SaaS Interviews with CEOs, Startups, Founders
Venture Studio Spinout Hits $250m Valuation for Clean Data Room SaaS
20 Sep 2022
Chapter 1: What is the main topic discussed in this episode?
So 50 customers today at a $25,000 ARPU. I mean, that puts you at about 1.2 in monthly revenue today, correct? Ish, yes. You are listening to Conversations with Nathan Latka, where I sit down and interview the top SaaS founders, like Eric Wan from Zoom. If you'd like to subscribe, go to getlatka.com.
We've published thousands of these interviews, and if you want to sort through them quickly by revenue or churn, CAC, valuation, or other metrics, the easiest way to do that is to go to getlatka.com and use our filtering tool. It's like a big Excel sheet for all of these podcast interviews. Check it out right now at getlatka.com. Hey folks, my guest today is Matt Kilmartin.
He's the CEO and co-founder of Habu, the global innovator in data clean room software. He's passionate about entrepreneurship and developing technology to help brands and their digital transformation. He's got 20 years of experience in his career working for innovative software and data companies such as Salesforce, Crux, and Akamai. Matt, you ready to take us to the top? Let's do it. All right.
So what does clean room software do? Yeah, so what it basically means is two companies that have data who have a common business interest to collaborate around each other's data, but they don't necessarily want to give each other their data, right? So meaning clean room software is neutral infrastructure where two parties can effectively do analytics across distributed data sets.
This is cross-beam space, right? Not really, I would say. Because that's the closest, like I try and pattern match. What's interesting is a lot of the cloud data warehouses are doing this now. So AWS, GCP, Microsoft are trying to do this. And that's where data sharing now happens at the cloud data layer. And then Snowflake and Databricks have capabilities around this as well. And so-
So maybe for my audience, give some use case. So the use case I have is you're potentially going to partner with a company. You want to share email lists. Crossbeam is like sort of how you do this anonymously without giving up data. What's a use case people use you for? So a use case for us is we work with a lot of, so I know Crossbeam because we use it. And a use case for us is Disney.
Disney is a client. Disney owns Hulu, ESPN, and they have all the TV viewership data, basically. L'Oreal is a big advertiser, and they want to know what are all the Disney and Hulu, what do you know about my customers, basically, right? So Disney doesn't want to give all of their data.
to l'oreal l'oreal doesn't want to give all their data to disney they use our software to do data collaboration so i see big companies like roku disney are examples of clients um and then also another big one is with retailers and manufacturers so like grocers and people in cpg companies interesting are those your two big use cases disney and hulu and grocers and cpg Those are, yeah.
And we have other verticals as well that we're successful in, but those are media entertainment, retail, CPG, advertising. Those are a lot of the use cases at which people are using our software for today. And are Disney and Hulu both paying you or is it one side of the marketplace? Today it's one side of the marketplace. Disney or Hulu? Well, who was part of Disney, actually?
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Chapter 2: What is clean room software and how does it work?
And how do you get someone paying 600K? How do you upsell? Consumption. So when someone buys our software, they get a certain number of data clean rooms or slots for collaboration. And as they use them and as they use more, they grow and there's an upsell opportunity. Okay. So let me try and give an example. Let's say H-E-B, the grocer, is paying for your software. They have X number of slots.
A slot might be filled by Procter & Gamble because they sell Dove shampoo and dial. Okay. That's how it all works. Yes. Yep. You got it. Your quick study. Okay. Interesting. Interesting. Interesting. Folks, as you know, time and place is everything, especially in marketing.
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As a thank you to their customers for helping them grow three times faster than the competition, LinkedIn's offering $100 credit on your next campaign. You can access it by going to linkedin.com slash SaaS interviews to claim your credit. That's linkedin.com forward slash SaaS interviews. Okay, so your upsell, that's a powerful upsell is number of slots.
Are there any other sort of very powerful upsells here, number of seats or no? We don't have a seat model right now. It's more based on as people are consuming more slots and collaboration. Okay, interesting. Then also... we're starting to look at some different modules as well, right? So sort of version one of the software was around, let's just stick with the L'Oreal example.
L'Oreal has their beauty customers, right? That's a data set. And then there's a data set with HEB or whoever else. We're starting to do now things more on machine learning, where they might have machine learning models built around skin types or propensity to buy other products. And how do you now start to do machine learning on other data sets? So that would be a more advanced module.
But out of the gate, it was sort of more data to data. And now it's more advanced modules are around ML and more model to data. And when was out of the gate? When did you launch? We've been out of it for three years, built technology for a year, launched commercially recently. Two weeks before the shutdown. So March of 2020 is when we launched commercially. And what was that like?
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Chapter 3: What are common use cases for data clean room software?
Beg, borrow, steal. Yeah, listen, it was a weird time, right? Because as you can see by our price points, it's kind of an enterprise sale and we're selling to big brands. There wasn't a lot of appetite for companies to enterprise buying at a time during COVID in the beginning. And so we had to get super flexible. And so we actually did some shorter term deals.
We call them first value deals where instead of letting someone sign up for a year, we'd have more flexible terms and start using our software. And we probably did, Well, actually, interestingly enough, of all those deals we did sort of six-month deals with to do sort of POCs or first value engagements, every single one of them has continued on as a customer. How many?
How many POCs have you done to date? Well, now we're done with the POCs. That was like two years ago. Okay. Like that was two years ago we first launched. Now we're doing proper. But take me back because there's so many of my listeners that are in that state, right? They're launching POCs to get going. How many did you have to launch and how many like converted to paid and what did you learn?
Oh yeah. Great question. So for us, because it's a little bit, because it's like enterprise, it was less around volume and quantity and more around quality. Right. Like one of my good buddies is the founding revenue leader at HubSpot. And obviously their model is very different. Right. And so, so yeah, so for us, it was all around like quality of fit and,
And then also really handshaking with those customers around mutual success plans, right? So that would be my advice is like, you know, you don't know your ICP. You're trying to figure it out. Place a few bets. Place a few different types of experiments. Identify what your learnings would want to be. Obviously, you want sort of the money, but really the money is like a short-term thing.
But it's good to get money. Matt, how many of those bets did you place? Are we talking like five Disney's or five grocery chains or 10 or 15? We placed probably... eight to 10 bets. Okay. Eight to 10. And you're, and, and then what does that sound like back then? Was it like, listen, here's, we're going to try and deliver over the next month or year or 15 days.
And at the end, it's going to cost you this. If you're happy, what was the timeline and what was the cost if they were happy? Uh, it was more, um, It was probably more of a 90 day, 90 to 120 day window of make happy value realization. And then we didn't break our pick, honestly, if it's going to cost this, right? We were really just focused on delivering great product, delivering value.
And we said, if we do that, it's going to work itself out. We'll have a proper business conversation. Oh, so you didn't try and set an anchor at the beginning of POC to say, if we work hard to make you happy, it will be 30 grand a month. No, it was, it was COVID. It was in the beginning of it. Right. You just negotiated at the end. Yeah.
So today, today, maybe we would be a little bit different, but yeah, I mean, it was, it was a different time. Right. And, and this is an in-person sale. It's an enterprise sale. And so all of a sudden we're trying to do the resume screen. So we were flexible early on and it's, and it served us and it served us well. Yep. How many today? How many customers? Oh boy. We are south of 50 still. Okay.
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Chapter 4: How does Habu's pricing model work?
I don't know. It was a good outcome. Not FU money, but it's comfortable money. Yeah. Listen, I'm still working. You're doing what you love though. Come on. Yeah. Yeah. I mean, this was not a small deal. This was not a small deal though. I mean, this was, I think what a $700 million deal if my memory's right. Yeah. Yeah. Yeah. Yes. Yeah. Yeah. Yeah. But listen, Salesforce is awesome.
I learned a ton there, but what I also learned was when there's rapid change and there's chaos in a market, you need innovation. And sometimes it's easier to innovate when you're really sort of small company starting with a clean slate. How long were you at Crocs? Almost five years. Oh, come on. Guys, he's underselling himself. You had some equity there. That was a very good exit for you.
Did you plow any of that money back into Abu or no? So you could say bootstrapped at the beginning or no? So Abu was actually launched inside of a venture studio. So the founders of Crocs started a venture studio. It's a fund called Superset. It's a $65 million fund and they start startups. They're not VCs. So they actually started 10 companies in the last three years. So how did you get involved?
Well, I was thinking about starting my own thing, but I'm not an engineer. And so I needed a technical co-founder. And these guys are my old bosses from the last company. And so to go work with them, I basically had instant access to world-class engineering talent and plenty of capital to get going. And so we raised Series A right as I came in.
We actually just closed our Series B. Snowflake invested in us, actually, as well. Oh, nice. We're up and rolling now. Quantify those for me. What was a series B? How much? 25 million. And that was a series B. When was a series A? Series A was November. Well, not February of 2020 is when we announced it. Feb 2020. Okay. And how much was that? End of 2019 is when we really closed it. Okay. Okay.
Okay. And how much was that for? 13. 13. And that's when you came in? Yes. Right then. Right when we closed that. Did the Series A investors, was that a contingency? They had to bring you in as CEO? You know, Superset actually leads the rounds as well. And so it's, I mean, it's people I've worked with forever, right?
So it's not, I mean, yes, we sort of mutually agree, but it was not necessarily contingent. Like they had been incubating and building some tech as well, trying to think about ways to solve this problem. Interesting. Okay, so got it. So you go into this.
So help me understand if someone else is listening right now going, I am just like Matt, I need to go find a venture studio where I can be the CEO of one of their companies and spin it out. I mean, how does that cap table shake out? I mean, do you look at Superset like a 50-50 sort of co-founder effectively? Are you way under 50% equity in the business?
I'd rather not get into the specifics of the cap table for Habu. There are other venture studio models out there. I think Superset's a little bit different than Y Combinator and some other folks like that because the... They play sort of very operational roles.
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Chapter 5: How did the guest adapt during the COVID-19 pandemic?
I mean, you're around there. We're on that track. Yes. Yeah. My point is you traded at somewhere around evaluation multiple of like 20X. I mean, I know others in your space, not your space directly, but with their same metrics that they're trading at like 30, 40X, right? So I'm just trying to get a sense of how you guys thought about dilution. Yeah.
Listen, I think we had a unique opportunity with our partnership with Snowflake and we partnered with Snowflake and Snowflake Ventures invested in WoW. And I think Snowflake's a great product and we built a bunch of our technology on it and it made... it made sense to, listen, I saw the power of distribution at a company like Salesforce.
And if you can do a partnership with Snowflake to help people sort of drive more compute with Snowflake, like we can get the potential benefit and partner with them from a distribution perspective. So that's why it made sense. That was one of the reasons. And also the market's hot. Yeah, no, I agree. That's a great answer. Last question.
Are you flying traditional model with your six quota carrying reps or you have them aiming at 5X, their OT in terms of quota? A hundred million dollar quota target. If they hit quota, they, they make something like, you know, 250 grand. It's funny.
I actually missed that meeting this morning, but we're actually going through those specifics, but yeah, probably quote will probably be a little bit, a little bit higher than that. And, um, you know, I, I've actually a fan of six month quotas at this stage of a company still a little bit, because I feel like, you know, we're still trying to figure it out.
Um, and, you know, listen, I, I love, listen, I love, um, you know, salespeople who make 300% no quota, blah, blah, blah. But it's like, yeah, well, guess what? Your CFO is going to change the goals next year, right? And so we want to make sure that we're appropriately compensating people, but we're also not setting the bar sort of too low.
Everyone's a shareholder, you know, and so... All 50 people own equity. Options pack comes with every offer. Yes. Oh, that's great. That's nice. So you do six month quota. So what, like 600 grand is the target for six months in terms of quota 1.2 for the year, something like that. Something like that. Yeah. Yeah. Interesting. Very cool. And how do you split it up amongst the reps?
Is it geography based or some other way? It's funny. It's a hot topic right now internally. Yeah. Because I think we're literally making these decisions today. I think we're probably going to move to some sort of a blend where it's actually more vertical, which I think at our stage is not that normal. But I think given the nature of what we do, that vertical expertise is really useful, right?
If someone can talk that HEB game and go talk it everywhere else, that's a good thing.
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