SaaS Interviews with CEOs, Startups, Founders
His First Exit Was for $200m, His Next Startup Hit $1m Then Almost Failed, Now 100% YoY Growth at Customer.ai with unique LLM model
19 Sep 2023
Chapter 1: What was Larry Kim's journey before Customers.ai?
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check it out right now at getlatka.com guys larry king launched mobile monkey in 2018 grew it to a million revenue very quickly in under a year and then sort of flatlined for a year or two got down to a couple hundred thousand cash in the bank and said man do i invest money from my last 200 million dollar exit into this thing or do i let the market reprice it what he decided to do instead was go use non-dilutive capital from founder path raised a couple hundred grand from us and then eventually said he got out of this
did a series a and it's now over 2 million in arr for customers dot ai which he's uniquely built a language learning model powered by the early data he captured off mobile monkey folks which now enables folks to basically install customers.ai and understand when somebody hits their website what is their email even if they don't sign up so they go market to those users he's grown fast now several customers over 100 grand per year
Hey folks, my guest today is Larry Kim. He's the CEO of Customers.ai, the world's fastest growing B2C sales and data platform. He founded WordStream, a major AdWords and Facebook tool provider, which managed billion dollar ad spend and was acquired by Gannett for $200 million in 2018. He's a guest lecturer at Harvard, MIT, and Boston University. Larry, you ready to take us to the top?
You got it, Nathan. Let's go. That's awesome. So first off, people might know you from sort of MobileMonkey and that world.
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Chapter 2: How did Larry pivot from MobileMonkey to Customers.ai?
To be clear, is Customers.ai a rebrand? Is it the same company? What's the story there?
It's kind of doing business as customers AI. You know, as we got stronger product market fit and moved away from some social tools that we started doing and focusing more on sales outreach, it made more sense to rebrand the company as customers of AI.
And I feel like everyone is just sort of sticking AI on their companies these days for juice. So my question is, is there real AI happening here or is it just like a juiced up Excel file?
Yeah, it's the biggest joke. These AI companies are just invoking ChatGPT API. I think we're significantly differentiated from that because we have our own LLM. We pull in all the... the scrape data from, from the customer website so that we can speak their language, if you will. And then we also have our own like proprietary data set of like, you know, consumer data.
So like, hundreds of 1000s of data points on on individual US consumers, like, you know, marital status, children, age income, like, and so using these large volumes of data sets, you can
know leverage and uh these ai use cases and so what date i mean when i look at like proprietary llm models that's language learning models for those of you not familiar with ai larry i always like to understand i think the way you get advantage of real mode there is if you have some unique data set used to feed your llm model right so the real question to ask here is what data set do you have that nobody else has that used to build your llm model it's it's the consumer data so how'd you get that
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Chapter 3: What unique challenges did Larry face while raising capital?
Well, the first iteration of this business, Mole Monkey, had a considerable number of users using our free software. We weren't able to monetize that. successfully in terms of licensing fees, but it is installed on hundreds of thousands of pages and websites. And as an artifact of that, there's a significant amount of data that's in there. Does that make sense?
And so I guess, how do you use data captured from like the mobile monkey, you know, install base to feed customers AI, which you described to me of the email as Nathan, if someone visits your website, we can tell you what their email is based off just their IP address.
So there's... It's like 101 signals in the algorithm, correct? But at a high level, what we're doing, the way these things work is you have this publisher network and then you do kind of device and browser fingerprinting, which is like, oh, I see he has this mouse installed or that he has a certain screen resolution or he's using a certain IP address. Or has these plugins installed?
So, and then... Wait, keep going.
That's valuable. What are the other digital footprints folks are using these days? Mouse type resolution, plugins installed, screen resolution.
Languages. There's lots of little...
things and then you can layer on top of that a level of ai analysis to kind of like really infer whether or not this is a match or not like oh this guy is visiting like a women's clothing store in you know topeka kansas but it's a guy who lives in uh you know new jersey like that doesn't really make sense like it's probably just like he's you know like i'm like a mistaken identity or something like this
So it's kind of a layer cake with different levels of data collection and analysis and so on.
You gave us the backstory here. The launch of MobileMonkey was what year? 2018. And the rebranded customer's AI was when?
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Chapter 4: What differentiates Customers.ai's AI model from others?
And, you know, are you open to sharing sort of what revenue flatlined that before you decide to pivot that sort of stuff?
Sure. Um, so we went from zero to a million. in a very short period of time, like under a year, I thought we'd made it. This was like, so by 2019, we're a million dollar ARR company. You know, unfortunately when you build in an ecosystem like a Facebook partner, you know, you're not really a master of your domain, if you will. You're kind of at the whim of the,
you know, some, some product manager at Facebook decides to, you know, kill some functionality. And then, you know, I mean, it's a double edged sword.
Like the, the neat thing is that you can build these products that go from zero to a million in no time at all, because you're, you're leveraging that audience, that enormous Facebook audience that the downside is you're, you're not in charge of that. So they made some really difficult decisions.
uh kind of policy changes which made it difficult for me to operate that business line um it got up to about a million and and um kind of got stuck there for a while and then the pandemic hit and we lost all our smb customers like it was a kind of a challenge of time um how low did cash balance get in the bank are you comfortable sharing uh so hundreds of thousands now keep in mind like
I'm independently wealthy, so I can put in as much money into this as I want to. The challenge is you really do want to have a neutral person putting that in to price these rounds. And so it got down to a couple hundred thousand dollars at one point. But the story is that around...
at the two-year mark into this journey we realized that this was not a great partnership with facebook in terms of like all the changes that they were making uh and and what our customers wanted to do um and we we decided to kind of uh do this sales outreach automation uh kind of uh use case for B2C.
So the same types of customers who are spending money on ads, you know, would they be interested in this new offering of IDing website visitors and providing that email and contact information to the website owners and doing sales outreach to them? I mean, the technology is kind of similar to the automation that you would put into a chatbot. So it's still our same, you know, drag and drop,
you know, uh, boxes and arrows kind of user interface for, for doing step one, step two, step three, like a sequence of, of, of automations. But instead of sending out messages on Facebook messenger and Instagram and Instagram messenger, it's, it's just emailing. Uh, and, and, um, we were pulling in the data through various, various different ways. And, um,
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Chapter 5: How does Customers.ai utilize data from previous ventures?
with founder path for, uh, when I was on the lid of initially it was about a 400 K loan. You know, we had over a million in revenue. So that's, you know, 400 K is like, you know, less than 40% of, of, uh, a month's, um, or of, of, of ARR. Um, and then I, I put an equal amount of, of, of capital in, uh, just to, um, to gross that up a little bit.
And that provided almost a full year of time to fully develop and show progress against this new ICP and new use case. And growing the business to over 2 million in revenue and getting a lot of interest in the business from investors and eventually doing our series A.
So what did you do? Did you pay? Well, first off, obviously, I want to learn here, because I'm obviously running founder path. But what did you and you can be directly and blatantly honest here to what are some things you may be disliked about the founder path model? And what are things that you may be liked about it?
Okay, so it's easier to talk about the things that I'm excited about. So the founder path model, it's like you just connect your Stripe or your Recurly, your Bank of America, your QuickBooks. Oh, my God, they give you a score. I wish all VCs were like that. You have to go on these all sorts of meetings and flying everywhere and you don't know where things are going.
What a brush for sure that is to just have a very quick and, um, dispassionate, you know, view and score of, of your business. And I think the first time that we did this, it was like, uh, you, you were able to get it done in like five business days or something like that, like from, from start to end. So that's, that's amazing.
Um, I'm going to force you to say something you didn't like if you had to pick the thing that you liked the least, maybe you didn't hate it, but the thing you liked the least about the process or the model or whatever the terms, what would it be?
Um, The rate was high. It's like credit card rates kind of things. But keep in mind, it's like unsecured debt. So I understand why it's that way. And in fact, interest rates are so high right now. It makes sense. And look, I think if you think about...
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Chapter 6: What strategies did Larry implement to grow revenue?
where we were able to get on that runway and what the alternative would have been, I think that this is a no-brainer, that this generated an incredible outcome, probably tens of millions in enterprise valuation. And, you know, significantly a better outcome than the call it tens of thousands of dollars of interest that I paid over a period of like under a year.
And so what did you do when you, I actually don't know this off the top of my head. When you raised the series, did you pay us off earlier or do we still have capital out with you?
So we did pay off the loan. It didn't make sense to have like, you know, 5 million in the bank making, making 4% in the bank or whatever. And, you know, paying out like the interest on like, you know,
How was that process? Was it easy to pay off early?
That was two emails to your collaborators and then just wiring.
All right. Enough about us. Back to you. So you come out of this thing. You add a million in ARR. So you pivot to customers.ai and you add an extra million in ARR?
Mm-hmm.
And then, and like, you know, adding customers, understanding the use case better and, and you know, still, still using the old business to, you know, pay the bills and stuff like this, but really growing this, this sales outreach automation use case and, and also AI, which, which is a super transformative use cases and, and moving up market and,
and eventually uh getting it getting financing done yeah that makes tons of sense so just to be clear uh that when did you break that two million arr mark was that this year or last year last year last year amazing and what are you targeting to end this year at
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Chapter 7: What lessons did Larry learn about funding and financing?
the climate is not like one where you can just spend like drug concealers and assume that there's going to be someone willing to, to bail you out at the end of that. You, you need to think about your, your efficiency numbers and you know,
various numbers but yeah burnt ar um you know it's below one last month so that's amazing so just to be clear when you do burn to ar you're taking the net burn from july multiplied by 12 and then dividing that into your total ar and that's under one
is it do you do arr to burn or burn the air which one do you do you know people do both it's a it's just as long as you understand the relationship sure sure yeah it's uh uh we're burning less than a dollar for every net dollar of arr being added which uh you know if you believe David Sacks or whatever that guy. We never see that, he says.
That's amazing. I guess, can you flesh out the team for me? How many folks are full-time today? 40 people. Wow. How many engineers?
It's about a third of the team, so 15.
Are you coding still?
No. You start, of course, you write the software for your prototype and your version one. But you have to do other things as a CEO.
That's great. In terms of a Series A, again, you are an independently wealthy, but you did want the market to price the thing. Was that really why you did it? I mean, because obviously you wouldn't take the 15 or whatever it was, dilution, percent dilution, if you didn't need to.
Part of this was also... that it was so easy, right? So like venture debt wasn't even on my radar, okay? Because like I've done that before and it's, you know, there's warrants, there's, you know, it's hard.
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Chapter 8: What are the future goals for Customers.ai and its growth?
uh i wouldn't say that i had like a thesis going into uh trying out your your uh financing products uh but rather i was convinced that this is such an easy no-brainer to go after at this time um i don't have to have any conversations about um you know how to finance a business, like this is a pretty simple strategy. And then, so that's what we ended up.
Before you came out of the valley, right, the trough, right, after the pivot layer, if you had raised an equity round while you were in the valley, how deluded do you think that probably would have been if you had to guess? How much of the company do you think you would have given up?
Well, traditionally, those are recaps, like what you're describing. And, you know, it would be the, it's whatever the inside investors would be willing to pay for. Like the idea that you just can't run out of gas in between, you know, gas stations is basically the problem. Now, it wouldn't,
it's it's kind of an unusual situation because like like i said i i do have i i i do have money so um you know probably i could have defended the the the common um you know by you know uh by you know putting in which i did and and i i have done that at every round of, of, of investment, uh, you know, from, from, um, uh, since, since, since, since inception of the company.
That's awesome. Well, listen, we're certainly rooting for you. I guess before we wrap up, uh, again, people can check Larry out at customers.ai. Larry, how many customers are paying for the software today?
It's in the hundreds. Well, we've got different products ranging from ASPs of like $7 to tens of thousands of dollars. But for our ICP customers that we're really focused on, it's in the mid-hundreds. And then we have these like over a thousand of these other kind of self-signup customers like you can't. you can't stop them from buying your thing.
So now that's a good, that's, that's not a bad thing to have going for you. What is the, don't obviously name the customer, but if you just look at a customer AI product, what's the largest customer paying you per year today? Do you have anyone over a hundred grand per year?
Uh, uh, there are, yes, there are more than one.
That's awesome. That's awesome. Well, we're rooting for you, man. Thanks for coming on. Let's wrap up here with the famous five. Number one, your favorite book. Uh, well, uh, How about zero to one? There you go. Number two, is there a CEO you're following or studying?
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