How Customers.ai Surpassed $2M ARR After a Near-Death Pivot and Series A Raise (Interview with CEO Larry Kim)

Introduction and Company Overview

Larry Kim

00:00Guys, Kim launched Mellow 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 100,000 cash in the bank and said, man, do I invest money from my last $200,000,000 exit of this thing? Or do I let the market reprice it? What he decided to do instead was go use non dilutive capital from Founderpath raised a couple $100

00:20from us and then eventually said he got out of this to this series A and is now over 2,000,000 in ARR for customers.ai, which he has 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 then go market to those users. He's grown fast now

Nathan Latka

00:42several customers over $100 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 GetLat for 200,000,000 in 2018. He's a guest lecturer at Harvard, MIT and Boston University. Larry, you ready to take us to the top?

Larry Kim

01:05>> You got it, Nathan. Let's go.

Is Customers.ai a Rebrand of MobileMonkey?

Nathan Latka

01:07That's awesome. So first off, people might know you from sort of MobileMonkey in that world. To be clear, is Customers dot ai a rebrand? Is it the same company? What's the story there?

Larry Kim

01:15>> It's kind of doing business as Customers dot ai.

01:20>> 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 customer side AI.

Nathan Latka

01:32And I feel like everyone is just sort of, sort of sticking AI on their companies these days for Juice. So my question is, is there real happening here or is it just like a Juice up Excel file?

Real AI or Just a Buzzword? The LLM Differentiator

Larry Kim

01:40>> Yeah, it's the biggest joke. Like, you know, these AI companies are just like invoking chattypt API. I think we're significantly differentiated from that because we have our own LLM. Like we have, like we pull in all the scrape data 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 consumer data. So like hundreds of thousands of data points on individual

02:11>> US consumers, like marital status, children, age, income. And so using these large volumes of data sets, you can leverage these AI use cases.

Nathan Latka

02:24And so what data, 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 remote there is if you have some unique dataset used to feed your LLM model, right? The real question to ask here is what dataset do you have that nobody else has that you use to build your LLM model?

Larry Kim

02:41>> It's the consumer data.

Nathan Latka

02:43How'd you get that?

Proprietary Data: The MobileMonkey Install Base Advantage

Larry Kim

02:45>> Well, the first iteration of this business, Mobile 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 significant amount of data that's in there. Does that make sense?

Nathan Latka

03:16And so I guess, how do you use data captured from like the MobileMonkey install base to feed customers AI? What 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.

Larry Kim

03:31>> 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 his mouse installed, or that he has a certain screen resolution or he's using a certain IP address or has these plugins installed. And then

04:05We keep later growing, that's valuable. What are the other like digital footprints folks are using today's mouse type resolution, plugins installed, screen resolution.

04:11>> Languages like this. There's a 100, 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 Topeka, Kansas, but it's a guy who lives in New Jersey. Like that doesn't really make sense. Like, it's probably just like he's, you know, like a mistaken

04:41>> identity or something like this. It's kind of a layer cake with different levels of data collection and analysis and so on.

Nathan Latka

04:54Oh, what's going on there, YouTube? Good to see you guys. Now imagine this, you love watching these interviews with SaaS founders. But imagine if we took all of the valuation data out from over 2,807 interviews I've done manually Saves you a lot of time. Well, we've done this. We've built it into the beautiful interface inside of Founderpath. Check this out. I'll show you how you can access this in a second. But you log in, you connect

05:17your Stripe account, you see your valuation real time, you can see what changed over the past eighty eight days and even set goals for valuation this year. Now the secret evaluation is there's many different ways to value a SaaS business. So the reason you're gonna see three or four different valuations inside of your Founderpath dashboard, this is all free by the way, is because depending on who's doing the buying of your SaaS company, you're gonna get

How Website Visitor Identification Works

Nathan Latka

05:41a different valuation. A VC is gonna pay a different valuation, private equity firm is different. If you're gonna do a minority sale, that's different. And if you sell the whole business, that's a different valuation. You can see all those when I hover over here. Right? So the teal is what a VC would pay. Yellow is what private equity and red is if you sold the whole thing outright. Now what's cool about this is this is not

06:03built off random data. Again, you guys hear these interviews on YouTube. All these datas are built from real time valuation data points founder share with us on the show. So traction, 1,200,000 seed round, 3.7 raise. They sold 22% of their business. Go in here and filter by the event. Maybe you only wanna see companies that have sold the whole business. Well, here are a bunch that have been acquired the valuation and the multiple. Maybe you're going

06:29out right now and you're raising your seed round. Well, go in here and look at all this recent seed deals that went down, what they raised, what valuation they raised at, and what percent that they sold. There's never been a larger dataset of SaaS valuations than what you can get now inside of Founderpath. And we're thrilled to bring it to you. Alright. We're gonna go back to the YouTube video here in a second, but if you

06:51wanna check this tool out, if you wanna jump in and sign up, you can check it out for free to get your valuation at this link. This link, founderpath.com/products/valuations. Or if you go to founderpath.com and hover over products, click on get your valuation here, and go ahead and sign up to give it a whirl. Again, all that valuation data live right inside the platform. I hope to see you there. Alright, let's jump back into the interview.

07:18Give us the backstory here. The launch of Mobile Monkey was what year?

Larry Kim

07:22>> 2018.

Nathan Latka

07:24And the rebrand of customers AI was when?

Larry Kim

07:26>> This year, this spring.

The MobileMonkey Launch and Early Growth

Nathan Latka

07:28Okay, brand new. Okay, got it. I guess take me through sort of how you, you, it takes a lot of courage to basically say, hey, Mobile Monkey wasn't working. We need to sort of re pivot rebrand, etcetera. How did you make sure you had like capital available to get through the pivot and, are you open to sharing sort of what revenue flatlined at before you decided to pivot, that sort of stuff?

Larry Kim

07:48>> Sure. 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.

07:58>> This was like, so by 2019, we're a million dollar ARR company.

08:04>> Unfortunately, when you build in an ecosystem like a Facebook partner,

08:09>> you're not really master of your domain, if you will. You're kind of at You're the at the whim of the, some product manager at Facebook decides to kill some functionality and then

08:25>> I mean, it's a double edged sword. Like 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 leveraging that audience, that enormous Facebook audience. Downside is you're not in charge of that.

08:40>> So they made some really difficult

08:45>> kind of policy changes, which made it difficult for me to operate that business line. It got up to about 1,000,000 and kind of got stuck there for a while. Then the pandemic hit and we lost all our SMB customers. Like it was a kind of a challenging time.

The Pivot Decision and Cash Position

Nathan Latka

09:03How low did cash balance get the bank? Are you comfortable sharing?

Larry Kim

09:06>> 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 it's like, really do want to have like a neutral person putting that in to price these rounds. And so it got down to a couple $100,000 at one point. We But the story is that around the two year mark into this journey, we realized that this was not a

09:44>> great partnership with Facebook in terms of like all the changes that they were making and what our customers wanted to do. And we decided to kind of do the sales outreach automation kind of

10:02>> use case for B2C. So the same types of customers who are spending money on ads, 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 drag and drop you know, boxes and arrows kind of user interface

10:35>> for doing step one, step two, step three, like a sequence of automations, but instead of sending up messages on Facebook Messenger and Instagram Messenger, it's emailing. And we were pulling in the data through various different ways. And that's hard to do, unless you alluded to, it's usually the case that you swing at something you miss and

11:02>> the company goes down in flames. It's rare that you're able to, like these pivots, they almost never work.

Using Founderpath Non-Dilutive Financing to Bridge the Pivot

Nathan Latka

11:10Well, we'll talk about this though, because you, I mean, you decided at this moment, I think it was in 2020, 2021 to go look at options like non dilutive financing. Help me, help me. I mean, you're independently wealthy. Someone listening might be going, why was Larry looking for non dilutive options when he's already rich? Help me understand that thinking.

Larry Kim

11:27>> It's what I described to you before. There's like, how does it get priced is the question. Like, do I want to make this really, really punitive and like, you know,

11:42>> do like a down round or do I want to do this as convertible note? And there's just different ways to finance a business or do my existing investors, what would they price it in? So like there's all sorts of-

Nathan Latka

12:02How bad was it when you had flatline at 1,000,000 and you looked at equity term sheets? What valuation cut were you looking at?

Larry Kim

12:10>> I hadn't even tested the market. I was kind of concerned that this was a tweener, like not quite a failure, but not quite a series A type business. And I just needed more runaway. And so through a combination of myself putting in, sorry, sorry, with Founderpath for when I was non dilutive, initially it was about a 400 ks loan. We had over 1,000,000 in revenue, so that's,

12:46>> 400 ks is like less than 40% of a month's

12:53>> ARR. And then I put an equal amount of capital just 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 2,000,000 in revenue and getting a lot

13:23of

13:23>> interest in the business from investors and eventually doing our series A.

Nathan Latka

13:30So what did you do? Did you pay? Well, first off, obviously I want to learn here because I'm obviously running Founderpath, what did you and you can be directly and blatantly honest here too. What are some things you maybe disliked about the Founderpath model and what are things that you maybe liked about it?

Larry Kim

13:42>> Okay, so it's easier to talk about the things that I'm excited about. So

13:48>> the Founderpath 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. Like, I wish all VCs were like that. Like

14:06>> have to go on these, all sorts of meetings and flying everywhere and you don't know where things are going. What I brush for sure of that is to just have a very quick and dispassionate view and score of your business. And I think the first time that we did this, it was like, were able to get it done in like five business days or something like that, like from start to end. So that's amazing.

Nathan Latka

14:38I'm gonna 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?

Larry Kim

14:49>> I mean, the rate was high. Like I, it's like credit card rates kind of

14:57>> things, but now keep in mind, it's like unsecured debt. Like this is like,

15:05>> so I understand why it's that way. And in fact, interest rates are so high right now. Like, you know, it

15:14>> makes sense. And look, I think if you think about where we were able to get on that runway and like what the alternative would have been, I think that this is a no brainer that this generated an incredible outcome,

15:34>> probably tens of millions in enterprise valuation and 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.

Nathan Latka

15:49And 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?

Growing to $2M ARR and Closing the Series A

Larry Kim

15:56>> So we did pay off the loan. It didn't make sense to have like, you know, 5,000,000 in the bank making 4% in the bank or whatever. And meanwhile paying out like the interest on like, you know.

Nathan Latka

16:08How was

16:09that process? Was it easy to pay off early or?

Larry Kim

16:11>> That two

16:14>> emails to your collaborators and then just wiring.

Nathan Latka

16:18All right, enough about us, Back to you. So you come out of this thing, you add a million in ARR, was that And so you pivot to customers.ai and you add an extra million in ARR? And

Larry Kim

16:29>> then like adding customers, understanding the use case better and still using the old business to pay the bills and stuff like this, but really growing this sales outreach automation use case. And also AI, which is super transformative use cases and moving up market and eventually getting financing done.

Revenue Targets and Burn Efficiency

Nathan Latka

17:00Yeah, that makes tons of sense. So just to be clear, when did you break that 2,000,000 ARR mark? Was that this year or last year?

Larry Kim

17:06>> Last year.

Nathan Latka

17:07Last year. Amazing. And what are you targeting to end this year at?

Larry Kim

17:11>> We'll call it mid to high single digit millions in ARR.

Nathan Latka

17:15Okay,

Larry Kim

17:16so it's a call maybe like five to seven, something like that.

17:19>> Yeah, that range.

Nathan Latka

17:20Yeah, that's incredible. Are you burning through the Series A like fast? Or are you doing this in a pretty unit economical positive way?

Team Size and Engineering Headcount

Larry Kim

17:30>> You know, I think

17:35>> that there's, we did a calculation that there's over two years of runway. I

17:43>> think emphatically, yes. Like the

17:48>> climate is not like one where you can just spend like drunken sailors and assume that there's going to be someone willing to bail you out at the end of that. You need to think about your efficiency numbers and there's various numbers, but burnt ARR, it's below one last month.

Nathan Latka

18:14That's amazing. So just to be clear, when you do burn to ARR, you're taking the net burn from July multiplied by 12 and then dividing that into your total ARR and that's under one.

18:23>> Is it, do you do ARR to burn or burn to ARR? Which one do you do?

Larry Kim

18:26You know, people do both.

18:28>> Just as long

18:30as we understand the relationship.

18:31>> Sure, sure, sure, sure. Yeah, it's, we're burning less than a dollar for every net dollar of ARR getting added, which if you believe David's sacks or whatever that guy, says something that's, we never see that he says.

Nathan Latka

18:48Yeah, yeah, That's amazing. And I guess, can you flesh out the team for me? How many folks are full time today? 40 people.

18:55Wow. Okay. And how many engineers?

Larry Kim

18:57>> It's about a third of the team. So 15.

Nathan Latka

19:00Okay. And are you coding still?

Larry Kim

19:03>> No.

19:05>> 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.

Nathan Latka

19:14That's great. Did you, in terms of the series A, again, are an independently wealthy, but you did want the market to price the thing. Was that really why you did it? I mean, because you obviously wouldn't take the 15 or percent dilution if you didn't need to.

Larry Kim

19:32>> You know, a 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, there's warrants, there's,

19:52>> it's hard.

Nathan Latka

19:55You did that with like a traditional bank or something?

Larry Kim

19:57>> Yeah, like Square One Bank or something like that.

Nathan Latka

19:59Long did that take?

Larry Kim

20:00>> Three, two, three, how long did it take months?

20:02>> Yes, months, months. And then they want to meet the founder and they want to like, look at all your books and everything. Like, so it's just like slightly less painful than doing a bonafide like venture round. So like, you know, the reality is that like, none of this was even like

20:22>> occupying any mind space.

20:26>> I met you at that Growth Market Conference in Toronto, and I've been following you and I think I'm on your list. And so you emailed me some information about your product and I was like, Okay, what the heck? I'll just

20:42>> give this a try. And it was more like, I wouldn't say that I had like a thesis going into trying out your financing products, but rather I was convinced that this is such an easy no brainer to go after at this time. I don't have to have any conversations about

21:10>> how to finance business. Like this is a pretty simple strategy. And then, that's what we

Nathan Latka

21:18ended up. Before you came out of the valley, right? The trough, right after the pivot, Larry, if had raised an equity round while you were at in the valley, how dilutive do you think that probably would have been if you had to guess? How much of the company you think you would have given up?

Larry Kim

21:33>> Well,

21:34>> those are recaps, like what you're describing. And it would be the it's whatever the inside investors would be willing to pay for. Like the idea that

21:49>> you just can't run out of gas in between

21:54>> gas stations is basically

21:58>> the problem. Now it wouldn't it's kind of an unusual situation because like I said, I

22:06>> do have money. So, you know, probably I could have defended common by

Customer Count and ICP Focus

Larry Kim

22:20>> putting in, which I did. I have done that at every round of investment, you know, from, since

22:32>> inception of the company.

Nathan Latka

22:33That's awesome. Well, listen, we're certainly rooting for you. I guess before we wrap up again, people can check lariatacustomers.ai. Larry, how many customers are paying for the software today?

Larry Kim

22:43>> It's in the hundreds.

22:45Okay. We've got different products ranging from ASPs of like $5 $7 to tens of thousands of dollars.

22:54>> 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 sign up customers. Like you can't stop them from buying your thing. Like,

Nathan Latka

23:16>> No,

23:17that's a good, 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 the customer AI product, what's the largest customer paying you per year today? Do have anyone over $100 per year?

Largest Customers and Deal Sizes

Larry Kim

23:29>> There are yes, there are more than one.

Nathan Latka

23:32That'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?

Larry Kim

23:39>> Well, how about Zero to One?

Nathan Latka

23:43>> There you go.

23:44Number two, is there a CEO you're following or studying?

Larry Kim

23:49>> Well, would have been Brian Halligan of HubSpot who is a legendary in the space and is now the chairman of the board of HubSpot.

Famous Five Rapid Fire

Nathan Latka

23:59Number three, what's your favorite online tool for building customers AI besides your own?

Larry Kim

24:05>> Auto GPT.

Nathan Latka

24:06Number four, how

24:08many hours of sleep do get every night?

Larry Kim

24:10>> No, it's a difficult time right now. It's very little, like six, seven hours.

Nathan Latka

24:15Okay.

24:16>> And situation there, you married single kids?

Larry Kim

24:18>> I have two boys and they're kindergarten and grade three and I'm married.

Nathan Latka

24:24That's awesome. And how old are you?

Larry Kim

24:26>> I'm 44.

Nathan Latka

24:2844,

24:29we had to check.

24:30>> Last question, Larry, what's in English in you when you were 20?

Larry Kim

24:37>> I think it has to do with

24:42>> going after bigger opportunities. So,

Lessons from WordStream vs Customers.ai

Larry Kim

24:48>> you know, my last business was like an ad management tool. Why? Because I just happened to know a little bit about, you know, managing ads. But you know, in theory, you could have leveraged those skills and ability to create any kind of business, you know? So just, I think we're doing that with customers AI. So what we're talking about here is like, we're providing the leads and the tools, you see what I'm saying? So WordStream was

25:20>> like a kind of a management tool. They would spend $500 with us for the tool expense, but then they would spend 10,000 on leads through Google and Facebook. So our share of wallet was smaller, more modest. Here, we're providing the tools and also the lead data, which is, I think, a much more interesting value prop. I wish I knew that twenty years ago.

Nathan Latka

25:47Guys, Larry Kim launched Mellow 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 100,000 cash in the bank and said, Man, do I invest money from my last $200,000,000 exit of this thing? Or do let the market reprice it? What he decided to do instead was go use non dilutive capital from Founderpath raised a couple $100

26:07from us and then eventually said he got out of this to this series A and is now over 2,000,000 in ARR for customers.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 then go market to those users. He's growing fast now several

26:30customers over a $100 per year, having a lot of fun doing it. You can tell from the smile on his face.

26:34>> Larry, thanks for taking us to the top. Thanks,

Larry Kim

26:36>> Nathan.

Nathan Latka

26:37One more thing before you go. We have a brand new show every Thursday at 1PM Central. It's called Shark Tank for SaaS. We call it deal or bust. One founder comes on, three hungry buyers, they try and do a deal live and the founder shares back end dashboards, their expenses, their revenue, ARPU, CAC, LTV, you name it, they share it. And the buyers try and make a deal live. It is fun to watch every Thursday one

27:02p. M. Central. Additionally, remember these recorded Founder interviews go live. We release them here on YouTube every day at two p. M. Central. To make sure you don't miss any of that, make sure you click the subscribe button below here on YouTube, the big red button and then click the little bell notification to make sure you get notifications when we do go live. I wouldn't want you to miss breaking news in the SaaS world, whether it's

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27:45are saying. Sign up for that at nathanlatka.com/slack. In the meantime, I'm hanging out with you here on YouTube. I'll be in the comments for the next thirty minutes. Feel free to let me know what you thought about this episode and if you enjoyed it, click the thumbs up. We get a lot of haters that are mad at how aggressive I am on these shows, but I do it so that we can all learn. We have to

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