>> Thank you everyone for joining us. This is Scott Dubin with FF venture capital. We're gonna give it just one more minute as people process through the system, and then we'll kick off thanks for your patience. All right, so again this is Scott Dubin with FF venture capital.

Thank you, everyone for taking time out on your Tuesday, for what we hope and what we know will be a very interesting conversation. We're very excited to be hosting dataPlor today for a conversation and ffVC CEO series. And so that Jeff, I'll try with that John will hand over to you.

>> I wanna thank folks for joining us today, this gonna be a fun conversation. Jeff is one of the nicest people I know and we're very pleased. I met him back in 2018 in Hawaii. We weren't on vacation it was all work, I assure you. At that point the company was based in LA it's now based in Miami.

So there's a lot of story there as well. We invested a couple of times since we love the company what they're seeing is coming enormous way. Jeff himself who's been in nuclear counter-intelligence for the US, and so is a real patriot and someone who's just I couldn't think of someone starting such great company as Jeff model is doing.

And so that brings us to dataPlor. So, dataPlor is emerging market data, that's made available to institutions so can't get it else wise they pay a lot of money for it. They pay a lot of money for it to be maintained, and there isn't as good a place together as day to floor.

So, I'm gonna hand over to Jeff to talk through the story and the like. And really this is going to be Q&A I guess so, I guess Jeff did a little introduction on the company and then we're gonna go back and forth. And please saw the put your questions into chat or into Q&A.

And from that we can handle those, particularly at the end. Cool, so Jeff, what is dataPlor? What's the product? Who are the customers? Why do they need you?
>> Awesome thanks John and thanks for the introduction. As John mentioned I'm the CEO and co founder of dataPlor and to put it simply we provide accurate places data to fortune 1000 companies.

You're probably wondering what's places data or what's point of interest data. It's everything that's around you, so ground truth of knowing that fits Jeff's cafe, or Jess law office, or Jeff's park in the US, the data is readily available, very accurate, but internationally it is not. It's the exact opposite and that's what we solve.

What do you lose, would you like me to go from here? John, you want me to tell you?
>> I know you can't talk about the customer names. But, some of the largest mapping company in fact most of the largest mapping companies in the world, now ingest your data.

So, kinda like, why they come to you because they've had for a while, what was the problem you're uniquely solving?
>> Yeah, that's a great question. So, let's take it to the US and I think that's something that most people can relate to. In the United States we live in a very formal economy where, if I were to start a business, Jeff's cafe in California, the Secretary of State is gonna publish that information within a few months.

From there I probably maybe I create a website, but I create some sort of digital footprint. Could be Yellow Pages, Tripadvisor, Google My Business, all of the above. And so there's actually big companies out there like Yext and others that keep that data online accurate. So what ends up happening are these big data companies out there, they scrape the public web.

And they know US government data's pretty accurate and then they know all this online data is pretty accurate as well. They're able to run their algorithms over it to merge it to match. And at the end of the day they're pretty confident Jeff's Cafe exists at 123 Main Street, right?

Sometimes you can take an Uber somewhere else and it's thrown, but most of the time it's good. From there these companies build products, right? They can build mobility products, they can build firmographic products such as how many employees, what's the revenue stream polygons. What's the size of the business so the square footage.

And all that creates a geospatial solution in the US. Okay, perfect, makes sense, there's about ten companies out there that are worth over a billion dollars competing in this market in the US. Let's go abroad, so in the US was about 25 million places internationally, 350 million places.

So, much bigger market opportunity than the US so that's the first part. Two, let's use Mexico as the first example and it's the first country we launched at. We used the government database, which is called the. And we pulled all the data and we did an analysis of it.

Turns out that only 19% of the data is correct. The rest is wrong. So, that's a big problem if 19% of the data is accurate with the rest being wrong. Because some of our customers actually used to use that data and thought it was accurate for being from the government.

Secondly, if you look at online Jeff's cafe in Mexico City, you go to Google My Business because how Google works there, you actually have to request a physical postcard. Sent to your business in Mexico City take a picture of the QR code. And that's how it gets verified.

So, most businesses even on Google aren't even verified right? Because less than 1% do that. So you look online and you see Jeff's cafe and Google My Business. It says, Jeff's cafe 123 Main Street Polanco Mexico City you're like, alright, this must be good. You go to tripadvisor to make sure that the reviews are great.

It says cafeteria de Jeff Main Street 125 Condesa, Mexico City. You go to the next site, it's something different. And you look on the map and you're like, they're all different locations, they're different phone numbers, what's right, what's wrong? So, what ends up happening is these big data companies, they take this public information and it ends up being bad data in bad data out.

In our competitors data's accuracy is about 30% And that's a huge problem. So imagine if you're Uber Eats, and you're in Mexico, and seven out of ten, phone calls don't work, that's extreme inefficiency. Imagine if you're a mapping company, and you wanna make sure that you get to the right location.

Well, if seven out of ten are going to the wrong location, that's a really bad problem. And so that's what we're solving for. Now I'll get into how we do it and why we do it, if that works for you, John.
>> Yeah, I mean, we'd only give away too much of a secret sauce, but yeah, please.

>> Yeah, so obviously keep it high level. Everything we built is in-house and proprietary and it really comes down to our process. So if you look at Mexico, as I said, our competition just takes whatever is available on the web. And this is the best they got, it's 30% accuracy, best of luck to all their customers.

So what we do is, one, we take every public available source, just like they do, and we run the same sort of algorithms, and we find out, wow, there is a lot of bad data. From there we have AI call bots that make up to 50,000 calls a day in the local language.

We actually have male and female voices and they ask simple questions like, is this Jeff's Cafe? Yes or no. Are you at 123 Main Street? Yes or no. Do you accept credit cards? Yes or no. And we associate that to a place in our database, right? So, but sometimes phone numbers don't work, they don't answer.

We have visual learning where we're able to ingest tens of millions of photos and be able to see, okay, is there any sign that says Jeff's Cafe? And can we put OCR technology around it and be like, all right, this is Jeff's Cafe, it is at 123 Main Street.

Associate that geolocation to a place in our database, right? So accuracy, accuracy, accuracy. And then from there, if we are still unsure, we have people in each country, and in each country, these individuals, we call them validators. They give us local knowledge, such as in Chile and Hong Kong, we had to tell the head of Apple Maps who reports to Tim Cook, that they don't use zip codes really in Chile and Hong Kong, and they got to get over it, right?

So we become the experts in the space by having people in these markets who also validate data that our systems can't produce. And at the end of the day, we're about 98% accurate.
>> Well, 98% versus 30% is probably the reason why these companies are paying you a view order of $100,000 plus a year.

And I love to use this term about entropy and data. They have subscribe to you to maintain data accuracy because businesses open, businesses close, businesses move, and without getting consistent data over time from you, it's a real problem for them. So it's a real lock in. So that's a lovely dynamic of a business.

Another thing I like as an investor is it costs you about a customer or so, I know one quarter customers to pay for a whole market. Take Mexico, it probably cost you one quarter customers to pay for Mexico, but you have multiple customers in Mexico. So every incremental customer is incredibly high margins here.

So I love this of mine the data once and sell it multiple times. And then your customers cuz they're global, they follow you around the world. And I believe you're now up to 40 markets and you're only in one market when we first invested. So why don't you talk a little bit about that market journey and how you can expand to so many markets and still have the company approaching manage profitability at this stage?

>> Yeah, so I mean, we've grown 4x year over year, 10x growth in customers year over year, past year, 20x growth in our product. As John mentioned, our customers don't license our data. So how we work, look at us like a SaaS solution, but on an annual subscription, right?

They pay upfront, fully upfront, let's say it's Mexico and we'll just use 50 grand, for example. 50 grand, they pay for it, and they have access to our data for a year, and it auto renews after 60 days. The problem is, is that after 30 days, after 60 days, after 90 days, that data starts to decay.

So our process in keeping that data up to date is what they're paying for. And so if a customer were to no longer work with us in Mexico, they would, one, lose the updates, which means their data will go bad and stale within about three months. And two, they don't have our data forever in perpetuity, right?

Which means you gotta clawback that data, which is very hard to do. And that's why from a churn rate, it's only about 1 to 3% on average for enterprise clients. And to date, we've had zero churn.
>> So, negative working capital because you're paid up front, high margins, high retention kind of business.

That's the kind we like, and the growth factors or number of customers, number of markets, and presumably over time, you can broaden the data sets yourself. So it's very much land and expand.
>> Correct, yeah. And to answer your expansion question is, we really rely on our customers, right?

For example, a large CPG customer wanted data in Saudi Arabia. We weren't there yet, but they're one of our largest customers. So we went and got it for them. It costs us about 30 grand, give or take, to build it, right? They paid us, I think, 35 grand, 40 grand for it.

So this customer ended up paying for our entire database, and now we're layering it and asking all the CPG, all the mapping customers that we work with, hey, are you interested? And so how we grow is we ask our customers, we get two, three, four, five customers interested in the markets.

And then we go and launch them, move them into evaluation, and then close them. And that's the way that our roadmap is been set up is our customers pretty much tell us where to go because they're the ones paying for it upfront, which makes our risk very, very low.

>> So what inspired you to start this company?
>> Yeah, good question. So in my previous life, I also worked at LivingSocial running data partnerships where I would actually license tens and millions of dollars worth of data from companies to help LivingSocial sales teams, marketing teams, mapping teams, etc.

LivingSocial at the time was a big competitor to Groupon. From there, I started a company. I started a company with some co-founders where we crowd source data in the United States. We had about 5,000 outside sales reps who just got paid for, they were going into places anyway, send us the data.

We ended up selling that company and had a nice soft landing, private equity firm bought it. And so I've been in the data role now for a while, and one of my good friends and mentors is a guy named Matt Graves. He was the CEO of Infogroup, a 500 million ARR small business data company in the US.

And he was telling me about a bunch of problems, and international stood out to me. And I was like, why is international so hard? And once I learned all of the nuances of international data and how dirty it is and no one's solving it, I felt like, well, dirtier the better, harder the better and that's why we started Data Blur.

>> That's kinda crazy, but that's a great origin story. So, let's talk a little bit about the evolution. You're now at 40 countries. I think there's somewhere around about 193 countries you probably will never do some of the developed markets, so I believe Japan's on the list. So, what's full market penetration?

>> Yeah, so full market penetration I think is really gonna depend, so we definitely wanna get up to 100 countries, right? There's 100 countries that I think are good, that are valuable, but we might get more than 100 countries. But those turn into more like regional deals, look at the Caribbean, for example, right?

Selling just Jamaica, it's probably not gonna be worth it versus selling the entire Caribbean data set, right? And we're already approaching 100 million places in our database. We'll probably end the year with closer to 150, maybe 200 million places in our database, and being probably close to 100 countries by end of year.

And we're doing that based off of our clients roadmap. We've been able to build it and automate it and scale it, right? And that's something that's really important when it comes to the competition, is our competition is legacy companies that are 15 years old, 20 years old. And we're building from an international first perspective, right?

And that's how we've been able to learn all these nuances and get us to where we are today. And to build that repeatable playbook where we were in one country as you said to now 40, to hopefully over 100 by end of year, which would be 1 to 100 in about 18 to 24 months.

>> Cool, so clearly you've got mapping companies, CPG companies, food delivery, e-commerce delivery companies, consultancy companies as kind of customers. Unfortunately, a lot of them have to remain nameless, but against that, let's talk about hedge funds. How can hedge funds use this data?
>> Yeah, so I think you're spot on where we work with pretty much every mapping company on the planet.

We work with the biggest CPGs on the planet. We work with the biggest third party logistics on the planet, right? And ride sharing is on the planet. So you guys can take it as it is, if you wanna talk about it in private, happy to, right? About those customer relationships.

So let's talk about hedge funds and what that looks like. So, that's like alternative data where, if you look at Mexico, and we've been building this dataset for a couple of years now, we know the open, close, right? So, did Jazz café open, and did it close? Jazz café closed and now it's John's Veestro.

All that information is super important, right? How many businesses are in a certain region in Polanco, which is a city or a region within Mexico City. That can get on the micro level to the macro level of all of Mexico about how many businesses, what's going on the ternary.

And so, we've actually been working with Bloomberg, we've been working with a bunch of different hedge funds and private equity firms that are interested. And wow, this could be really good information to know how that economy is doing. We know more about the economy than probably the World Bank on a small business scale, right?

Because the government doesn't know or they only have 90% accuracy. So that sort of information can be sold to financial firms across the globe. Secondly, when it comes to hedge funds and financial firms, we've had several customers reach out to, or potential customers reach out to us for fraud prevention.

Imagine if you're giving a $20,000 loan to a business in Mexico. Well, it'd be great to know it one more step of verification that data poor knows that this place physically exists, right?
>> And has existed for more than six weeks.
>> Yes, and has existed for over a year, or two years, right?

And that's a sort of information where it's gonna become more valuable, and that's what we're building towards.
>> Cool, so you've got these different vectors, customers, markets, data, how big can this get?
>> Yeah, so the way I look at it, and there's a couple ways you can look at it from the market itself where there's ten multi-billion dollar companies focused just on US data, right?

And that's like 25 million places when we're going after 250 million places. So, right there, the coverage, the geographic expansion, and being that leader alone makes us a huge company. From there though, we wanna be a full suite solution. We want a client to come to us and say, I don't wanna go to five different vendors for satellite imagery, for mobility, for firmographic, for polygons.

If we can be that all in solution it's great, you're paying for a subscription to our data, and you're adding on these products. And that's where we get super big where we own the entire geospatial process for these companies. And that's when it's really hard for any competition to get in there, because we're that one stop solution.

>> That's kind of exciting, and I think you know FFPC looks to find companies who can get to 100 million revenue run rate or more. So I have my fingers crossed so you're gonna cross that threshold. We have about 10% of our portfolio there, so it's kind of exciting.

Let's talk about the R word, recession, right? Everyone's concerned, we're going into recession, the large companies are talking us in there. The shutting down of the money printing is doing that, consumers are maxing out their credit cards, every which way you want to articulate it. So, are you cyclical, counter cyclical?

What have you seen in last couple of months, are people pulling back, stepping up? What do you think?
>> Yeah, so from investment standpoint, it's a crazy hot mess out there, right? The public markets, VCs are crazy, but from a customer standpoint, we have more demand than we can fill.

Our VP of sales is complaining every single day to me and our CTO about how can we get out more deliveries? How can we move faster? And the reason that is, is because data is something that you don't ever cheat on. Because again, if three out of ten phone calls work, if you're a marketing and sales organization, that's not good.

You want at least eight, nine out of ten working, right? If three out of ten or seven out of ten of your locations are being dropped off in the wrong place, they're switching providers, right? And so, these big mapping companies, these big CPG companies, these big financial firms, they are all competing against each other.

And when we talk to them, we let them know who we are working with, and they absolutely wanna keep buying, right? That's also the way for growth, right? So, if you are looking at the market, international economies are the way for growth right now. You look at Latin, you look at Southeast Asia.

They were closed down for a while, they're now opening up. And so the CPGs are, well, I wanna get market penetration. And so from a recession standpoint, we're not worried about it as a business. We've been running this company very lean the whole time. John has you know, I run it like a very tight ship.

And at the end of the day, I think our revenue is gonna speak for itself and I think our clients are speaking for themselves.
>> Yeah, and again this time your managed profitability means any capital you're coming up to a capital raise, any capital you raise is gonna be growth capital.

So-
>> Yeah.
>> Let's talk a little bit about, as a manager, we're in this sort of hybrid remote working world, you've run the business from Hawaii when you're part of the blue startups accelerator, LA, Miami now, so you've got hybrid, you've got remote. How do you deal with employee burnout, employee engagement, just sort of company culture?

>> Yeah, that's a great question. It's hard. It's hard every day, right? First, we've always been a fully remote company from day one. So fortunately COVID didn't have an impact on us from a work environment, right? We were able to always sustain and be remote. We've actually really perfected how we work together.

My CTO is based in Hawaii and my engineers are based on CST and ESG. And so, what ends up happening is my engineers work during the morning and afternoon, they push all their code. My CTO comes on and then reviews the code, provides comments. And then it's like this nice circle, right?

So that's what's great about that. We have daily stand ups and everything that I think every company should do but what I really focus on, John, is time off, right? We're sprinting hard a lot so for Memorial Day, we have four days off. You gotta make sure that your team is well rested, well taken care of because if not, they're gonna burn out and if they burn out, that's just gonna be a hard problem for an early stage company, right?

Every person we have is an elite individual and if they leave, we gotta fill that. So, a lot of time off, making sure that personal days, mental health days, those sorts of things it sounds cliche, but I force them. I'm just like, hey, we're having a data blur all day this Friday and it just changes things.

>> It's important. We have unlimited vacation at FPC but we track it, and we track it to make sure that people do actually get time off as well. If anyone has any questions, please put it in the Q&A or in the chat and let us know more awesome.

Okay, here's another question for you. What advice to give to founders in their first couple years of their journey to learn how they can build a resilient organization? Honestly, I think a lot of people, particularly first time founders, they come into this not knowing what the challenges are.

So what's your advice to new founders coming in?
>> Yeah, so we can even use dataPlor as an example. When we first started dataPlor, we were solving local business or big challenges for companies in Mexico. But we're more service oriented, right? And we had to learn from that and shift to more of a database driven business because that's what scales and that being a service business is really difficult.

And so I think for every founder out there, it's about failing and realizing that when you see all the shiny LinkedIn posts about raising money or disclose this, there's a lot of things underneath the surface that built that and a lot of failure, right? And so I really advocate our team for failure.

Fail first, it's okay because you're gonna learn from that immediately. So that's just kind of how I would say for every founder, it's just like it's okay to mess up and to fail. The key is just not to do it again and we're going to learn from it because I screw up every single day?

>> Okay, you're kind of like a service based business as opposed to product based.
>> Used to be more service based.
>> You used to be more so you've really turned that into more of a SAS like business. And the way you found that was because there's high entropy in the data.

They have to pay you every month, albeit upfront, but they have to pay you to have access to it. So you've turned what would be a service business into effectively a software business.
>> Correct, yeah. So we figured out how to manage people, how to collect the data, how to scale that data, even for a service company, sell it multiple times.

And we were like, actually, look at all these learnings, let's turn this into more of a SAS business. And that's what we did and turned it into a SAS play where it's just you pay upfront and you get the data and the margins are 95% and let other smaller companies worry about the services and doing one off things

>> All right, and then the issue of services is it becomes one off whereas now you have-
>> Yeah.
>> Right, so now let's think about the competition. You can't be the only person doing this. How do you stop the competition from competing? What strategy are you using to stay ahead of the competition?

>> Yeah, so one, the competition is so focused on the US market and so focused on having all of these other products that they haven't shifted their focus of getting the physical place in these markets. If you don't know that Jeff's Cafe exists in Mexico City, nothing else matters.

The polygons or the square footage, the mobility data, the firmographic, the demographic, the satellite imagery, everything doesn't matter unless you have this physical place. These companies, one, should they spin up some call bots and JSON calls and do some visual learning? Sure, they could do that and probably help their data.

But what we have is, due to being a services company at first and having over 100,000 people on our platform to start, we know how to manage data across the world by using very limited amount of human capital now. And that human capital is what makes the difference and what pushes us over the edge to always win.

And I never see the competition building out the full process that we have when they have ten competitors going against each other in the US. So we compete against billion dollar companies that sell international data every day and I don't go and say, hey, get rid of Dun and Bradstreet.

I don't say, hey, get rid of precisely. I say, hey, do a bake off, see what you think. You like data pillars data? Well, ingest it in with all of your other data. That's fine, I don't care what you do with it. And that makes the sales process a lot easier as well is where I'm not trying to sell against the competition, I'm just saying we're three times more accurate.

Take a look and add us to your data set. And if that means removing a competitor, great. If it means adding us as one of your vendors, great. Doesn't matter to me.
>> So, in effect, the competition came at this as a different problem and now institutionally, they're structurally very difficult for them to look at it from your perspective.

But you came at it from a data accuracy perspective from the get go and that's how everything is institutionally built. So let's come back to data entropy. How do you keep your database clean? Is it people? Is it an AI? Is it a small toothbrush?
>> Yeah, yeah, it's all made up.

We were just saying it's good, no so, we start with economies of scale, right? If we can figure out online that there was a recent published review on Tripadvisor, that's a great signal. That's free, right? So, we take as much free data as we can. And that's like our first step right?

If nothing's changing online that we've had, great. From there, we'll make phone calls, right? And just if someone answers, awesome. That's all we need, we can stop right there. It's good that cost a fraction of a penny. Okay, if not all right, we might have to actually use a human to make some phone calls and to figure it out.

But end of day we go from the cheapest to the most expensive.
>> Like funneling down before you get to people.
>> Like a reverse, yeah, Like reverse pyramid and at the very end is where we spend more money but it's like the last 1%.
>> I mean, this is one of the reasons why we love businesses that are leanly financed, you learn how to be very economical.

Like if capital had been incredibly easy in the first year or two, you would have spent it and you'd spend it the most obvious way. Lots of people, lots of things which don't scale. Instead you build a highly scalable business. Jeff, look, I wanna thank you very much for your time today.

I think this has been really interesting. I'm excited for us to be part of your journey, and excited for what I think you guys can do over the next few years.
>> Absolutely, thanks, John, for taking the time and Ohsumi. Thank you, Scott. And if anyone has any questions, feel free to reach out to Scott and John directly, happy to have one on ones and share more about dataPlor.

>> Thank you very much, cheers.
>> Cheers, guys.