think at an early stage, um, you know, you want both founders bought into a huge extent. And I think the way Eric and I work, you know, we really see it as both of our companies. And I think in a way, um, you know, a lot of what he bet on was the dynamic between us and us being able to figure it out together. And he's definitely, you know, a great resource if we're trying to talk through a decision we maybe disagree on or sort of want, you know, a really expert opinion on. But I think that by and large, he's had a lot of confidence in our ability to navigate it and figure it out.

AI assessment note: “a lot of what he bet on was the dynamic between us”

Q And how many, first off, define a connection. When an employer's paying you 300 bucks on average per month, what are they paying for? Are they paying for 10 new resumes per month that meets their criteria? What are they paying for?

A So, and yeah, again, it just kind of depends on the package. So they're, they're really paying for, um, you know, basically access to the database, the ability to get these ripple scout reports on the sort of, um, the sort of higher end packages, and then also just sort of different levels of filters. So a lot, one, one big focus for a lot of companies, particularly bigger companies is how do you increase diversity? And so we have sort of filters that allow companies to bring on students from sort of different backgrounds and really make sure, you know, they're doing everything they can to create an inclusive work Place that sort of come at some of the more premium levels as well. But Nathan, just to get back to your earlier question a little bit about, you know, would you sell out at ten million dollars? I think that, uh, I think that on some level, uh, when you, when you start something, you do it not just because of, you know, that sort of personal game, but because you want to build something that's going to be really incredible and really meaningful and it's going to fix a huge problem. And I think we're pretty rational. I don't think that we would be, uh, we would be doing this if we didn't think that we could You know, become the sort of B player in this market, but the college recruiting market's twenty five billion dollars. I think on the student side of things, we're …

AI assessment note: “they're really paying for, um, you know, basically access to the database”

Q Okay, and what will you definitely not give up more than in terms of equity in the company for two million bucks?

A So I think that's actually in some ways, you know, not, not, not totally how we think about it. I think, uh, from our perspective, what we want is more than the highest valuation possible. We want the, you know, the right partner and the right evaluation. I think a lot of companies make the mistake in series a round and sort of grabbing, you know, the biggest valuation they can possibly get away with. And I think that makes it tough in the following rounds. And so I think for us, you know, we'll sort of see, uh, obviously we'd love to give up as little of the company as possible, but it just really depends what the market's willing to pay.

AI assessment note: “that's actually in some ways, you know, not totally how we think about it.”