S4E4 – Jonathan Chin – On target for 8 figures with DaaS

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Key Timecodes

• (00:58) – Show intro and background history
• (01:54) – Deeper into his background history and business model
• (03:33) – How long did it take to get his first client
• (05:08) – Deeper into his business model
• (11:54) – Understanding the numbers of his company
• (17:10) – Deeper into his business  strategies
• (22:49) – Deeper into the company revenue
• (28:48) – Understanding the data as a service model
• (31:15) – How many years to break the 1 Million point
• (36:08) – How his company charges the clients
• (38:19) – A key takeaway from the guest
• (48:58) – What is the worst advice he ever received
• (50:19) – What is the best advice he ever received
• (53:38) – Guest contacts

Transcription

[00:00:00.000] – intro

Hey, this is Sean Tepper, the host of Payback Time, an approachable and transparent podcast in building businesses, increasing wealth, and achieving financial freedom. I’d like to bring on guests to hear authentic stories while giving you actionable takeaways you can use today. Let’s go. Are you looking to create a SaaS business? This is a great episode that talks about an enterprise SaaS specifically in the data analytics space. We talk about how long it took, how many years it took to get to a million ARR. We also talk about what revenues are on track to generate this year, which is about 16 million. They have about 30 employees. The sales cycles are quite interesting. We actually talk about how do you, if you’re somebody who’s looking to build a SaaS, how do you onboard enterprise clients and how do you structure the contracts? That can be a little tricky if you never worked in this space, but we break it down in this episode. All right, let’s get into it. Please welcome Jonathan Chin.

[00:00:58.790] – Sean

Jonathan, welcome to the show.

[00:00:59.890] – Jonathan

Hey, Sean. Great to be here and thanks for having me on.

[00:01:03.040] – Sean

Yeah, thanks for joining me. So why don’t you kick us off and tell us about your background?

[00:01:07.270] – Jonathan

Yeah. So my name is Jonathan Chin. I’m the co-founder here, a data as a service company called Factius. And my current role right now is I run the growth and data strategy here. Although being a co-founder, I’ve worn many hats through our history, from everything to product to sales to taking out the trash. So I’ve done everything from a Spectrum perspective as far as technology company and building one up. Let’s see. I went to school at UC Santa Barbara down in California. I’m originally a Californiaan. I live now in Portland, Oregon. I studied business, economics and accounting, believe it or not. So yeah, that’s where I started my career at a CPA firm and as a tax accountant before getting here.

[00:01:54.830] – Sean

Got you. Okay, so let’s talk about the transition point. This seems to be a hard point in people’s careers as, hey, I’m working a full-time job, corporate America, let’s say, and I want to create a business and make that jump. So were you bootstrapping this new business a little bit or did you raise funds to go all in? How did that work?

[00:02:14.980] – Jonathan

It was definitely bootstrapped. I did have an unconventional, I’d say, path into entrepreneurship. And I’d say very lucky and fortunate. My co-founder, a guy called Dan Afrasiabi, he’s on our board now. He was the founding CEO and he was an experienced entrepreneur that we met through Mutual Friends and took me under his wing as the number two. And when we say bootstrap, we were definitely using some of his personal funds to really get the company started before. So I had a really fortunate opportunity to learn entrepreneurship from someone that was experienced. But transitioning from bootstrap to actually raising money became a pretty fast learning process and experience.

[00:03:02.400] – Sean

Sure. How long did you bootstrap the company before jumping in?

[00:03:05.950] – Jonathan

So I’d say we took our first angel money before actually raising a real round, probably a year, year and a half after. We were solidifying a vision and an idea before we went out to Friendlies. And we did eventually convince a handful of angel investors to come in. So in a way, we’re still bootstrap, but just with other people’s money at that point before raising an actual round of funding.

[00:03:33.950] – Sean

So a year and a half and you bootstrap. Were you able to or here’s a question for you, how long did it take to get your first client?

[00:03:44.080] – Jonathan

That’s a great question. So originally we were in build mode for probably six to eight months when we first started the company with no customers, building tech. And what we built was an application that analyzed consumer transaction data. So think like credit cards and debit cards. And every time you swipe, we’d see what you see on your bank account. And the idea was originally this is very, very long ago, about 10, 12 years ago. The idea was you’d link your bank account so we could get access to your transactions, and then we would give you reward points for spending at certain places. Essentially those reward points you could use and redeem for certain things like a chance to win a new car or a new TV or something like that. And so we had to build that entire application. It took, like I said, probably eight months before we even launched from a marketing perspective to get it in a place where you could take our first customers. So that was quite a journey. And it was fun. It was really fun. But it was always that when you click the on button and you’re like, okay, will anyone actually sign up and give us their data and think this is cool?

[00:04:56.200] – Jonathan

That was really nervous.

[00:04:58.350] – Sean

Yeah. We’re going to get into that. But before we get into the weeds, let’s just talk about the name of the business here. Is it Factius?

[00:05:05.390] – Jonathan

Yeah, Factius. So F-A-C-T and then E-U-S. Factius.

[00:05:08.970] – Sean

And this is just at a high level. I’ll try to give a shot in the dark here and then you can correct me if I’m wrong. But this is a data as a service. Is this an enterprise play or small and mid-size business play?

[00:05:20.450] – Jonathan

It’s right now an enterprise play. So all of our customers are large enterprises or pretty large, medium-sized enterprises. We think from a value perspective, our data has a down market opportunity. What we’re not struggling with but debating internally is always, do we do that ourselves or do we find a partner to help us get to that? We call it the long tail, but the SMB market where I still think there’s a huge play for data and more intelligence there and running businesses more efficiently with the type of data we have. But it’s tough. There’s a lot of businesses out there that’s a lot of sales people, a lot of account management people, a lot of marketing and also product market fit. There’s a lot of different sub-segments and niches that you need to productize for.

[00:06:07.740] – Sean

Sure. Just to pause here. I’d like to talk to the audience a little bit. So with Jonathan’s company, he’s got a B2B enterprise SaaS play. We’ll get into the numbers here in a little bit. But an enterprise play can be very lucrative for one customer. But acquiring one customer, it’s a lot of work. It’s a lot of meetings. It’s a lot of presentations. It can take three, six, nine, sometimes 12 months to secure customer. Whereas with Tykr, I consider that a B2C SaaS. We call it a low touch SaaS, like a Netflix. There’s not a lot of runway there. It’s like we let people in 14-day free trial, no credit card. And if you like it, great. And credit card. And if you don’t, you can -.

[00:06:47.420] – Jonathan

Self-serve, right? Yeah, that’s totally. Exactly. That’s a dream.

[00:06:51.550] – Sean

It’s awesome. I love the model, but I have a lot of respect for enterprise SaaS. So let’s get into that a little bit first or let’s start with the problem that you solve, you collect, sounds like financial data for enterprises, but what does it do? What pain does it remove for the customer?

[00:07:08.170] – Jonathan

Okay. So at a high level, we partner with what we call banks and processors in the industry to safely monetize consumer transaction data. So from a privacy perspective and a legal and data rights perspective, we’re compliant with everything from GLBA, GDPR, CCPA, everything. And we help them extract data safely so that we can help productize it. What we do from the product perspective is we have over 100 million debit and credit cards in the US. So we have a very good pulse on consumer spending and consumer behavior in the US. And that intelligence in and of itself is the value prop, the data and what you can extract from the data. Where I think we do try to call ourselves a daas company, a data as a service company, where data companies are a little different than SaaS SaaS is the value prop prop a SaaS software company is well defined within the software itself. So you build it, it takes some value or makes something easier, makes something faster, makes something cheaper. And like your business, people either want it or they they don’t. They pay for it or they don’t because or they think it’s worth whatever a monthly or annual charges are.

[00:08:21.170] – Jonathan

Where data becomes a little different is data is a lot more more like, I call like I call it clay, and SaaS is like a hammer. A hammer hits a nail or when you swing swing it. Can be molded into many different value props. So some of our customers are hedge funds and investors, active retail or stock traders. They use our data to try to forecast revenue for Target or Walmart before earnings announcements. The value proposition, our data definitely can do do that, most of our revenue right now comes from that. However, we are trying to get into the retail market. So someone like Starbucks, they could use exactly exactly the data that we give to a hedge fund, but they’ll use it to try to figure out which independent coffee brands in each city or region are encroaching and taking Starbucks customers away. So that’s where data as a service is really interesting and exciting, but it also does become hard. Hard. Like have to help educate your customer. And the idea, though, is it’s very leverageable. The same data set can solve so many different use cases. Theoretically, our company should be able to make a ton of money, be worth a lot.

[00:09:29.340] – Jonathan

That’s the goal and the idea.

[00:09:31.070] – Sean

I like that analogy of a data company being Clay because you’re right. One customer may use it for this use case and another company or customer may use it for something entirely different. Bingo. Bingo. Right? I assume you onboard a customer and you probably have an account team that works with that customer to like, okay, what do you want your dashboard to look look like? What are you looking for? Is that true? You have a team working with each customer?

[00:10:00.770] – Jonathan

Sort of. We have essentially our account/product team. And really it lives in product because those are the art experts on the data itself and what it can and can’t do and how you can and can’t use it. And when I say you can’t and can’t, it’s because data does have limitations at some point. When you only have US data, you have nothing really on Canada. Canada. So are natural limitations to data itself. But to be honest, a lot of that actually comes pre-customer, which is to your point on enterprise sales, you’re investing a lot in each sale, but it is a bit of like the horse leading the horse to water. People are interested in data. Starbucks, for example, was a customer at one point, and we’re trying to win the back. But when we sold them as clients, there was a lot of education on what can you use this data for and almost helping them build a business case internally to justify paying us for the data, which did work. But it is a pretty, I call it a consultative sale. And a lot of what you described happens actually upfront before the sale.

[00:11:04.330] – Jonathan

And then the idea, though, is we can propagate value throughout. Once we get the foot in the door, we can open up more opportunities because like I said, in Starbucks, they have a very large company. They have a marketing team, a real estate team to try to find new new locations should they open the new new one, analysis or intelligence team. There’s so many different teams that could use the data and opening up more use cases would help us grow the account. And that’s the land and expand strategy for the data.

[00:11:31.480] – Sean

Got you. So in summary, just to summarize your business model, what you do is you help companies become more efficient at marketing, sales, maybe even operations. Is that a correct statement?

[00:11:43.020] – Jonathan

Yeah. Nice. Those are the major touch points for at least retail. The other big one would just also just be, I mean, efficient is a good word, understand their competition at a much more granular level beyond surveys.

[00:11:56.070] – Sean

Yeah, that’s huge. That’s awesome. All right, let’s get into some of of the here. We’re talking about sales a little bit, sales cycle. How long on average does it take to onboard a customer?

[00:12:08.010] – Jonathan

That’s the question I’ll answer, but I hate to answer. Honestly, we’re probably three to six months is probably a good number. In some extent longer than that. I’d say that definitely is correlated with the dollar amounts, meaning the higher dollar amounts are a lot closer to six months and the smaller dollar amounts are closer to three three months. Fast. Our record was 28 days as the shortest, which was good, but also probably an outlier right now. I’m trying to get more of those in, but they’re in there have been definitely longer ones.

[00:12:42.270] – Sean

Welcome to the world of enterprise sales. It is not a glamorous process. Three to six months isn’t too bad because I’ve talked to other people that we’re talking six to nine months, 12 months. It’s just call after call.

[00:12:57.690] – Jonathan

We’ve had a few of those.

[00:12:59.010] – Sean

Oh, man, you take a customer out to to dinner. Are you thinking? Thinking? Are ready to move forward? Well, not quite. We’re thinking about that conversation over and over.

[00:13:09.130] – Jonathan

I’ll tell you the thing that hurts us a lot. At some point, you just can’t stress about things out of your control. Like I said, a lot of our revenue today comes from the investor market, from, I think, hedge funds, mutual funds and retail investors. When things like a new war breaks out, all of a sudden, everything’s on pause. The The market essentially what data data can you about how stocks are going to do it, not correlated. Data doesn’t matter at that point. There’s all these other global factors happening, or let’s say a pandemic where things lockdown, everything just gets put on pause all of a sudden. And like I said, we can stress about it, but there’s also literally nothing to do at that point other than write a letter to the board.

[00:13:50.690] – Sean

Exactly. Yeah, geopolitical geopolitical events, unfortunate. I actually just released a YouTube video, or soon will be released as of the recording of this video, so talking about some of these events can be really short, like an impact to the stock market and some can be fairly long. And in your case, in the sales cycle, it’s the same thing. It’s like it can be a blip on the radar or it’s like, all right, here we sit for the next few months. We’re going to have to deal with with this and.

[00:14:15.680] – Jonathan

And it’s always weird. I mean, you run your own company. It’s hard because no one saying no, they’re just saying, hey, not right now. So forecasting your own business becomes just very hard and difficult. But at the same time, we have no control over these types of things. Things. Just seem to be happening more frequently these days.

[00:14:32.890] – Sean

Right. Yeah. It’s no kidding around the globe. Interesting time to live. All right, let’s get into the numbers a little more. I want to talk about average contract size and then revenues of your company. Can you give us what is the range on the low end to the high end your average contract size?

[00:14:49.660] – Jonathan

So we break our market market matching the data products that we build. We don’t have that many, so it’s not a lot. But it’s good to understand in the data world, the top end of the market, I call them the high data acumen enterprises. They’ve invested a lot of people, money and technology efforts into building a very robust internal data practice. And those guys really like to buy what we call our, we call it row level data, but really just think of it as a stream of data where they’re doing all the data science and analysis on their end because they’ve essentially hired and invested in people and technology to do that. And we’re really just the raw materials, the data itself. Those contracts contracts ironically because we do less, we actually charge more for because there’s more you can get, there’s more value. And those average probably the low seven figures. So high six figure per year per annum deals where that’s like I said, these are large enterprises that have really invested in building that team. And that’s a very small segment of the market, but it does exist. Kind of the meat of the market where we actually do a lot of the data data science the word we use is but really it just means we select the best card holders that we have that are using it.

[00:16:05.980] – Jonathan

Maybe you guys know, but maybe you don’t. But the average American has four credit and debit cards in their wallet. I think I have maybe six. Most of us have more than one. One is the primary or maybe two are the primary, and the others are just you have accounts and they have cards. So we filter out just the top of wallet cards and make a really nice data panel where we call it a very representative of US spending. And those contracts probably range in the few hundred thousand per year range. It’s for companies that the heavy lifting that we’re doing, the heavy lifting, they’re really just extracting insight from, okay, now I want to do exactly exactly what you’re Understand my customer better, understand my competition better, understand maybe maybe spending behaviors better, those types of things. They can extract that very easily out of this product. We don’t yet have a product down market where it’s like I said, sub six figures. We think there is an appetite in the market there, and it’s more about what is the right product, what is the right fit, and how do you approach that market?

[00:17:07.300] – Jonathan

But we do think opportunistically, that’s a very green field.

[00:17:10.480] – Sean

And do you do any trial period with your customers? We can set up a dashboard, let you play with it for 30, 60, 90 days or whatever, and then after after we enter into the the.

[00:17:21.220] – Jonathan

Yeah, we 100 % do. In the data industry, it’s really interesting. It’s essentially become, I think, part of the sales process, what we we call, people call it a backtest, some people call it a trial period, where historical data. So think like a data that’s that’s I have data every day. So I have yesterday’s data today. So very recent data. But usually for a trial, we’ll give them data that’s three, six, maybe even a year old. And really what people are doing, and trial periods last between 45 to 90 90 days, our clients or prospects are doing are are really just trying to match up. We tell them our data is good and representative spending. So like like hedge what they would do is they’ll take a look at the data and say, if I had this data last year, how might my trading have been been different would that have worked out positively for me? A retailer might say, Starbucks, for example, they’ll take the trial period and say, your data says that that and Blue bottle coffee are encroaching on our market in California. They can look at their own first party data to see, oh, Im like, my California numbers dipped a little bit.

[00:18:32.860] – Jonathan

Maybe that is the answer and corroborate our data with their internal knowledge. Essentially, just if our data was completely wrong, they can’t trust it. So there’s that trust period, which is what the trial is meant for. Less like like where you’re trying to figure out, do I like this software? Does it work? This is a little bit more of a, can I trust what I’m looking at? Because obviously, obviously, myself, we’re going to tell you it’s it’s and everyone should be testing it. But they’re in a way testing to make sure what we say is true within the data itself, which by the way, sorry, I didn’t mean the tangent, but it’s an interesting thing when we talk about when we train our internal staff on the product and the sales team. On a sales sales I always tell people, Listen, you don’t need to trick anyone into buying data. What we have in our product documents are what they are, and they’re all going to see the data. So if our data is weak in the Midwest region because we just don’t have a lot lot of just tell them that, because at some point they’re going to see the data and they’re going to realize that.

[00:19:34.060] – Jonathan

And we can’t fabricate data all of a sudden manifest Midwest region another million card holders. So it’s going to come up and you just need to be transparent because they will see it. So it becomes interesting because they’re going to see all the warts and scars scars the data that are missing and and gaps. We just be upfront about it. However, the other side of the coin is there is no perfect data set. Set. And Mastercard might be the most comprehensive in our world. They do not sell data at the level that we sell it. So there’s no such thing as perfect. If someone wants perfect, we don’t need to measure ourselves against that, I guess, is what I tell my team. Sure.

[00:20:14.100] – Sean

Sure. That’s well put with data. You’re right. You can’t put a, what is this saying? Lipstick on a a Lipstick on a page. I was going to say that’s that’s.

[00:20:22.680] – Jonathan

Exactly what I was telling you. Tell us you want bacon, we’ve got got.

[00:20:26.030] – Sean

It, That’s it. Yeah. So in the process, process, I a part of the cell might be maybe the presentation of the graphs, like the interface. If you can efficiently get to the data speed, I’m sure is a factor. And how it’s presented, I’m sure that’s another factor as well. Is that true?

[00:20:43.770] – Jonathan

Absolutely. I’d say that’s where DAS companies, just like any company in tech, you need to put a little sizzle in your product. And the way you present data is actually it’s so so important not everyone’s data acumen is that high. Not everyone actually can read graphs that that well. Providing, like I said, dashboards or just presentations with relevant graphs and graphical interfaces are really good. The other thing we’ve learned is in each industry, the terminology is different, and it’s really important to get the terminology right, because then when they look at the graph, they’re seeing what you’re trying to show them. And from the data company perspective, it’s hard. I remember remember of the first conferences, the National Retail, NRF conference, January, and every January it’s the biggest retail conference in the the in New York. I would go around telling prospects like, Oh, we have card data, credit card data, and you can get a lot of insight from ours. And everyone tells me I have card data. I was like, Oh, this is not… Their definition of card data is their own card data. So they have their POS systems and their own everything.

[00:21:56.900] – Jonathan

And so they have access to all that. I was like, Oh, no, I have… In’t have everyone once they leave the the store, have all their spending on that side. We had to figure out how do we articulate this without confusing confusing them, at the same time, in less than 10 seconds, five seconds. And it is a little bit of maybe I should have prepped more for that. At the same time, it was a good learning experience, though. Like, hey, guys, I can tell you what not to do. But that becomes as much as the graphs and presentation of data is important, terminology coupled with that is equally important.

[00:22:33.070] – Sean

Good to know for listeners out there, you’re going to create an enterprise SaaS. Even if it’s SMB or even B2C, it’s like understand the data and how you present it to customers. I like the comment on the sizzle. That is a key factor. I’m going to jump back to the revenues here or the numbers, I should say. How long has this company been in business?

[00:22:58.280] – Jonathan

We’ve technically in business about, gosh, now it’s 13 years. We’ve gone through some pivots, though. And so I’d say the most recent iteration of the business is about three or four years old, where we’re monetizing data at an enterprise level. I call us a 13 year old startup. When I do interviews and we hire people, we’re getting close to profitability. But part of the reason we’ve been around for a long time is we actually were profitable for a good chunk of that 13 year year history We used to build data systems for enterprises and then hence transition to actually monetizing the data itself. That’s how we were in a similar market and similar space, and our domain knowledge is very relevant for what we’re doing doing what we did in the past. But the way the company operates is just very different now.

[00:23:49.940] – Sean

Got you. When you say build, were you like a service business, like an agency?

[00:23:55.090] – Jonathan

Yeah, exactly. The way we started the the company briefly talk about that idea where we had this B2C app. We quickly pivoted. And to be honest, it was not so much we failed at the app and pivoted. We did some very, very deep analysis on the cost of customer acquisition of B2C. And what we hypothesized was a critical mass and scale and how much money it would take to get to. I think we had on our little rewards app online, we had like 60,000 to 100,000 users. They’re all free. So it’s easy to get people sign up for something for free. Not easy. You’d have to pay for the marketing on the lead gen. But we quickly realized just literally talking to customers, essentially we were trying to sell ads based on our app where McDonald’s could target Burger King customers, and I could assure them based on transaction data, they had not shopped at McDonald’s for the past six weeks. And so it was supposedly like a very good value prop. And And everybody we talked to said, when you get to maybe maybe million customers, then I’ll be interested. But 100,000 is just too small for me to pay attention to this.

[00:25:09.250] – Jonathan

I was like, okay, that’s fair. And then like I said, we did a very deep analysis on on cost of a customer acquisition. I don’t what it would take to get to 5 million million users. And we’re a time horizon of X. We’re like, wow, that’s a lot of money really fast. And that was some napkin math. A lot of things can happen in between that change that for good or for bad. But in situations like that, you’re conservative. And sorry, I didn’t mean to be long winded on this, but what we ended up doing was the tech we built for the reward app was essentially 80 % of it was really good at analyzing consumer transaction data. The app part for the rewards is very small piece of the tech because we had to go through banking systems and understanding all the transaction data and managing that data. So we essentially pivoted that part of the tech into what you call like a B2B service business. And if for listeners out there that don’t know this, the US banking systems are still being run on very old 80s mainframes. And so when we started doing some biz dev work on discovering that we realized, realized, we have something that could modernize a lot of these systems.

[00:26:19.390] – Jonathan

And that became the pivot point where it was a service business and we’d go to banks and processors. We wouldn’t actually replace things, mainly because it was really scary for people to say, say, if your system goes down, people might not have access to their money. And we’re like, like, we don’t want to do that either. But what we did pitch them was if you extract the data out of the main frames into a modern data architecture, you can extract a lot of value value customers, putting AI tech on top of it. Ai back then was really honestly just math, statistics, algorithms. You could analyze that on modern cloud architecture that you couldn’t do on these ’80s main frames. And so they could could do interesting things. The class example I always tell people is a lot of banks, they have seasonal marketing. So in the spring, they would market new cars and auto loans and mortgages in the summer. And that’s when school is out. It was very seasonal, which makes some sense. But really, it was like you could be a lot more target and personalized. And so the example example like, if you analyze, let’s say, Sean’s transaction data and we see, oh, Sean has an increase in auto auto repair in October, and it seems to be like every month he’s spending $500, $600 on these auto repairs.

[00:27:36.940] – Jonathan

Maybe he’s actually a better target for an auto loan than waiting till springtime. Essentially, once you extract data out of the main frames and into modern architecture, these are like SQL systems, essentially, in the cloud, then you can put that type of intelligence. So we built these out for a lot of banks and processors. And that was essentially, like I said, our our period where we were doing this as a service.

[00:28:01.020] – Sean

Right. I could see that. And then you you to the data as a service. Do you still have the service arm of your business still in operations?

[00:28:09.540] – Jonathan

No, we sunset that. When I say sunset, we sold it all off to the actual customers. Probably, I want to say two years ago was the last bits of services. Essentially, we built systems and then we sold service contracts on top of the system. That was what we did and then just just off that a few years ago. But like I said, we’re working with the same data now, monetizing transaction data. So our domain knowledge is extremely high in the industry as far as understanding and working with transaction data. It’s not that easy to work with. That’s, I guess, what I’m saying. Saying. Just took raw banking data. No one could just make sense. So that gave us a little edge.

[00:28:48.740] – Sean

You as a business, your team, when I say you, was like, hey, we can continue this route where we provide a service and then we sell contracts with the service, or we can look at a more scalable model, look at SaaS, but call it Data as a Service, which is is this segment. So that’s the decision you you made look at something that’s much more scalable. We’re going to the moon. That’s why we were looking at a Data as a.

[00:29:14.490] – Jonathan

Service play. You sound like one of our investors. They’re like, like, the service business is great. You guys are profitable. Where’s the multiple? Where’s the exit strategy? Where’s this? And you hit it right on the head as far as the decision. I’d say what made it a little easier was… We actually did have some… It actually started in the investment space. We had some companies, hedge funds and company servicing hedge funds approach us about, about, we heard you guys have transaction data. We’d be interested in buying in. And at the time, the data rights and the contracts, we weren’t allowed to, but that did essentially pique the interest and allowed us to explore and analyze that segment to your point and just say, hey, this is actually a growing growing segment over year is growing quite large. It’s much much like you said, a a SaaS, type of business where you rinse and repeat, you can scale up faster, contracts are big, demand is growing. So that allowed us to look fair. So I would say it wasn’t just coming to thin air. We did get some luck and some people approached us about buying our data already.

[00:30:16.940] – Sean

That’s the beautiful thing is when you have a customer out there that approaches you with an idea idea of, could it do this? You want to totally be open minded to that conversation because your business could pivot in a pretty scalable direction. It sounds like that’s what happened to you guys.

[00:30:34.340] – Jonathan

Totally. That’s actually, I’d say, a piece of advice. Always take calls that might sound a little weird like that. It’s very little opportunity cost to take the call and try to understand what people are wanting to do. And it definitely helped. That was 100 % something that gave us that little boost in that direction.

[00:30:53.340] – Sean

Yes. Instead of like, I see a lot of entrepreneurs are trying to force a round object into a a square and it’s like it’s not going to work. You need people coming to you with a problem. Problem. And hey, we just happen to be able to solve that problem if we just do this little tweak here, that little tweak there. There we go. I love the segue here. We jumped away from the numbers. I want to jump back to that. Sure. Sorry. No, this is great context on the evolution of your model. Model. So years in business, do you recall how many years it took to break a million in ARR?

[00:31:31.990] – Jonathan

Probably Probably three or four. I’d three or four years is when we probably got about probably year three, four, because we did, like I said, we went through essentially we were doing free stuff for 100,000 customers for the first two something years. Once we got into the service business and building the data systems, those are big contracts, relatively large. Large. Those contracts, those those very long sales cycles. But yeah, those became multi-year deals, though, which actually did help. So I’d say, yeah, probably around year three, three and a half when we got there.

[00:32:07.420] – Sean

There. Three four years in in revenue or a to a million in in Okay. And then 2022, do you recall what your revenue was?

[00:32:15.030] – Jonathan

We were in the the eight to range there.

[00:32:17.440] – Sean

Eight to to ten Okay.

[00:32:18.570] – Jonathan

Yeah, in ARR. Yeah.

[00:32:20.030] – Sean

Nice. And then then projected.

[00:32:23.970] – Jonathan

We’re hoping to double our 2022. So 16 to 20.

[00:32:27.890] – Sean

Hey, nice work.

[00:32:28.920] – Jonathan

It’s been a good year. From a data launching perspective, we brought on a new data partner that’s been an extremely good data set. And that’s another thing that’s actually pretty interesting about DAS versus versus SAS, raw materials, your source data really has a lot of influence on how successful you can be, which makes sense if you think about about if you only had California data, there’s a limited use case. If you have all of us, it’s great. If you had the whole world, it’s even better. Acquiring data contracts is just as as important selling your data and building your products. You can’t just build. Like in SaaS, you can build your product and build features, and you have probably a Jira board of what you add and don’t add. Where in data, a lot of times you win at the beginning on your raw materials. You still have to execute everything else. But what I said earlier, you can’t just fabricate data. So if your data doesn’t have that much utility, you’re not going to really translate that to a lot of sales either.

[00:33:26.020] – Sean

Totally. Expansion revenue, what does that look like as a percentage year over year? Do you know, over the last year or two?

[00:33:33.260] – Jonathan

That’s a tough one for me to probably pinpoint, to be honest. I mean, that does exist and that’s definitely part of our growth strategy. I couldn’t off the top of my head tell you the number, but I could at least explain how it works in our world, what expansion revenue looks like, at least in both markets. I was saying earlier in the retail retail Starbucks, we might get one one Department, just say, like their market intelligence department, then the marketing department, maybe the real estate department, they eventually start buying rights to the data so that they can use it within their ecosystems. And landing, expanding is always much easier than trying to sell them individually. And then what’s really interesting in the Hedge Fund world, I don’t know how much of listeners understand how a lot of Hedge Funds work, but many of the very large Hedge Funds, let’s just say, like Lenium or Citadell, they’re actually broken up into what they call pod structures, where essentially it’s one big hedge fund broken up into 50 mini mini Hedge And the mini hedge funds technically are actually not allowed to talk or collaborate because they don’t want people to…

[00:34:42.960] – Jonathan

The idea is that they all have 50 different strategies and some smart guy picking stocks on this one strategy. And if they collaborate too much, they actually might converge into similar strategies or like strategies and the idea is diversify. Just like everyone in our own personal personal finance, everyone says Hedge Hedge do the same thing with what they call different portfolio managers. So it’s 50 different pods within one hedge fund. Usually when we sell our data to a hedge fund, it’s a handful of pods that are interested in the data. Each year, more and more get.

[00:35:15.020] – Sean

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[00:36:19.100] – Jonathan

It’s actually more like like to be honest. It is more like seats. So what we do for like these, like I said, we call them pod shops, but there’s like a license per pod. Pod. What’s actually nice is it’s a self-regulated type of thing where they can get in a lot of trouble with the SCC if they’re trading, using data that they’re not supposed to use. Use. So disincented to cheat. Cheat. So even I have a data pipe set up for, let’s say, one of the big hedge funds, let’s say, Citadell. Technically, I have no control if they wanted to share the data with all 50 pods, and I can’t really police that at all because the data is on their side of the fence. But what’s nice is from a regulatory perspective, they’re very very to do that because they can get a lot of trouble. So we have that trust barrier where they have to buy additional seat licenses for each one and they’re all priced out. And And the pricing that I quoted you were actually, actually, like I it varies between. Sometimes we do like an enterprise deal. That’s the seven-figure deals where we’re like, all right, you want economies of scale.

[00:37:19.090] – Jonathan

We want a large contract. You want to turn everyone on. We’ll build that in. Those are the seven-figure deals.

[00:37:24.170] – Sean

Got you. Okay. And how many employees at the company?

[00:37:28.050] – Jonathan

I want to say we just hit 30. Still pretty small. I still know everyone’s name.

[00:37:35.800] – Sean

That’s good.

[00:37:36.850] – Jonathan

You’re.

[00:37:37.780] – Sean

In a good spot. 30 employees at estimated or projected 16 million in revenue. Looks like you’re about to be profitable.

[00:37:44.280] – Jonathan

Yeah, that’s exactly right. Right.

[00:37:46.060] – Sean

We’re pretty How much have you raised to date?

[00:37:49.410] – Jonathan

I want to say we just closed a technical this this Series B.

[00:37:56.070] – Sean

Sounds right.

[00:37:56.860] – Jonathan

Year. Up to today, I want to say we raised probably 15 million.

[00:38:01.930] – Sean

Okay.

[00:38:02.790] – Jonathan

That’s – Around there, I think. That’s probably a ballpark. But yeah, it’s close.

[00:38:06.510] – Sean

Sitting in your Series Series that’s yeah, that sounds right based on some of the other guests I’ve had on that have have businesses. But that’s great. You guys are in a really healthy spot. Good to see you’re expanding. Before we jump to the Rapid Fire round, what is one key takeaway you can give to somebody who is an aspiring entrepreneur that wants to create an enterprise SaaS business?

[00:38:33.340] – Jonathan

So I would say I think one of the things that have benefited me in my career and being where I am today has always been immerse yourself in other knowledge. I’d say when I say other knowledge, being an expert in what you do in your industry is really important. But entrepreneurs, we literally solve problems that people don’t know exist yet. And in order to to do you have to have knowledge outside of your own domain to really help solve the problems. It sounds really vague, but for me, when we started the company as this B2C application trying to give people reward points, I had no idea how banking technology worked in the whole industry. And it was only until diving into all the essentially inefficiencies in that market that we could see the force from the trees and where the opportunity was in the initial pivot, which was, oh, these people don’t have… The US is this amazing technology country, but the banking system is actually extremely lagging. And the reason there’s less innovation in the banking sectors and why other countries are growing faster is because the systems are so old. And how can we approach that?

[00:39:45.650] – Jonathan

And like I said, you don’t learn that in school. You don’t really learn that on the job either when we were doing what we’re doing. It was more just having that ability to educate, self educate and motivate and learn and read and research. So I think that’s like really important to think outside of your own domain. I wouldn’t say outside the box, and it’s not even about trying to find an idea. It’s just learn about industries and things. That’s, I think, really, really important.

[00:40:12.220] – Sean

That advice is actually very much in line with Ray Ray He owns the largest hedge fund in the world. He actually does not hire anybody who has went to school for economics or finance. He likes to hire art students or teachers or or shouldn’t say somebody in the trade, maybe somebody in the the but somebody who has that ability to switch gears from one industry to the next and quickly understand and learn things on their their and at the same time have some creative as well as critical critical that is a more valuable employee in his eyes than somebody that’s like, I only went to school for finance. I only want to have a career career finances. Totally. Yeah, because you’re.

[00:40:54.860] – Jonathan

Siloed then. That’s a great, that’s a huge skill. Another example I’ve seen seen to Sigma, good friend of mine works there. They’re one of the largest systematic hedge funds, which really just means they use a lot of AI to do the trading for them. They train a model, but they hire actually a lot of PhDs in non-finance or non-economics, not even statistics. A lot of them are like physics or natural sciences. And from what I’ve heard, some of the stuff they do in the lab is like, well, markets like stocks and traders are operating like these evolutionary animals back in the day when things were fighting and competing for resources in the world. And so they have these biology and physics PhDs applying a lot of algorithms that they use to discover things in the rainforest to try to apply to capital markets. I have no idea how it works or if it does work, but their fund is really successful from what I understand. But exactly what you’re saying, different disciplines have a lot of value in cross-pollinating with what you do. So yeah, definitely I’d say always read outside the box or research, maybe not think, but keep your skills sharp within what you’re doing.

[00:42:08.580] – Jonathan

But having more knowledge is always better.

[00:42:10.910] – Sean

Yes, I love it. All right, let’s jump into the rapid-fire round. This is the part of the episode where we get to find out who Jonathan really is. All right, if you can try to answer each question in about 15 seconds or less. Are you ready?

[00:42:25.080] – Jonathan

I’ll give it a shot, yeah.

[00:42:26.120] – Sean

All right. What is your favorite podcast?

[00:42:28.390] – Jonathan

Well, besides this podcast, Payback, I’d say RadioLab. I’m a big fan. And podcasts are a great way to consume what we were just talking about, knowledge about other things. And I do have a small commute to work, so it’s a great time. But I’ve enjoyed RadioLab for many many years I think the way they do their documentary style, deep dives into industries I’d know nothing about or disciplines is really fun to also take your mind off work, but also continue to connect things in your brain.

[00:42:56.310] – Sean

It’s in my list, actually. I have listened to that show show Good one. What is a recent book you read and would recommend?

[00:43:04.200] – Jonathan

Read and recommend. Oh, Bad Blood was good. I know it’s a little bit older and not new new, but that was good. That was a a I’d I’d for entrepreneurs like us, it’s an interesting way to really see how VC industry can really just snowball into good or bad things, but a good insight into just what happened and how that happened. The modern and wrong type of stuff.

[00:43:32.100] – Sean

That is actually one of my favorite books I’ve read in the last five years. Years. Oh, For the listeners out there, it’s on the story of Theranos and that whole debacle.

[00:43:46.080] – Jonathan

Elizabeth.

[00:43:46.550] – Sean

Elizabeth Yep. I’m gravitated towards big lessons learned in the tech industry. I just finished the docuseries on Netflix, Netflix, on Uber with Joseph Joseph Gordon. It playing Travis Kalinik.

[00:44:03.550] – Jonathan

Oh, right. Yeah, he was playing. Yeah.

[00:44:07.000] – Sean

Love learning about the train wreck. It was very good. Very good. And of course, what is it? The The or the the I think is the name on.

[00:44:16.920] – Jonathan

The Founder, the McDonald’s one?

[00:44:18.070] – Sean

Yes.

[00:44:19.420] – Jonathan

Michael Michael That was great. Actually, I love that one too. I know that’s based on a book. I just haven’t read that one. But that dramatized movie was really good. I do think the interesting lessons I’ve seen in just just Bad or Theranos, Uber and Uber lift and Airbnb, I actually sometimes when I talk to my family about entrepreneurship, it’s interesting as entrepreneurs, sometimes there’s opportunities on these fringes of legality, which realistically, sometimes they work and sometimes they don’t. In a way, Uber, the application and as a consumer is a great idea. But realistically, it was illegal to run a non-licensed non-licensed service in most major cities in the US or Europe. So the very, very beginning, you’re not supposed to do that. And the tech involved was super cool. You could punch it in, start end destination, driver knows where to pick you up with because the phones and they don’t even need to know where you’re going. They just follow the line. Great idea. Cool way to revolutionize the industry. But that opportunity came on the fringes of legality, which is just an interesting risk profile on as an entrepreneur. I don’t do anything outside of any legal bounds at our company, but it is an interesting, Airbnb same thing too.

[00:45:39.230] – Jonathan

You’re not supposed to rent your apartment out to random people if you’re in a building, but that opportunity also came out. And so I think it’s an interesting fringe case that at some point that’d be a good deep dive into how do you figure out if you should or should not dive in when you know it’s actually illegal, but I think we can get over that legal boundary at some point and then become super profitable. And I don’t know what that pitch looks like.

[00:46:04.230] – Sean

There’s wisdom there. The path less traveled is where the biggest rewards can really happen. So if you’re flirting with that, okay, we’re on the fence here between legal and illegal and you really want to disrupt something, you have to go to that. You have to be willing to go to that edge. Totally. And most people will not take that journey. I know we’re talking about Spotify Spotify the show. That’s right. An example fighting the big players out there. It’s like most people are sitting back and like, Nope, I’m not fighting that battle. I’m going to go create something else. And then you get guys like like that are like, I’m going to push this.

[00:46:43.050] – Jonathan

And that’s actually a better example of probably where an entrepreneur can do do something was less about legal and more about business rights. It wasn’t actually illegal from a common law perspective or constitutional perspective, but it was like, okay, you’re violating potentially business contracts or pushing those boundaries. Whereas Uber and and where it’s like, no, there’s actually laws and licenses and real life stuff, which.

[00:47:07.130] – Sean

Yeah, those.

[00:47:08.290] – Jonathan

Are all interesting. Same with with where they were just pushing those boundaries pretty bad.

[00:47:12.890] – Sean

I can get behind them all except for Theranos. Theranos. Like into when it’s human lives involved, I’m like music.

[00:47:22.230] – Sean

Totally agree. I can touch that real estate. I would maybe go there.

[00:47:26.910] – Jonathan

Nobody’s dying from that perspective. No, I totally agree. Itotally agree.

[00:47:30.560] – Sean

Yes, exactly. I know we took more than 15 seconds, but I really- Sorry about that.

[00:47:35.570] – Jonathan

That discussion was great.

[00:47:36.870] – Sean

Loved it. All right, let’s get into the movie question here. What is your favorite movie?

[00:47:41.650] – Jonathan

I think it’s a cross between… And this is just because I loved it when I was young and I think it stayed the rock with nick nick and Sean Connery. Oh, my God, I love that movie. And that’s my probably cheesy nights action movie, but love it. And then Kiss Kiss Bang Bang. I don’t know if anyone’s seen that. Yeah, That’s Robert Robert Jr, amazing. Shame Black.

[00:48:03.150] – Jonathan

Directed. Oh, good. I love Noir and I got into Noir fiction in college. I read a lot of Raymond Chan there. Then Kiss Kiss Kiss Bang was this perfect mix of of noir, comedy, and how that is. It’s super.

[00:48:17.040] – Sean

We won’t spend too much longer here. I just read Age of cage. It’s on the movie history of Nicholas cage. Any nick cage fans out there, pick up that book. It is the nostalgia from the beginning of his his all the way to the present, even touching on one of the latest comedy films he did, Unbearable Weight of Massive Massive Yes. It’s so good. But he touches on the rock and Conair and and face-off like the out there who grew up around, like I was born in the early ’80s, so anything that Cage?

[00:48:51.220] – Jonathan

Totally.

[00:48:51.940] – Sean

So yeah, good, good. Love your choices there. All All right, we get a few more questions here. What is the worst advice you ever received?

[00:49:02.800] – Jonathan

Yeah, I’d say unfortunately, I think that it comes from probably my family in general, but it was just definitely pick something stable and you can have fun outside of work, but you got to do something stable to make money. I feel like my parents immigrated from Hong Kong, and same with most of my extended family, I’d say that was like a pretty normal immigrant type of mentality to come and just stabilize life. And I think where I’d say it’s the worst of the the is I think separating honestly with a wall, your work life and your social and I I guess I think it’s a mistake a lot of people… It is me me I’m not telling anyone to subscribe to what I believe in, but I personally think there’s a lot more. You can mesh and blur those lines a lot more in life. And yes, work-life balance is a super important concept for mental mental health, and health and mental-health is an important part of work-life, but you can make lifelong friends at work. You can have great relationships through work. I think denying that and separating those two in silos is a mistake, especially in today’s age where work is such a big part of your life.

[00:50:19.070] – Sean

All right, flip that equation. What is the best advice you ever received?

[00:50:23.740] – Jonathan

The best advice I’ve ever received? That’s probably also from my parents. My dad definitely always told me, in his own way, tell me to be be humble. Never the smartest guy in the room. But growing up playing sports, he was always like, There’s always someone better. So don’t try to become the best. Just put in your work, do the best you can. But essentially mentally pushing yourself down when someone… There are always going to be smarter people. People. There’s always to be someone faster than you and jump higher than you in sports. And that’s just reality of life. And that’s okay. Essentially, it’s okay. Be humble, but also be confident in what you can and what your abilities. Essentially, you don’t need to worry about not being the best because being the best is really hard and you don’t have to be the best to be successful.

[00:51:23.640] – Sean

I remember a coach, I used to be a competitive swimmer, and a coach once told me he’s like, there’s always somebody better. You could be the best guy at this particular event on this this at this meet, but there’s always somebody better. Keep that in mind. I will always remember that because it applies to life.

[00:51:41.320] – Jonathan

That’s literally what my dad used to tell me. There’s always someone better. There’s always someone better. But it was like, don’t worry. You suck. It was more like, it’s okay.

[00:51:49.890] – Sean

It’s just perspective and humility. There’s this underlying tone of wisdom there on humility. It’s great. Would you swim?

[00:51:58.500] – Jonathan

Were you a sprinter? Hundred meters?

[00:52:00.610] – Sean

I was a backstroker and flyer. Good Good.

[00:52:04.480] – Jonathan

I.

[00:52:05.290] – Sean

Love the the For the listeners out there, I was the guy that really loved the underwater work. But as soon as you put me in the surface, that’s when the real real would run me down.

[00:52:17.700] – Jonathan

Awesome. Yeah.

[00:52:19.790] – Sean

All right, last question here is the time machine question. If you could go back in time to give your younger self advice, what age would you visit and what would you say?

[00:52:27.940] – Jonathan

I’d say it was… I’d probably visit my college years and probably just in that window. And I think I would take essentially some of my own advice that I just gave on this podcast, which is is and research outside the box and not get too consumed with essentially what I was doing in my major. And college is a great opportunity to explore many things in life and different subjects and different expertise. It’s true. I wish I did more of that. And although I think I’ve got a pretty well rounded probably mind now, I do think I spent a little too much time obsessing over my economics and my accounting stuff in school and probably could have enjoyed a lot more other subjects. Like I said, from a learning perspective, college was a fun time. I’m sure most people had good experiences there, had good time. I think that opportunity doesn’t come essentially twice in life where you’re essentially a professional professional student being able to read and learn from professors that are experts in their their field. It’s great opportunity to explore different things.

[00:53:37.460] – Sean

Nice. Great advice. Love it. All right. Right. And can the audience reach you?

[00:53:42.560] – Jonathan

They can reach me either on LinkedIn. So, Jonathan Chin, co-founder at FACTIS. I don’t think I’m the first result, unfortunately. There’s a lot of Jonathan Chins out there. I’m still bummed about that, but I’m pretty easy to find. I think my profile picture needs an update because I had my COVID hair, which was down at my shoulders. And also, yeah, it’s the LinkedIn or my name, jonathan. Chin, like the chin, at factius. Com is my email. So anybody interested in talking shop or want to learn more about data, please hit me up. Awesome. Awesome.

[00:54:14.890] – Sean

All Jonathan, thank you so much for.

[00:54:16.660] – Jonathan

Your time. Thanks for having me, me, It was really great.

[00:54:18.840] – Sean

All right. We’ll see you. Hey, I’d like to say thank you for checking out this podcast. I know there’s a lot of other podcasts out there you could be listening to, so thanks for spending some time with me. And if you have a moment, please head over to Apple Apple and leave a five star review. The more reviews we get, especially five star reviews, the higher this podcast will rank in Apple. So thanks for doing that. And remember, this show is for entertainment purposes only. If If heard any stocks mentioned on this podcast, please do not buy or sell those stocks based solely on what you hear. All right, thanks for your time. We’ll see you.