Auren Hoffman (00:00.957) My guest today is Vin Vacanti. Vin is the co-founder and CEO of YipitData, an alternative intelligence data platform that analyze billions of consumer daily points daily for institutional investors and corporations. They reached unicorn status with a $475 million Series E from Carlyle in 2021. Vin, Welcome to World of DaaS.

Vin Vacanti (00:27.502) Thank you, Auren. I've been listening to many, many of the episodes, so I'm excited to finally participate.

Auren Hoffman (00:32.797) Oh, this is awesome. Well, you're a data nerd. So I'm really excited to have you here. Now, your clients are many of your clients are like some of the most sophisticated investors in the world. What are like some of these like hedge funds understand about data that maybe like a corporation hasn't yet figured out?

Vin Vacanti (00:53.902) Yeah, I think that's a really interesting question. Oftentimes the question behind that question is, well, why aren't so many more corporations adopting data the same way kind of hedge funds do? And I think it comes down to, it's not necessarily that hedge funds are smarter in using data. It's more that data in and of itself has no value. The real value is if it solves an actual business problem.

And what's unique about the hedge fund market is they actually have an opportunity every single day to make a business decision on a security that they buy more, do they sell more, do they hold, et cetera. So every day there's a decision to be made. And so for them, data can inform whether or not they should make that decision. Oftentimes people believe, well, if I have this interesting data corporations, of course they will use it. But my question is, well, hold on. How will that corporation generate more revenue?

as a result of using that data or how it make them much more efficient as a result of that data. And that's usually where people, well, I think it's valuable, et cetera. So I think it's less that hedge funds are more sophisticated in their usage of data. It's more that hedge funds have like a real business value from accessing that data in the way that corporations may not depending on which end market they are.

Auren Hoffman (02:15.117) So I would assume most companies, like a B2C company, can make decisions about pricing pretty often. Maybe back in the day, they'd make pricing decisions once a year, and now they can make pricing decisions at least daily or sometimes discriminate versus, know, Vin might get a different price than Orin or something like that. It seems like there's a lot of opportunities to do it there. There's a lot of opportunities to do it in marketing. There's a lot of opportunities to do it in labor.

There's lots of things where like there's dollars there, but yet like for whatever reason, it doesn't seem that people are using it at the same degree.

Vin Vacanti (02:52.586) I think that's a very fair point. think it comes down to what are the three most important things that take that example of a DTC company? What are the three most important things they're trying to do? Ultimately, it's usually in that example, it's marketing. They're trying to grow their user base versus like, you know, change their price by a dollar or down by a dollar. Now that can impact like the lifetime value and all those things, but far more important to them is like, how do I just get a much broader, you know,

consumer base for my product. And so it often does come down to like, where is their biggest business need?

Auren Hoffman (03:28.817) Yeah, yeah, yeah. Yeah, we had a, we were working with one of the biggest quick serve restaurant kind of chains in the world. And they, what they did is every, every week they would determine the store hours for every store in the world. And, you know, obviously the default would be to leave it the same. So if it was open at 7 a.m. and leave it the same.

But they would look at all the competitors, what they were doing. And maybe if a competitor was opening, they might move it to 630 to gain market share. They might move it to 730 to save money and get more profitable. They're constantly tweaking things like that. And then of course, labor came into place. Like maybe they had a labor shortage, maybe they had a labor gut, and they had ability to move things as well from the labor standpoint. So it's like a very sophisticated operation. And I probably met with at least like

30 other large firms and no one else was doing anything even near to that level of sophistication. This is like five years ago.

Vin Vacanti (04:35.426) Yeah, that's a really interesting point. Maybe I learned this actually on your podcast, but there's this notion that if you take any domain, any end market, there's like for every a hundred companies, there's going to be like a few that are like extremely data savvy that see that as how they win. But then the majority of them just don't. It's not how they see their competitive edge. They may be going crazy on the menu items themselves and like what kind of ingredients.

Auren Hoffman (05:01.595) Yeah.

Vin Vacanti (05:04.717) I think there's also an element of like, not every company will win just through massive like usage of data. Different companies will see themselves as having different strengths where maybe their marketing strength and they're all over that. Or maybe, like I said, it's like the food quality. Some of them are on the data side. So different people will have where they see us, like where they want to focus on it.

Auren Hoffman (05:24.677) True and hedge funds too, right? if you're Renaissance or you're 0.72 or something like that, you're going to be investing a lot in data. you're Citadel, if you're Berkshire Hathaway or something and you're just like having a very extremely long duration, maybe data has no value at all to you.

Vin Vacanti (05:48.334) Yeah, that's a great point because a lot of people are sometimes kind of make the point of like, I'm surprised that why isn't every hedge fund aggressively buying like every kind of possible data set that there are because there are some that are really aggressive there and see that as kind of their right to win, et cetera. And I think it comes down to two things, which is one, point we sort of just talked about, which is not every company sees data.

as the way they win, they may see it in other ways, other things that they do that's more unique to them. But I think the second was that, you know, when all this data became available, it became a real problem for the industry in some ways, opportunity and problem, because you felt like, do I need to use this data? And if I'm not using this data, am I at a competitive disadvantage relative to everyone else? And that's really where like our company came in and other companies like us came in. They said, you know what? It's okay.

We're going to go and collect all of that data, bring it under one roof. And then we're going to make sure you have the best, greatest conclusions from that data so that you can go back and do the things you are kind of good at. And you don't have to worry about building some crazy data team to take all of this data. And I think you've seen that all over the board in all industries. There are companies that have developed these great data assets.

But to get to the true long tail, often requires somebody else kind of doing all of the work for people and then serving up the full meal versus just providing the raw ingredients.

Auren Hoffman (07:17.711) And one thing, I guess there's like many different things like in the pipeline, if we kind of like walk through the pipeline of data, one thing in the pipeline, this might be like later on, is joining disparate data sets so you can ask like deeper questions of the data. Like, how do you think about that? And how do you think about these like different join keys to do it?

Vin Vacanti (07:42.782) super important. it's often the case that, you know, the customer, they don't care about the data. They care about solving the business problem that they have. And you're like, wow, I can't, I can solve part of it because I have one data set, but I don't have data. Like, I don't care. Like, get it. Like, and so our customers on the fun side, they want to know the most there is to know about Netflix or about Uber or Amazon or intuitive surgical, whatever the company may be. And it is our job.

to go get all relevant data sets, bring it all under one roof, and then bring it together and deliver our best insights on that company based on all the data that we are sort of observing. And I think it's one of the challenges that a lot of data businesses have had in terms of scaling their business is that if you're just providing one of those kind of raw inputs in, it's not that valuable to them because you're asking them to do a lot of

to take your raw input and combine it with a bunch of other raw inputs to ultimately get to some sort of insight. And so I think it's really important. Our perspective is we're not here to sell data. We're here to solve their business problems. And it's our job to make it work on them.

Auren Hoffman (08:54.521) And data itself is also like super messy. Like a lot of the data sets that you're buying or using, I assume have lots of errors in them. They have bugs. may be fraud in some of the pieces of the data that you have to root out. There's lots of mistakes in all the data sets. So even some of the best data has lots of mistakes in it and other types of things. how do you figure out like...

how do you figure out like, okay, we got to get as close to the truth as possible, but nothing like completely predicts the past. So how do you kind of figure all that out?

Vin Vacanti (09:34.104) Yes, great question. So the problem with the data that we work with, which is alternative data, is it's not intended for this. It's kind of exhaust data. And so it's extremely messy. And as a result of that, it takes a tremendous amount of effort to turn that data into something that is a reliable signal, insightful about a business. And so the problem we've always had as a company is we've always had data on millions of companies.

And the crime of our company is we would just throw all of that away and focus on just a few companies because the cost of processing that data for each company was so high because the strings you don't know how to match it to the right vendor to the right skew the data can be off there could be gaps in the data etc. So it worked for us to pick stocks because if we could really do a great job for uber

people would then pay us a lot of money for a subscription to just the Uber product. And so you can think of it as like, and I know you love charts, you can think of it as a chart where there's like a certain return on investment for each company. And if you do public tickers, the return is very high. The moment you go off of public tickers, the return gets really low. And so the ROI was never there for processing data for the millions of companies. And we only did like 500 companies, a thousand companies.

What's changed all of that is AI. And so now, using LLMs, you can start tagging this data at scale in a way that would have taken a human kind of in the loop mechanism to do that. That's what's changing about our company today is today we have the most, we are the source of truth for real time company performance data for a bunch of stocks.

If you want to know right now how is Uber doing Netflix, Amazon, there's no better place than us to provide that estimate. And that's why our business exists. We are on a mission to become that for every company, not just the public tickers, but we could have never done it had it not been for AI, lowering the cost of cleaning that data and making it reliable across millions of companies. So that's why we're kind of really excited about this next journey for

Auren Hoffman (11:53.639) What one place for data, is like somewhat interesting, but maybe isn't a massive market is like understanding my competitors, understanding the competitive set. And everyone made it find that differently. If you're Starbucks, it might be everyone who sells food and drink or something. So might be a very, very broad set for something like, you know,

Uber still could be pretty broad because obviously there's Lyft. There might be different transportation services. There's DoorDash or these other kinds of things that are out there. Maybe ultimately they even see like a Tesla as a competitor and stuff like that. But there hasn't been a lot of money in like understanding my competitors because it's somewhat nebulous thing. Like have you been able to like create like think of like products around that?

Vin Vacanti (12:43.052) Yes, great point. So you're exactly right. think anyone who thinks about data is like, my God, it's amazing. You're going to be able to see how you do versus all your competitors. But if you ever actually try to sell that to some money, they're like, okay, you know, I guess kind of interesting. I guess I lost a little share here, but it's because of this and this, and it doesn't really matter. Plus they already know what their competitors are doing because they talk to lots of people and all that stuff. So it's not something that people are willing to pay a tremendous amount for.

Auren Hoffman (12:55.868) Yeah.

Vin Vacanti (13:10.222) So it's kind of hard to build a business off of just straight up kind of competitive intelligence. That's a like thousands, maybe small tens of thousands kind of opportunity. And it's really hard to have like, it's a high churn business and all of that kind of stuff. That's why I kind of go back to this beginning where I kind of the example we give at the companies, we always talk about when Google earth came out, I remember it coming out and I thought it was the coolest thing ever. You were like a satellite. You could go to the Eiffel tower, your house and all this stuff.

But then at the round, literally the same time Google Maps came out, so much more boring. was just like a map or whatever. Guess how big Google Maps is versus Google Earth today. And that's because Google Earth doesn't solve a real problem. You don't actually need to be a satellite on a daily basis to go around and look at things. You need to get from place A to place B all the time. And so Google Maps is far bigger. So I always sort of talk to people when they're kind of building data businesses. Don't get caught building Google Earths.

Auren Hoffman (13:48.775) Yeah, yeah, it's crazy.

Auren Hoffman (14:07.737) something that because it's super cool and it's fun and it's sexy and

Vin Vacanti (14:12.108) Yeah, data is cruel. The thing I always love is people always put data on a map. Look at all this data on map. And I'm like, so what? What am I supposed to do with this map? And so instead, go find an actual business problem that they have that this data can go solve. And that's going to cause them to put real dollars in their pocket as a result of using that data. And so that's why like competitive intelligence is not a great market, because it doesn't actually put dollars in their pockets.

Auren Hoffman (14:15.473) Yeah.

Auren Hoffman (14:19.1) Yeah.

Auren Hoffman (14:39.869) It's also not sure, like, what do do with it? It's not as actionable to...

Vin Vacanti (14:43.668) Exactly, the actionable. Do they take a business decision the very next day because of what you're putting in front

Auren Hoffman (14:51.067) And do you agree the most valuable data is the temporal data, data that's changing?

Vin Vacanti (14:59.47) no. I mean, there is data out there that is extremely valuable. That is historic. That is largely historical data. so for example, take, zoom info, having contact information for all of these business professionals is extremely sort of valuable. doesn't like

The fact that one week later, how much has it really kind of changed? Just a little bit. But it's the, but like the corpus of that is very valuable. A lot of this stuff, think about like AI training, like in Reddit data and how valuable all of that corpus of Reddit data is. The fact that the very next month, some new information comes out is not as kind of valuable to it. On the other hand.

Auren Hoffman (15:46.909) But do you think it's really that valuable? mean, Google paid a lot of money for the reddit data for two years. I think it was, let's say, $60 million. And they got it for two years. When that ends, do you think they're still going to pay $60 million for it? Or is it going to be like $6 million for it?

Vin Vacanti (16:06.828) Yeah, it comes down to the value. The value, there was a tremendous value in the historical data in training these models. There is some value because the world changes, new products, new opinions, new things. But you're right that it doesn't feel like that month of data is worth as much as like the historical data was worth. And so from that perspective, I agree with you. Now, for us, a lot of people look at our data, our data's value isn't the history of the data.

Auren Hoffman (16:17.309) For sure.

Vin Vacanti (16:36.694) our data's value in the fact that we're going to see it next month. Because its company performance data changes dramatically. Everyone wants to know what's going to happen next month, not what happened in the past. And so for us, the temporal data is very valuable from that perspective.

Auren Hoffman (16:53.529) Some of the data that you get and lots of other people get are crawling sites. There's cat and mouse game of crawling and there's CloudFlare and there's all these anti-scraping, anti-crawling. How do think that converges over time?

Vin Vacanti (17:11.0) You know, I used to stress out about that a lot because we originally started, we don't do, it's a smaller part of our business today, but we started by collecting data from the public web. you know,

Auren Hoffman (17:20.955) Yeah. And crawling like eBay and Amazon getting priced like those types of things.

Vin Vacanti (17:26.38) And I used to really worry that those companies were going to make it harder and harder for people to sort of access them. The reality is that there's so much business value in collecting that public web data that as hard as they try to make it on that side, there are companies that have been created that are just as entrepreneurial and just as innovative to help other organizations make sure they can go get that data. So there is this thing where like the harder they make it, the

Auren Hoffman (17:47.654) Yeah.

Vin Vacanti (17:55.01) more of a business opportunity there is for people to go help other organizations. we're not, know, like, it's like millions of companies are collecting kind of public web data.

Auren Hoffman (18:01.851) Have you heard of this company string by the way? yeah. They're, they're like, they're like an amazing crawl company and they're just like, they're just, they get so in the weeds of just that one problem. Yeah.

Vin Vacanti (18:05.976) No, I have not.

Vin Vacanti (18:14.958) Exactly. that look, if there's business need and you and you and it's legally not an issue to collect that data, then ultimately someone will the companies on the other side will also rise up and make that kind of a possible.

Auren Hoffman (18:30.641) Now, most of the really big, well-known data companies were started over 40 years ago. have, you know, the Morningstar, the Bloombergs, the Standard & Poor's, you know, all these companies that are out there. And in some ways, like you're competing with many of them, maybe all of those ones I mentioned. How do you think about those companies?

Vin Vacanti (18:54.306) Yeah, of course. So one of my favorite things to say about data businesses is if you look at a bunch of businesses 120 years ago and are they around today, you're going to find that data businesses far outperform every other category of business that is still around.

Auren Hoffman (19:08.699) Yeah, I'm sorry. mean, Dun & Bradstreet, right? Abraham Lincoln worked for Dun & Bradstreet, right? okay. I'm sorry. Like that's, that's insane. Right? Like.

Vin Vacanti (19:13.228) Yes, you took my quote. I was about to say that. That's my favorite quote. How crazy is it that you're like one of your alumni is done it like also Moody's was a part of that in Bradstreet. You know that got kind of spun off in 2000, etc. So like these, you know, standard and poor's has been around for forever. Obviously Reuters, etc. So these businesses are around forever. Now what was fortunate for? Yes, what was very fortunate for us was.

Auren Hoffman (19:24.725) I didn't realize that. Okay. Yeah. Yeah.

Auren Hoffman (19:30.139) Yeah.

Auren Hoffman (19:35.527) They're very Lindy, essentially, right?

Vin Vacanti (19:41.804) that when we started Yipit Data, this didn't seem like a big market, alternative data. We were doing web scraping and we were just selling specific tickers to investment funds, et cetera. So it kind of became, it was kind of like a little snowball and it kind of started rolling downhill and getting bigger and bigger. And so now we just, we have a bunch of data. We've built our own panels. We have these exclusive relationships with a lot of other companies, cetera. So now it's not so easy.

for these bigger companies to just launch. And none of them have. There's a very big, barred entry to this space. But it's also, while it is huge, it's not so huge, because these companies are so big that for them, makes a big, they need huge revenue to move the needle for them. So we've nicely been in that slot of under the radar, but keep getting bigger, keep getting bigger. And so now it's too late.

for them to kind of enter the space because there's just too much that we have done over the last 15 years.

Auren Hoffman (20:42.971) And if you think these companies like SMP, Dow Jones, Reuters, they've really been built by &A. They're very good about buying companies for X times EBIT. And because of the efficiencies, can make the company more efficient. They can also stuff the channel. They've got great salespeople.

you know, even though they're buying an X and the X might be high, like they can grow that EBIT really fast on both sides of the, the, of the, operation. Have you thought about using and A as a lever now that you're, you're starting to be at scale?

Vin Vacanti (21:21.762) Yeah, for sure. Well, you on that point, I think the part that people sometimes don't give them credit for, it's not just about efficiency. It's not just about the distribution. It's actually a lot about the data that they acquire. They get to integrate that data in a bunch of products that they have that make those products better. And that's, in some ways, that is the real reason that acquisition makes so much sense.

Auren Hoffman (21:39.184) Yup, yup.

Make the products better, yep.

So every time I buy a company, it's making their other products better as well.

Vin Vacanti (21:52.054) Yeah, data businesses are like that, which is it's really about getting more data into the organization and then improving as many products as you can, which gives you more customers, more breadth. And then you go around and you buy other companies. You know, we acquired one of our largest partners back in 2021, which is this business Edison. And that was great for us because it kind of gave us the ability to work more closely with them, you know, and build out that panel and collect.

you know, be able to parse the data better and all of those types of things. And so we're always, you know, interested in potential kind of acquisitions, but it has to make sense in the sense of, is it going to make our existing products better versus just another company where we're going to try to, you know, drive it through our sales platform and those types of strategies.

Auren Hoffman (22:41.181) It still seems that data is not the most understood of business models. It's not like everyone understands SaaS and all the different metrics around SaaS or something like that. When you talk to investors who are buying data businesses or thinking about investing in data businesses, what do they most misunderstand?

Vin Vacanti (23:02.414) Yes, I have a huge gripe about this and I think they misunderstand three things. First, this snowball effect, which is as the data asset gets bigger, your ability to improve all the products, which brings more customers, which funnels it right back into the data. so data business historically don't grow that fast. They're not the company that's going to grow 200%, 300%, but they're consistent.

Auren Hoffman (23:17.116) Yep.

Vin Vacanti (23:28.376) They just keep growing. And then the barrier to entry on those data businesses are very strong. The second thing is when you think about AI, software is actually now getting really hit by this notion that if you think about a business, there's data, there's processing, and then there's distribution. And AI is really starting to chip away at the barrier to entry on the processing of data, which is for us an opportunity because it allows us to make our data more.

Auren Hoffman (23:52.253) for sure.

Vin Vacanti (23:58.026) actionable, like what we were talking about with the using AI. And so that's the second thing I feel like investors don't understand about data businesses is they're actually very AI resilient, because the AI models are only as good as the data that they have access to. And if you're a data business, then you're sitting on data that is as long as it's not available in the public domain, it's hard for an open AI or an anthropic to kind of replicate your business, hard for a three person company to just use

know, LLMs to create workflow solutions, et cetera. The third is around costs. You can, when you think about the accounting system that was created, which is Gap and all that stuff, the way it kind of works is there's this concept of gross profit. And it's like, you sell a good for $10 and then what did it cost you to kind of produce that good? Let's say it cost you $5 to make that shirt. And so your gross profit is $5.

And a lot of people want to go, what's the gross margin and how's the gross margin scaling and all of those things. Cause it's almost like a proxy for EBIT for like long-term EBITDA margins. But it's also a sense of like, what is this a good unit economic business, et cetera. The thing about data businesses, which is unique is the cogs don't grow as the revenue grows. And so you actually spend a ton of money creating like the data product, but then the incremental customer, all of that falls to the bottom line.

And so when you look at, can have data businesses when you first start that have negative gross profits because they have all this fixed cost in producing their data, but that gross margin will scale amazingly well. And so early on in our, our kind of existence, our gross profit wasn't that great today. It's gotten so much better because of this scaling factor. But then if we go and get a new data set, it bumps it back down. And then we have to kind of scale that to kind of get it back to being a great place.

And I feel like investors are just like, they don't get that notion that there's a fixed cost.

Auren Hoffman (25:56.423) Well, also it just depends on like where, should you put it? Right. So I remember having this like big argument with like one of our auditors, like they wanted to put all this stuff in cogs. I'm like, okay, well it's not a, I'm like, it's not cogs. because, you know, if I was, if I had a SaaS company and developing software, you wouldn't put it in cogs. it's an asset that I'm building that I'm selling many, many times. Right. You just put that in my general operations of my business. and just where, like you, so it's either.

I think you can make the case that everything is right about data businesses, but everything is wrong about SaaS businesses because they should be putting more things in COGS or the opposite, right? Because all their assets that they're building, they're building this amazing workflow, but none of it, their gross margins are 90%, quote unquote. Whereas the data business, the same thing, they're building this amazing data set, but they have to put that in COGS for some reason.

Vin Vacanti (26:52.982) Yeah. So my advice always to people in the data business is really focus on this. Get it right. Have your story right around what is a fixed cost. Show the unit economics of an incremental customer and how much falls to the bottom line. Because the gross profit for the data businesses, COGs are sometimes fixed. Most time variable for data businesses are almost entirely fixed. And so like you should show how the gross profit like

can increase dramatically as the number of customers.

Auren Hoffman (27:24.613) Yeah, yeah, yeah. Yeah. That makes a lot of sense. know, the some, a lot of data companies do struggle to scale at a certain point. Like their growth basically gets to flat at some point. And this by the way, it's true in software companies too. A lot of software companies, there's a certain point where they just struggle. They hit their TAM. What should companies do at that point?

Vin Vacanti (27:49.75) Yes, three things. First, they have to be able to find new end markets for their data. They can't get caught just selling to one end market, and that's hard. It's hard to go and source a completely new end market and figure out how to retrofit your data to kind of work for them.

Auren Hoffman (28:05.199) Especially because the, and the, probably the first end market is the one that would pay the most for that data. Right. If you're selling into hedge funds or whatever, like that's the probably the most that they're like, they like that day is probably the most. then like the next market you do, like they're going to, they might not want to pay as much for the data.

Vin Vacanti (28:23.67) And look, it depends. If the data is new, if it hasn't existed before, it's possible that there is a far bigger market that you're not aware of. Because the issue with the hedge fund market is there aren't 20,000 hedge funds to kind of go sell to. But in these other markets, there are tens and twenties and 30,000 companies to kind of go sell to. So the first is in a market. The second is you should really talk to your customers and say, OK, you're using our data for what? Because like I said, no one wants data.

Auren Hoffman (28:31.517) That's true. Yep.

Auren Hoffman (28:38.31) Yep.

Vin Vacanti (28:53.89) They want a solution to their problem. And so for instance, I'm going to give a stupid example, but maybe they're using the data for location selection. So then you should try to understand, what are they doing exactly once they get our data? And can I do that part too? And if I can do that part too,

Auren Hoffman (29:08.581) Yeah. And they might be marrying it with three other data sets. They might be doing this other thing. They might be providing some sort of data science on top of it, et cetera.

Vin Vacanti (29:17.688) And then, nope, I find you go up to stack, you get 10 times more money. If you keep going up to stack. And then the third thing is like, a lot of people are like, wow, I can only have this one data set. You know, I'm not going to use any other data set. This is what I sort of specialize in. And I think that's a mistake because ultimately that's the other way to not get caught is start adding incremental data sets that when combined with your data set, make the product better. And then some of those data sets can launch completely new end markets.

And I think people sometimes get caught thinking like, I'm only ever going to do this one data set. Start looking for tangential data sets to marry to your existing data set.

Auren Hoffman (29:54.651) Now on the AI side, most of our companies in the data world, AI has significantly reduced their costs of creating the data. And you were mentioning before that has allowed you to go much longer tail on the number of companies that you, but I would say at least in the least so far, the, these companies have not yet passed that cost onto their customer. So for most of our data companies, they're

you know, their, their, their EBIT has grown, their margins have grown, et cetera. but these are competitive markets with probably many competitors who are also investing in AI. How do you think that changes over time? Like, are we going to start to see some price compression over time with some of these data companies? If the cost of the creating that data does get cheaper over time, or are they going to try to layer more services to keep the price where it's at? Or how do think that kind of changes?

Vin Vacanti (30:54.254) Well, no one's going to lower their own price unless they're doing it to attract a new logo from a competitor. And so some of that was definitely going to happen. But I think on the AI side, a lot of people are very focused on the efficiency side. But the far bigger opportunity is the ability to develop new products using AI that historically you could not. Because that is,

Auren Hoffman (31:02.503) Correct, yeah, yep.

Vin Vacanti (31:21.698) Well, an EBITDA margin percentage, the rule of 40 is you add revenue growth to EBITDA margin. The reality is the market doesn't value those equally. You'd much rather have a...

Auren Hoffman (31:29.787) Yeah. The growth is at least three X the other side. Right. Yeah.

Vin Vacanti (31:34.862) Exactly. so the advice I always give to people with these data businesses is, yes, there's efficiency there, but think of like, how does AI, by lowering the cost to process this data, what else could you be processing with this data that would allow you to develop completely new products?

Auren Hoffman (31:50.981) price is a weapon. mean, you can grow your business if you're selling the same thing as somebody else. mean, obviously, this is hard to do an apples to apples comparison on data, but if you were selling the same thing at half the cost, it would be a lot easier to sell it, right?

Vin Vacanti (32:06.894) It would be, I think the bigger opportunity there is less about selling it to the existing customer base, obviously at a cheaper price to win share more by lowering the cost to produce it. Does it open up a completely new market that historically would have never paid you, you know, 30,000 a year, but they'll pay you 5,000. And then all of a sudden now you've got like 100 X the potential customers to go after at a much lower price.

Auren Hoffman (32:20.571) A new market, yep.

Auren Hoffman (32:35.101) One kind of like kind of a data business, I always think is interesting. I'd love to get your thoughts on is the expert network business. I'm sure you've thought about that a lot over the years. Like how do think that's evolving?

Vin Vacanti (32:49.208) So the expert network business, amazing business that they created, extremely profitable business that has a network effect in terms of you build out this network of experts and then funds, but corporations, they need expertise. And it's really painful. And they reach out to you, you find them an expert. It's already in the network. You kind of build them. You get a cut of that, et cetera. That makes a ton of sense. And then it got very competitive.

Because anyone could build an expert network in some ways, because I'll find you experts, because a lot of it is just hunting people down, et cetera, on LinkedIn, et cetera. But then Tegas showed up. And Tegas, was genius what they did, which is they said, you know what? We're going to do the calls at cost, because the real value is, we convince you to allow us to publish the transcripts? And that was super interesting, because all of a sudden, people were like, sure, I'll do that. And then they built up the library.

Auren Hoffman (33:46.333) Again, they use price as a weapon, right? Yeah.

Vin Vacanti (33:48.974) But they used a different business model. It's a disruptive technique, which is you just change the business model. And it was priced, but was really there was another. They started to build a network of data as a result of that. And I think what's interesting is in this world of AI, AI is only as good as the data has access to. And you can kind of assume AI will get access to anything that's in the public domain.

Auren Hoffman (33:51.581) Correct, yeah.

Vin Vacanti (34:15.042) These expert networks are actually sitting on an interesting opportunity because they have data that's not accessible to AI, which is all these calls and all this expertise that is getting poured through. And I think that is an interesting angle in terms of creating unique proprietary data. And so when you look at some of these large financial services companies, they're starting to be like, uh-oh, what's our unique proprietary data? And I think expert networks are sitting on an interesting.

potential unique proprietary data set if they can convince their customers to allow them to kind of, you know, start publishing these transcripts.

Auren Hoffman (34:52.593) What one interesting data set that people have talked a lot about, you know a lot about is credit card and debit card data. Outside the US, what other markets have robust credit card and debit card data?

Vin Vacanti (35:07.886) I think you have some stuff in Europe. I think there's some stuff in APAC, but it's hard. It's hard. The real challenge is there isn't as big a market need as you would think. Because when you look at like the public tickers, which is hedge funds really drive the majority of the demand for credit card panels, most of the investable companies are in the United States. There's not that many in these other regions. And the cost

Auren Hoffman (35:35.601) Wouldn't you want to know like how they're doing versus, you know, and some of them are global. mean, if you think of like Netflix, like they have global, you know, they're or Uber or Starbucks or, you just go down the list. Like, wouldn't you want to know how they're doing in these other markets?

Vin Vacanti (35:52.216) for sure, but the cost of building out that panel, it may be a little cheaper, but not that much cheaper to do it in these other regions. It's still pretty painful. And so then you have to say, what's the ROI? If the ROI, if there were a bunch of publicly traded companies in those regions, then it would be interesting. if it's just, it's one of the 74 markets that Netflix is in, like, you know, it doesn't really move the needle to know that one.

Auren Hoffman (35:56.177) Yeah.

Auren Hoffman (36:03.536) Yeah.

Auren Hoffman (36:19.325) But then why does the US move, if the US is 30 % of the Netflix revenues or somewhere in that range or something like that, why does the credit card data even matter that much if it's fluctuating, if it's just US? Whereas for Lyft, the US market might be 98 % of their data, their revenues or something like, maybe that makes more sense if the credit card data is actually indicative of their performance.

Vin Vacanti (36:49.358) I don't think it does matter that much for Netflix. I think you got to have like a global perspective. I think the real thing with Netflix is like a lot of people use Netflix. And so really what it comes down to is like, how are they upselling the Netflix customer bases? Like how are price increases being handled? You how's the gaming stuff going? It's much more like a competitive kind of thing. It's less about predicting like in the U S the exact top line. It helps a little bit. And there's still some interest cause it can kind of.

Auren Hoffman (36:51.335) Okay.

Auren Hoffman (37:05.851) Yep.

Vin Vacanti (37:17.902) you if they fall all of a sudden, they stop growing, they decline, those are noticeable things, that's news. But you're right, it's not as valuable.

Auren Hoffman (37:26.205) Yeah, interesting. Now, I remember you telling me a story of how you famously taught yourself to code after you had some outsourced developers back in the day that didn't work out. will link to it in the show notes. You wrote extensively about this. Nowadays, we have all these AI coding tools that are out there. You've got

You've got great UI coding tools like Bolt.New or Replet or Versel and stuff. I imagine it's even easier to get from zero to one on coding today. What would be your advice to non-technical founders?

Vin Vacanti (38:10.208) so you're exactly right. basically it's, it's been getting easier every year. Like AWS, when I first started, I didn't even have access to AWS. Like I was doing like a server on one and one, and it was just like a disaster trying to manage that. I had no idea what I was doing. If like, I remember there was.

Auren Hoffman (38:15.953) Yeah, every year. Yeah.

Auren Hoffman (38:25.723) Yeah. Even just putting an environment on your laptop was like, I mean, even just a few years ago was super hard to do.

Vin Vacanti (38:33.474) Yeah, I remember we had these servers running our website at the time and I had one, if the server went down, I had one command that I would run that would boot it back up. If that command didn't work, I was like, well, it's over because I literally don't know anything else to do if this one thing doesn't work. So it was way harder back then, but now like, you know, like putting something in the app store, like if that was painful, but now that's gotten easier through all of this type of stuff.

Auren Hoffman (38:58.034) Yep.

Vin Vacanti (39:00.13) So I would say, so some people say, well, I don't need to learn to code at all. And I'm not sure about that because the problem is it's great for building like a simple prototype. But what you'll find is as the prototype gets a little bit more sophisticated and then you actually start having real people using it, you go to change the thing and like the whole thing breaks and you're like, whoops, what happened? And then you, then you like undo it and then you go to change it again. It breaks again. So it is important that I think you have an decent understanding of what's going on.

but it makes it so much better in terms of not having to know all the crazy syntax and a lot of like weird stuff, et cetera. But there's also like some security issues. If you put something out this way, like it's probably you've exposed your whole database and you have no idea, et cetera. And so I think it is important that you still kind of learn like the technical basics to this, but I think it's critical for companies where like the product is really the thing, right? If you're like,

Auren Hoffman (39:38.523) Yeah, yeah.

Hopefully.

Vin Vacanti (39:57.966) If you were a Groupon, you didn't need to learn to code. Like you can build a little website that can collect people's payments and that's like very straightforward. It was really a sales and marketing challenge. But if you're building a product for consumers or a workflow product for businesses, I think you got to build that yourself as a founder, because if you're not, and if you're just outsourcing that, it's never going to work because you need to be iterating like on an hourly basis. It's like, oh, I want to change this. It can't be, okay, let me reach out to my developer.

and they're gonna come back in a month and change it, you need to be able to type it in and see it reflected in like the next like 30 minutes.

Auren Hoffman (40:32.412) Yep. Yep. And even what I found is like, it used to be, it used to be, we're talking like a little over a year ago, if you're like a product manager at a company, you would like mock something up and you'd kind of like take it to some engineers. And then they would kind of like, you'd work with them on like a V1 or something. And then you'd, you'd get like a somewhat workable demo and then you talk about it internally or something. Nowadays, like the product manager does all that themselves.

Like they, they, they can literally get to at least the demo. No, it's not hardened. doesn't have the security. It's got tons of other issues, et cetera. But like they can literally get to, like, like a super visual, very beautiful solution, like right off the bat. And then they can go to the engineers. Like I say, Hey, make this better. And so that product manager has so much more, in some ways they're like more powerful than they've ever were before.

Vin Vacanti (41:30.232) But I think, know, it's funny you said, I think that's true everywhere. The engineer is a lot more powerful than they were before. And so.

Auren Hoffman (41:32.925) Yeah, that's right.

Right. That's true. Yeah. Cause also I found like a lot of engineers I know, like I'm most of the engineers I know are more like backend engineers. and they never could do front end and front end was like magic or something. And so they're like, I don't do that stuff. And now they're like, I can do it. Like they just like, they just like get it done like immediately and stuff. And so you're right there. Each person now has so much that they can do more themselves than they could do before.

Vin Vacanti (41:49.015) I know.

Vin Vacanti (42:03.34) Yeah, that's exactly right. And I think, look, it's going to keep going. I also think it's why it's such an exciting time for entrepreneurs, because the hardest thing as an entrepreneur is like the why now. Like, you know, like why now? Because you're also just doing the same thing everyone else has done. And there's such a great why now. And companies, blank AI, will try it. Like, it doesn't matter what it is. Like, people are like, people are like afraid not to try it, because if they don't, they get left behind.

Auren Hoffman (42:24.763) Yep, totally.

Vin Vacanti (42:32.0) Now, of course, there will be retention issues with all these companies that go from zero to 100 and back down to 10 after three years. Yeah, so people will try anything, but then are they going to really retain the product afterwards is like the real question.

Auren Hoffman (42:37.019) Yeah, we're seeing that in our companies as well.

Auren Hoffman (42:45.239) One of the things that's happening, think, at least in the market of like, whenever you see these high growth things that are happening, at least for our companies, they're growing so fast, that almost always that means there's like four amazing competitors are also growing so fast. And then their products are also amazing and also getting better on a weekly basis. And so, you know, the customer might be moving between all these because

If they made a decision to buy you six months ago, that probably was the right decision six months ago. That may not be the right decision today, given like how much these products have diverged over time.

Vin Vacanti (43:24.45) And I think the other challenge is, like, what's the moat that they're building when they, a customer starts using them, how easy it is for that customer to just switch to the next hot company that comes along. And they, and like, it's hard to build a moat when you're going from zero to a hundred, because you're just kind of like, you know, you're not integrating deeply into their environment and there's no sales process that kind of someone else has to now go through, et cetera. And so like, that's the thing I would definitely encourage is like, get very aggressive with.

the building out of moat for the business.

Auren Hoffman (43:58.993) Now, now one of your biggest shareholders is now Carlisle. What have you learned from them?

Vin Vacanti (44:05.324) Yeah, for sure. Honestly, we were, we were traditionally, we were like a VC back company until Norwest invested in 2019 and then Carlisle invested in 2021. And we've learned a tremendous amount from both of them, which is about running a great business. because, know, previously with VCs, it's very much just, you know, you want a 10 X, you want to get as big as possible, as fast as possible. And they really kind of came in and told us like the power.

of running like an efficient business. And the reason why that's so powerful is when you run a business efficiently, that means you have extra cash that comes from a customer sort of signing up. And what can you do with that cash? You can self invest that cash into the business, which for us meant let's go sign more data partnerships, let's grow our panels, et cetera. And it kind of created like a much more accountability around like how efficient we were being. I remember when Northwest came in, we were spending all this money on AWS.

And they were like, hey, you guys should be more profitable than this. And then we were like, OK. And then we looked at our AWS thing. And then three months later, we had cut it in half just through efficiency. And there's a lot of businesses right now that are listening to this podcast. And they've never once really tried to make their cloud cost more efficient. And if they did, they would instantly find just free money coming back to them. It's actually one of the reasons, one of the products that we built.

Auren Hoffman (45:12.466) Wow.

Vin Vacanti (45:31.47) kind of for our own internal purposes was this notion that we had all this software subscriptions. And it costs a lot of money. It was very hard to kind of manage all of it. We didn't even know everything that we were buying because the ERPs don't organize it nicely by vendor, et cetera, what you're spending. And so we built this product internally that went through our entire ERP and then figured out all of the different software that we were buying and created a nice web application where it listed them all with renewal dates and all this stuff.

Auren Hoffman (45:58.525) Mm-hmm.

Vin Vacanti (46:00.258) And we were like, wow, this is so nice. And then we thought, huh, maybe other companies would like something like this too. And then we were like, well, but we're not going to charge for the business models. If we can get enough companies to do it, then we have a data set of what all these different vendors are doing that. Yeah, and so we spun it off from our, it's still in the company, obviously, but it's a new product called Spend Hound. And we have almost 1,000 companies now that use this product to help them organize.

Auren Hoffman (46:13.629) Yep. Yeah, it's a co-op.

Auren Hoffman (46:24.985) I love it.

Vin Vacanti (46:29.983) their expenses better. And now we even have like a procurement expert team.

Auren Hoffman (46:33.437) I actually created a business that did exactly this with like QuickBooks. Back in the day, we sold it to G2, but it was the exact same idea.

Vin Vacanti (46:40.061) wow.

Vin Vacanti (46:44.726) Yeah. And so, so yeah, so now, you know, that's been awesome, because we now have visibility into the B2B side. So we can tell you which accounting firms are doing well, which software companies are doing well. We found the ERP data a disaster. I don't know if you remember this, because people just type in things and you don't know what is it referencing. That's where the LLM is coming. Because now we can have the AI system.

Auren Hoffman (46:53.757) Amazing.

Auren Hoffman (47:01.835) yeah, so hard to normalize. Yeah, it's so tough. Yes.

You can go much deeper. You don't have to just say, just do it for like a specific vendor.

Vin Vacanti (47:15.758) Exactly. And so that's what we're working on right now, which is why I sort of made this point of we are the source of truth for how Uber and Netflix are doing right now, but our long term mission is to be the source of truth for every.

Auren Hoffman (47:30.887) What other things internally are you tracking that you don't think most other companies track?

Like if I, if I go into Vin's dashboard, are there particular things that you care deeply about that maybe, you know, the average company doesn't.

Vin Vacanti (47:49.454) Well, I think every company is unique in terms of what is the key thing, the key bottleneck to their current growth. And I believe very much you manage what you measure. And so we have this concept, our company of scoreboards, which is every initiative there's a scoreboard and the scoreboard is something that tells you, you winning or are you losing within one second of looking at it? So it's always where we are, what the target is. Are we behind it? Red.

Auren Hoffman (48:13.661) Okay.

Vin Vacanti (48:18.668) Are we above it green? And it's like, what is the North Star for that initiative? And then we have that, we have some sub stats doing that. Like we have scoreboards all over the place at the company. So everyone knows, are we winning or are we losing by how much? And we also have forecasts for like the next three months. And it's always our best guess as to what it will be. And then is it, we winning or losing? And then we send it out every week. So every week it says, what did it, how did the forecast change? Did it go down or did it go up since the last time? And it's like,

Auren Hoffman (48:28.477) That's awesome.

Auren Hoffman (48:42.717) Mmm, just like a delta.

Vin Vacanti (48:46.978) That has been awesome in terms of that's like the dashboard that we have at our company. And so that's how we know whether or not we're winning or losing across all of our initiatives.

Auren Hoffman (48:55.805) How do you think about people? Because coordinating lots of people is really hard. have this kind of like, at the best case scenario, you've got maybe an end communication problem, maybe worst case scenario, an unsquared communication problem. It makes things, you know, the more people have, just things just by default move slower in a company. So how do you think about actually the employees? you trying to...

keep the number down so you can move quickly? Are you trying to silo them? Are you trying to two pizza rule it? Like, how are you thinking about like organizationally?

Vin Vacanti (49:30.616) So we definitely try to two pizza roll it, which is right. Like people, you can feed everyone with two pizzas. We try, we really try to break stuff up into components that work like in their own way. And then we give them as much ownership and autonomy as they, as they need to do what they do. What we don't want is like, here's 150 engineers and then 20 product managers and all stuff, all working on some like.

Big big project because that stuff like really falls apart. It's like really hard to that. Well, so even that you try to then create like API. So this team is owning this and they have an API that they sort of integrate with another team and then they have an API and so an API can be an actual API or like kind of like a you know metaphorical API. But we think it's that way. It works for us is to just try to break people up into a smaller group as is reasonable so that they don't.

need to coordinate with endless, you know, all these other.

Auren Hoffman (50:30.203) Yeah, okay, that makes sense. And now that you have like all these like sophisticated people on your board and stuff, like how do you effective, how do you run effective board meetings?

Vin Vacanti (50:39.906) Yes, very hard. It's very hard, but we do. I sort of see the board meeting as an opportunity for three things. First, it's really for the team itself, which is every quarter, it's like we have to stop and think about our business from an analytical perspective and not just kind of like, you you kind of pull yourself back a little bit and you're like, okay, well, what's working? What's not working?

is the strategy, right? All of that kind of stuff. So 90 % of the value of the board meeting is actually forcing us to analyze our own business. Now, and then in the actual board meeting, there's two things we're hoping to accomplish. One is alignment with the board. So get everyone on the same page. Like they get it. They understand what is happening with the company. And they also are aligned with where the company is heading. And then the second is feedback because the benefit of the board is they've seen so many other companies.

Auren Hoffman (51:11.771) the prep. Yeah.

Vin Vacanti (51:35.138) And so they can, have like, have another company that did this. And like, for instance, it just talking to you, you're like, in some ways, like you have this interesting position because you've seen all these data businesses and you're like, uh-oh, like you need to do this because my other company X and this other domain had the same problem. And you should sort of, I would be focused on that. Now every company is unique. So it doesn't always translate one to one, but I think that's like the value of, having so many observation points across so many other.

Auren Hoffman (51:48.669) Yeah.

Vin Vacanti (52:04.962) companies that you should be able to bring in stories from other people. Go to market product expansion, AI usage to all the other companies.

Auren Hoffman (52:14.429) Two more questions we ask all of our guests. The first one is what is the conspiracy theory that you believe?

Vin Vacanti (52:20.97) Okay, very hard question. would say I generally, I know enough to say I don't know what I don't know. And when I look at all the conspiracy theories, I don't know enough about like, I'm not in the inside. I don't have those details to know whether it's true or not true. But I would say generally speaking, I am not, I don't believe in almost any of them because I really believe in like the Benjamin Franklin quote.

which is if you want three people to keep a secret, two of them have to be dead. And in my mind, these conspiracy theories often involve so many people to make them kind of plausible. And so in my mind is like, how in the world were they able to keep this thing secret for so long? Someone would have spilled their guts in one way or another. And so that's why I generally am not as big a believer, but I also, you know, I'm...

I don't know these things. I wasn't involved in the JFK assassination, so I don't know what happened and who was involved and who wasn't. What intel do I have to have an opinion?

Auren Hoffman (53:20.189) That's what someone involved would say. They would say they weren't involved in it.

Vin Vacanti (53:22.908) Yes!

Auren Hoffman (53:26.961) all right. Last question. asked all of our guests, what conventional wisdom or advice do you think is generally bad advice?

Vin Vacanti (53:34.799) Vin Vacanti (53:38.53) The one that comes to mind is a lot of people say you should be really focused when you're working on a business. Like don't go do a ton of things. Like stay very focused and do that one thing very well. And I think that there's a nuance to it, which is I believe you have to be at the right place at the right time. And the only way I know how to be at the right place at the right time is to be at lots of places at lots of times. And so we're big believers in a

in like the things that are really scaling, we're very focused and we continue to deliver that and deliver that really well. But we're big believers in having a bunch of bets, small little bets that we are making here and there, many of which just die right there. And then we get a little customer calls, whoops, that's not good, that's not good, that's not good. But those little bets are how Yipit.com, the daily deal aggregator became a 700 person Yipit data business.

Auren Hoffman (54:34.545) Yes.

Vin Vacanti (54:35.426) was because it was one little bet that we were making that we weren't even sure if it was going to work. And so, you know, if you look at our history of a business, you know, the business has grown really well. But if you actually look at all the things we've done, basically one little bet every year that did 90 % of the work. And then the other little bets all died, all died. And the only way I know how to do that is to have lots of little bets. And I know people say, you should be very food.

Auren Hoffman (54:37.635) One little bit after another. Yeah.

Auren Hoffman (54:54.961) Yeah, it's very tail oriented. Yeah.

Vin Vacanti (55:03.79) because you should be very focused. I'll take the second one, is people say hire great people and get out of the way. I do not believe that at all from a conventional wisdom perspective. I remember I read this post by this guy, Dave Kellogg, and he talked about how there's a thousand things any company can be doing right now. The reality is 997 don't matter. There's about three things that actually matter for that company. And I believe when you're kind of running a business,

you should know what those three things are and you should guarantee those three things go well. And if that means you have to go seven layers down and talk to this person who's working on that project and guarantee that thing goes well, you just have to do it because no one in the business has the context or the authority that the CEO founder has over that business. And so they are the only ones that can go in there and completely change the game on that initiative versus hearing about it six months later and knowing it didn't work and you're like,

Why didn't we change the game back then? Of course they didn't change the game. Who were they to change the game? Et cetera. And so they didn't know what you know. And so that's the other conventional wisdom that I think people feel like I'm not supposed to get in the weeds. Find the three most important things and get deep in the weeds on those three things.

Auren Hoffman (56:03.867) Right, right, totally. Yeah.

Auren Hoffman (56:19.549) Awesome. This is great. Thank you, Vin, for joining us at World of DaaS. Again, Vin, you're the CEO of Yipit Data. Super cool company. One of the coolest data companies that are out there. I follow you on LinkedIn. I definitely encourage your listeners to engage with you there. This has been a ton of fun and super interesting.

Vin Vacanti (56:35.982) All right, thank you, and I love what you do with the World of DaaS. There's not that many data companies. And so it's really helpful. There's endless SAS companies, not that many data companies. So it's really helpful to have this podcast and all that you do for the data community. And I really appreciate it.

Auren Hoffman (56:52.401) Awesome.