[Auren Hoffman] Welcome to World of DaaS, a show for data enthusiasts. I'm your host, [Auren Hoffman], CEO of SafeGraph. For more conversations, videos and transcripts, visit SafeGraph.com/podcast. 

Hello fellow data nerds. My guest today is Daniel Yanisse. Daniel is co-founder and CEO of Checkr a now $4.6 billion market cap company automating background checks. Daniel, welcome to World of DaaS.

[Daniel Yanisse]  Thank you, Auren. Great to be here.

[Auren Hoffman] Now you found the Checkr like in 2014. This is just as like the gig economy was taking off, you had all these companies like Uber and Doordash and Instacart. And they're hiring tons of people or bringing tons of gig workers on. You know, to an outsider, it looks like perfect, like product timing fit. Was this kind of explosion of the gig economy part of the original Checkr thesis? Or was this like a really nice, like extra layer that kind of came into being?

[Daniel Yanisse]  Yeah, no, I mean, it's, it's not just like in random. I had the idea for Checkr because I was part of that explosion of the gig economy. My last job before Checkr was in a gig economy startup, called deliv, was doing retail deliveries on demand deliveries from retail stores, starting at the same time as DoorDash and Instacart and Uber and all the players. So I was in that space. And so that's why I was already there. And I saw that potential need and that gap in the market.

[Auren Hoffman] I mean, I remember back in the day, like doing a check on someone's criminal history was like a non trivial thing, would take a long time. Sometimes it could take like over a week to do and it was it was hard. And it was very costly. And so is that why you kind of saw that similar need?

[Daniel Yanisse] Yeah. And back then I think it was a painful and expensive and slow process, like you say, kind of for everyone for all types of employers. But when the gig economy started, that's when things were going to break, because the needs of staffing and speed and experience for the gig economy are like orders of magnitude higher than traditional hiring. And so yeah, I was seeing that the current process not going to scale for hiring 10s of 1000s of contractors in a matter of weeks.

[Auren Hoffman] Keith Rabois is kind of famous for like a startup formula, which is basically find a super highly fragmented industry with really low net promoter score, with a really low NPS. And then kind of like add a vertically integrated product to do that. To me, it seems like Checkr is kind of like perfect example, almost a textbook example of this. Do you think that's fair? And like, how did you think about it as you're kind of starting up the company?

[Daniel Yanisse]  Yeah, I completely agree with that. I guess I didn't see that quote from Keith Rabois. But when I looked at the market, so relatively large markets, I saw a really kind of broken process and bad product expanse. And I talked to some early customers who also had really low NPS and not happy with their provider. So an industry is highly fragmented. There's over 3000 background check companies, and even the biggest ones have smaller market share. So yeah, it aligns with that. And I agree with this principle, or for other startups and other companies, I think it helps to be in that situation.

[Auren Hoffman] To have a complete product you kind of need to get and you have to make sure you're getting the information from all these different courthouses. And last time I checked, there's like over 6,000 courthouses in America. And you don't want to miss a bunch of them, and then miss, you know, some sort of conviction on somebody. What is there? Was there some sort of aggregator that you're able to tap into to start, because building all those integrations into 6,000 courthouses be really, really overbearing for a new startup?

[Daniel Yanisse] Yeah, yeah. Yeah, it's a very complex supply chain and process to build background checks. And I think like, like every startup, you have to start by doing just the most painful part for the customer. And then over time, you can increase the complexity and then go deeper into that, you know, vertically deeper, I would say, Yes, for us, we found aggregators. They were hard to find. But we did find some aggregators, even the biggest companies were not doing everything in house. They actually were using third parties and different aggregators. And so we found a few suppliers who could get us started. And I would say what we focused on working on was the main pain point for the customer was the product interface. It was that there was no API that was a big part of our success, and even the data bored and the user experience was really old. So we focused on creating a great user and product interface on top of the legacy supply chain and aggregators. And then we iterated and refined from there. But we really started at the top of the product stack.

[Auren Hoffman] Okay, so because if I was imagining if I was like starting a background check company in 2014, or here, I like the first thing I'd be looking at, if you'd like some sort of API, because probably a lot of these guys have like a form you have to go in. And you have to like manually type into some sort of like API, and then probably like, the next thing would be speed, like, you want to get back to them as fast as possible. So they can make a decision quickly and ideally in like a machine readable way. And then the third one would be price is in my mind, or there's some other thing like that, that was really important back then.

[Daniel Yanisse]  Yeah. And so back then was the API was really important. That was not in existence. So we invented the first, you know, JSON open modern API for background checks. And then the second two painful things for customers were speed. You're right. And accuracy, the accuracy of biometrics was really poor. Because the data is very fragmented. There is no standards, there's lots of incomplete data.

[Auren Hoffman] Is the accuracy really on, there's a lot of false negatives, or they're even false positives as well?

[Daniel Yanisse] Both false positives and false negatives. And for background checks, both are really bad, right? Like this an important crime, or you can attribute the wrong crime to a person who hasn't done it, which is also very bad for the consumer. So the consequences are high for both mistakes. But the accuracy rates are extremely low. The accuracy was low in the background check, and it was slow, because actually, the legacy competitors, and the industry was doing the work manually. So they would actually have outsourced, you know, workers reviewing the data, matching the fields, doing the filtering and, and composing the background check by hands, which is expensive, slow and error prone. 

[Auren Hoffman] I can imagine why they were doing that. Because like, doing all these merges actually really got this like identity resolution thing, which looks like a really, really hard problem. And then you've got this other kind of resolution, which is you want to code up, let's say a DUI in a similar way or something, right? Imagine for new startup, both of these problems are really hard to tackle. So how did you think about tackling those?

[Daniel Yanisse]  We started automating that. So first, I mean, at the beginning, we did the same thing as the competition was doing, which was not structuring the data, just offering you know, all of the results and doing some of the regulatory and compliance there's also compliance filtering that is to happen. There's a lot of laws that this is a regulated industry, this federal and state laws that limit what can be shared or not shared in a background check. So that in the beginning, we did it by hand, and there was not structure were not able to separate the DUI from a murder or from different types of crimes. But then we, you know, we knew that structuring the data will be the foundation to create mobile food products and decisioning products. So then we started to structure the data in deterministic way first, when the structure and the fields allow us to kind of categorize and filter but very quickly, we hit the barrier of having completely unstructured data and some type of crime is being called different names in different jurisdictions, typos abbreviations. So it was very hard to just organize free text basically. But I studied machine learning in college and did quite a lot of different projects on that. So for NLP classification, and that's actually something not too hard to do. So then we started to move the way of machine learning to better classify and organize the data, we created our own standards of severity of crime and legal decisions, we use millions of data points from our volume that we're starting to scale and manual labeling to start to train algorithms to classify the data. So that became some of our core IP and and foundation to make the background check data more actionable. And then we built other products on top of that.

[Auren Hoffman] As you're taking the human out of the loop. Like I imagine your variable costs can come down really, really significantly really fast. At that point, are you thinking about how to use price as a lever to gain market share, or like it's not as important just because you've got speed, and you've got accuracy, and you've got the API and the user experience?

[Daniel Yanisse]  Yeah, so our efficiency and costs are lower because we use, you know, automation rather than human labor. That's why we're able to do the same amount of business and volume of background checks as companies for 1000s of employees when we add on the hundreds, so it's like over 10x less people. Price, I mean price is an art in the science, we're still working a lot on price. Actually, like lower price sometimes doesn't help get more market share of customers, especially in the enterprise or larger customers. Larger customers also want to buy a high quality service. And the price is sometimes a reflection of the quality of the service or the value you're getting. So we did experiment with price, we can definitely be competitive. But it hasn't been a major, major factor, I would say to gain market share.

[Auren Hoffman] Because in some cases, we've seen in the data world where like prices, a huge one can go from 20% to 70%, using price as a lever, but it's possible in other cases that companies are happy to pay. As long as there as long as you're charging a fair price. You don't have to charge an unreasonably low price, and companies are happy to pay it if the service is good.

[Daniel Yanisse] Yeah, yeah, I think fair price is very important. And so we definitely charge a fair price, you know, we can be competitive, we like to offer more value to the customer for a similar product that they would buy somewhere else. In our space it is very complex. The customer is not just buying the data, they're buying accuracy, they're buying good software and workflows for their teams to make hiring decisions. They buy in compliance and auditability in case you know, there's HR compliance or litigations. So they really, they're buying a lot of education and customer service to help them navigate this complex area. So they really want a strategic partner that works on hard problems with them. So other areas, you know, data can be commoditized. Like sometimes, you know, if you're just buying a data component or something, it's a race to the bottom, and it's commoditized. And some parts of biometric can get commoditized at scale. But I would say overall, for HR or hiring operations team working with us, they want a high quality product and really good quality of service.

[Auren Hoffman] Now, if you're working with like an Uber or an Instacart, or something, just mass volume of people that they need a background check is so high, I could see why they would want to do like a direct API integration into you. But like, let's say the average small company, let's say SafeGraph, like we, you know, we don't hire that many people, you know, we might have to do background checks on 20 or 30 new people per quarter or something. So for companies like us is the way to acquire us is to like a channel, whether it's a payroll provider or an ETF system, or how do you think about going to market to get the largest global market?

[Daniel Yanisse] Yeah, yeah, no, absolutely. So, you know, small and medium sized customers also love Checkr because of the easy user interface. We have, you know, really simple and mobile friendly, candidate friendly user experience. So it's just very easy to do a background check, given low volume, make decisions, interact with the candidate, in this part of the process. And then in terms of acquisitions? Yes, we started that channel strategy. And early on in the company. Because we had an API, actually, a lot of HR partners and platforms came to Checkr and say, like, hey, would love to add background checks to our all in one HR platform or payroll company. And so the API first strategy allows you to do that. So yes, channel is one of our large ways to acquire customers and distribute the product. We're working with a lot of channel partners, like Rippling, Paylocity, and if it's with also vertical software companies, for team sports, churches, volunteering, so yeah, it definitely works. Well, the API strategy with partners, you know, distributing you to small and medium sized customers.

[Auren Hoffman] How would you give advice to other people trying to work on this channel type of strategy? You know, I've seen work super well, I've seen it take really long time and maybe not show. Are there kind of like lessons learned, are there many scars that you've had that you can instill upon us?

[Daniel Yanisse] Yeah, we were still working on it. It is a more complex motion, because it's indirect, you know, it's like the customer, the partner and you need to to align. There's lots of product decisions to be made and economic decisions to be made. I would say a few learnings are number one, it does take time, there's a lot of inertia. So it's not something that you know, you get marketing dollars, you get acquisition and you keep scaling like you need to. It's gonna take years to put this in motion and then start to see the fruit of those partnerships. I would say don't be too greedy on the economics in the early days. I think the most important thing is to prioritize adoption. Prioritize product quality. So you can do a lot of partnerships and big things like that. Without a product component, I think those things have been pretty weak, because you have to just pay people for two different sales teams, etc. And at the end of the day doesn't change that much for the customer to buy together. So what we've prioritized works well is really, we go to partners, and we make sure that with our product, their product gets stronger. So building our product deeply into their product, investing in that joint R&D and product roadmapping and creating just a great experience for their end customer. That's the priority. If you're able to do that, then the adoption will come and then potential more monetization will come. Prioritizing the product experience of the end customer was kind of a learning to keep in mind. Because sometimes you forget that.

[Auren Hoffman] Typically like a SaaS company or DaaS company, you know, when you're doing a sale, you've got a salesperson and then after they make that sale, you hand that off to like an experienced customer success manager. In the channel thing do is similar to like this like BD sales or into BD who gets the original deal done? And then are you passing it on to like a relationship manager who's like growing that over time? Or is it more combined?

[Daniel Yanisse] No, no, we have a similar thing, we have partner acquisition and partner success. So yes, we have teams who are working with new partners to start integrating with Checkr. And then once the partnership is launched, then we pass it to a partner success manager who continues to work with the partner to make sure that you know the product and the solution is delivering value and and keeps growing. So yeah, similar motion but separate team.

[Auren Hoffman] When you come to the salesperson, there's an ACV. And it's kind of easy, and you can figure it out. Whereas if you think of like the original person making the partnership, okay, that could actually be a net negative because it could take a lot of time and not yield any sales at all, could end up being hundreds of millions of dollars as a channel, like how do you figure out a good way to to compensate that person?

[Daniel Yanisse] Yeah, no, I mean, it's tricky. It's definitely less predictable than a direct sales motion. So we have to take that into account, there's still a way to estimate the potential, not ACV. But the potential long term revenue, right? By looking at the scale of the partner how many customers they have. And in order to iterate, you can kind of get some metrics from the other successful partnerships. But, but yeah, it's gonna be, it's gonna be less, less easy to compensate and have, you know, direct revenue numbers tied to compensation. It's going to be more, you know, based on other metrics, like number of signups number of partners acquired, and things like that more and more quantitative metrics.

[Auren Hoffman]  Earlier, we talked about data merges and stuff like that. One of the nice things I think, with criminal checks is usually are getting like a social security number. Is that true? And is that making that merge much easier because other people have this nine digit number? How do you think about this, like identity merge on a person?

[Daniel Yanisse]  Yeah, yeah. So the other thing, you know, we talked about the classification issue with organizing the criminal records and the data. The other big problem is identity matching identity resolution. And we get the SSN number from the consumer. We can use that to develop the address history and do some identity checks that this is the right name, the right address. The Social Security number is correct. But unfortunately, on the other side, the criminal records the DMV data, all of the data on the other side is not tied to a social security number.

[Auren Hoffman] Oh, it's not. Okay, got it. So this is where you can get a lot of these false positives and false negatives come in, because there could be another Daniel, Yanisse who is like doing all this bad stuff can can get merged in.

[Daniel Yanisse] We need to use you know, complex algorithms and also like, you know, non probabilistic algorithms for identity resolution and matching, there's lots of research and science and papers around identity resolution. But we have to use multiple attributes like first name, last name, middle name, date of birth, addresses, driver's license numbers, so we have to use multiple data points to match identity.

[Auren Hoffman] And I imagine when someone is applying for, let's say, to be a driver, Uber, potentially at the very same time, they're looking at multiple different gig worker type of things. And so you could be essentially getting that the same request of that same individual, within a week from multiple different sources. Is there any, like economies of scale that happened there?

[Daniel Yanisse] Yeah, yeah, I mean, over time, like any data business and data company, you know, the you have economies of scale, as you start to get, you know, large scale, you can start having more buying power on buying the data. And, and you can start to have the same person coming multiple times and have, you know, some reuse of the data or, or having more value each time to kind of increase the quality of the profile or the picture you have. So there is definitely some of that. One challenge, though, for us is that we're in a regulated industry that has really strict requirements on the accuracy and the freshness of the data. So there are lots of data points that we cannot reuse need to have fresh information. But that being said, Yes, the scale of data does help us improve accuracy, qualities, beans, as we scaling, now, we're doing over 30 million background checks a year. And we've covered that think over 70 to 80 million people in the US we're pretty good coverage of the of the US working populations, which which gives some interesting insights. Yeah.

[Auren Hoffman] Now you started the US, but now you're also doing Canada and the EU. And how do you think about this as you move international? Because first in the US alone, you've got all these different state regulations, they're asking different things. Now you've got all these international regulations, I imagine just adds like massive complexity to the workflow.

[Daniel Yanisse] Yeah, yeah, no, absolutely. I mean, we're not just like a software company, we have a for dependency on the physical world, the government's the compliance data laws, it's very complex. It's very fragmented. So. So global is very hard. What I tried to not do international as long as possible that that was one of the first advice I would give is like, if you have a big US market, don't go internationally, too early, like make sure you you grow as fast by focusing. So we did that for the first six years. But last year, our customers were really demanding and asking for international. So that was a limitation of growth that we had to go explore. And we are doing pretty well in the US. So we decided last year, to start the long journey of doing International. They are, it is complex, everything you said, there's also you know, GDPR, and all kinds of privacy laws in different ways. The background checks supply chain is completely different in every country, the laws as well as language on top of that. One thing that is a bit simpler in other countries is the justice system is not as fragmented as in the US, for example, or in the UK, you can hit one data source and have all of the centralized records for the country, that is simpler than 6000 courthouses in the US. So there are some places where the data is a bit more centralized. But, but outside of that, it is very complex, because you have 200 countries, you have countries, we don't have the infrastructure like in the US.

[Auren Hoffman] The decentralization in the US in some ways is a feature for you not a bug, because it makes it very, very difficult for someone else to do something.

[Daniel Yanisse] Yeah, the complexity actually makes that we have a business, it's because there is a lot of hard work to be done to create the simple product for the customers.

[Auren Hoffman] Interesting. Now, it doesn't seem like your data, or at least the data asset could be really interesting, whether it's to a nonprofit or academia or to governments looking at trends on rehabilitation or employment. How have you thought about making a profit? Obviously, it is very sensitive data. So you have to you can only make it available in some sort of aggregated form. How have you thought about working with some of these entities?

[Daniel Yanisse] Yeah, so it is very sensitive personal data and, and we care a lot about privacy and the consumer and helping them. Our mission at Checkr is to build a fairer future and really to help create opportunities for people so we care a lot about re entry fair chance. Helping people with criminal records getting back into the workforce. So on on that topic, actually, we partner with the MIT to to do studies with their economics departments, around recidivism and criminality and access to jobs. So we are battling with leading academic institution, and share it in working with them by sharing some anonymized aggregated data to help them better understand it and study the space and we will work on publishing those results and and helping you know, governments and businesses better, better, better help, you know, sort of some of the social problems?

[Auren Hoffman] Is there a way to tie it back to, let's say, people who have had jail time as a way to tie it back to specific prisons? So these prisons are better at rehabilitation than others are specific jurisdictions or specific types of crimes?

[Daniel Yanisse] Yeah, so I think, right now, we're looking more at specific type of crimes, and we're looking also at the time dimension. So what is the likelihood of reoffending? You know, after one year, two year, three years, four years, and what what was fascinating is, the likelihood of reoffending after, after one year drops extremely low. So the first year of getting out of prison is very important, you know, for the person to, to get a job to get housing to to get this kind of foundation back. If that happens, then the resubmission rate is is really minor is, is into the low single digit percent.

[Auren Hoffman] Wow, that's awesome. Okay, so like, let's get these let's get people a job, let's get them on their feet. Once we can do that now that can be good. They feel like they're productive members of society. They've got a lot to lose. So it by so they do that they're, they're excited, etc. Interesting.

[Daniel Yanisse] 100% Yeah, I mean, employment is the Employment and Housing but employment allows to get income to pay for housing and everything is the number one success criteria for getting out of prison. It, it sounds pretty true. TreeView or, but it is, it is actually very, you know, hard for for people to get employment, when they when they get out and there's not enough structures and support. So that's one big area that we're focused on, you know, funding working with nonprofits, and also helping employers be more open to giving second chances and jobs to people.

[Auren Hoffman] And employment is not just about the money, when when you're when you're at a job, you, you're you're gaining self worth from that job, you feel like you're contributing back to people, you're helping people, you know, all jobs are about helping people do things and you feel like you're a better part of society, I imagine.

[Daniel Yanisse] Yes. 100% I mean, one of the hardest things, you know, I went to multiple prison visits and talk to inmates, one of the hardest thing is read the, the psychological stigma, and and, and brand pressure and shame, some that people have, you know, getting out of prison. So it's pretty important to rebuild confidence and self worth. And so yes, that's absolutely right. Like having a job is a big part of that process.

[Auren Hoffman] And you and you guys are promoting this, you're calling it like fair chance hiring is Is that right? And I assume that means basically also companies should they could choose not to see certain things. So maybe they deem a certain type of crime, maybe like a drug possession from X number of years ago, is no longer relevant. And then they could they could they don't even have to you can filter that out before it even gets them. Is that the way you do it?

[Daniel Yanisse] That's right. That's exactly right. That's, that's fair chance. It's, you know, looking at your hiring policies and questioning, you know, does it really is this information really relevant is that type of crime, posing any risk or even relevant to the job. And then we build products on. That's why we needed to do that standardization and segmentation of the data, because then we were able to build the product called Checkr assess, which is like a rule engine, which allows customers for each one of the job to configure the type of crimes that they care about, don't care about, on what timeframe. 

[Auren Hoffman] And that is the case, it's not just on a per employee basis, but it's on a per job. So for an accounting job, you might care about petty theft, but on some other jobs that might not show up or something.

[Daniel Yanisse] That's exactly right. And you have all of those marijuana possessions that are really not relevant anymore with the legalization of marijuana dwis you know, traffic violations are not relevant if you're not driving, is there's lots of simple things like this, that employers can make, and actually allows millions of people to be qualified and get opportunities. And for the business, it's great because you get access to more talent and right now there's not enough supply and workers.

[Auren Hoffman] Now you're you're also at the same time, like interacting directly with consumers. And you're building different products for them whether it's to help people expunge old records or you know, etc. What What are you so in some ways, you're like, You're like a b2b company becoming also a b2c company usually see it go the other way? Like, is there any interesting learnings from that?

[Daniel Yanisse] Yeah, so the consumer we call them the candidates, the job candidates. They’ve always been a user of our product because we need to interact with them as part of this process together. Send the authorization. We provide services to them. If there's any inaccuracies, and they want to dispute the records, they work with us.

[Auren Hoffman] They're essentially getting a copy of the law. Right? And so then they can go see that, in some ways, it's like a beautiful law for you. Because now your your, your you, all these employers have to introduce you to every single consumer that comes in, right.

[Daniel Yanisse] Yeah, yeah, that's exactly right. So we, we care a lot for the candidates and the consumers, we want to help them go through that process, educate them, you know, fix any potential errors that can happen, even at the source. And more recently, like you said, we, we also have a Checkr Foundation, where we build nonprofit products and the experiment product is one of those we, we created a national website to expunge and clear criminal records. And and we're working on funding that from, from some of the Checkr resources and external as well.

[Auren Hoffman] But this motions B to C motion, in some ways requires like a little bit of a different DNA than the original b2b motion. Does that mean, you have to like, hire all these new types of people that have more that DNA? Or how does one like make that transition?

[Daniel Yanisse] Yeah, I mean, it's really hard to be a b2b and a b2c company. At the same time, very few companies have successfully done that and move from one to the other. like LinkedIn has done it cut from b2c to b2b. monetization. But so for us now, we're a b2b company. So the monetization is from the business, the customer, we see the consumer as, almost as a another customer, or different persona, or user of the customer, they usually tied with one of our customers. And all of the products we build for them are not monetized, they're free. So it makes it a little bit easier. We're not monetizing the consumers, we're providing free services to them. And that's also part of the customer value. So we do have, we are building we have a few. We have product managers and engineers who are focused on more consumer product, and designers as well. But it is not a big part of our platform and monetization. You can see

[Auren Hoffman] It's like everything, fica, so well, Lansing and CEO fica, he was a guest on world of das, and you know, the office, they have their big businesses b2b. But they've got a very substantial myfico business Originally, it was like you were everything was free. But now that they have all these, they have lots of products that they can also upsell people monetize. You could imagine a scenario long term where a Checkr could be could be doing those things.

[Daniel Yanisse] Yeah, we could definitely imagine that I mean, my vision for the long term is to build infrastructure for the future of work, and, and to build more products, and the worker is a big, you know, component of this product. So I do think we will continue to build free products for the workers and the consumers, you could think about potentially, you know, like a reusable background and profile and having kind of a hub where the consumer can get access to more services, and potentially long term some of the services can be could be monetized. So definitely there is some of that opportunity, longer term.

[Auren Hoffman] So on the on the b2c side, like I remember the first time I looked for an apartment in San Francisco, I was advised to like get a credit check before I looked for the apartment, I remember like having this like paper credit check, and I did at my own expense. And then I would show it to the landlord beforehand. And that kind of allowed me to like move up in the line of like a new apartment but never seen a candidate come to an interview with like their criminal check already in hand or something like, why is that not done?

[Daniel Yanisse] Yeah, that's true. So a few reasons why So first, like right now you don't really know about the place to get your free background check yourself as a consumer, right? Like, that's not really a thing. We did build a website called better future that calm that allow consumers to get a free background check. It's mostly to help people with criminal records to understand their picture and then help them be prepared. So that's one one of our free consumer products. And and then we are working with tenant screening and real estate customers who are doing credit check and background check on potential tenants and it is part of the process, but it hasn't been traditionally done by the consumer presenting it because there is not that much consumer adoption of checking your your your background check right

[Auren Hoffman] now thinking about product expansion as you're thinking about product expansion. So, you know, we have this theory that we wrote about in the das Bible that it's much easier for a data service to acquire another company than for a SaaS company to acquire another company who SAS companies have, you know, like, just a whole UI that makes it really, really difficult to to do the integration. First of all, do you agree with that theory? And then if so, like, how do you think about Apple As a growth strategy,

[Daniel Yanisse] I agree with that theory, that's what makes, you know, product acquisition integrations hard, they still happen a lot. I mean, a lot of companies are buying other products, it just it is harder to create a user experience that the same. So for Checkr is a data company and the SaaS company, we have all of the data, but we also have a lot of API's and workflows and dashboards and automation. So so we have that, that also similar issue with with the user interface in the dashboard to integrate. But, but we actually even started running the business in a separate ways, we have a data business at Checkr that is, like really two data company, and then we have like more than the SAS company on top of that. And we so we have

[Auren Hoffman] companies like our fixed pricing data companies are variable, like per API call type of thing, or, or is it all just like one bundled thing?

[Daniel Yanisse] No, it's, it's different. So our data company sells data to Checkr as an internal customer, but also to other, you know, other companies, you know, credit companies, identity companies, or the,

[Auren Hoffman] like someone who owns like, the internals, check our data company, p&l type of thing, or something. Yeah, that's cool.

[Daniel Yanisse] It is CEO of the of the data business. And so that's from the Bentley, we have built that business through m&a. And I agree with you like it's much easier to buy data companies and, you know, start to integrate the data sets and think there's still a lot of work to do to integrate the data sets and create, you know, unified products, because you see the one some type of API's and stuff and dashboards, but it is easier. So definitely, we've done a lot of m&a on the data side, the pricing structure, actually, the background check, pricing has always been transactional, because there's some some viable costs. And that's how the industry has been. So we kept the transaction or and usage base. Some of our new add on products, like Checkr assess our subscription products. So we have a mix of both,

[Auren Hoffman] if you think of like the overall employment verification market, where there's like the criminal background check market, that's Checkr and and your, your, your fast becoming like by far the biggest company in that space. But then there's also do like, kind of the adjacent market, which is verification of someone's employment history, maybe their salary status or other types of things that they work at this place. I don't know maybe even like their history of like, did they go to this college or something? And you know, that that they're in that case, there's like the huge player of the work number, which is a big dominant player, there's like these new players that are trying to pose the work number like to work in Argyle, like, how do you think about that market? As with your market, it does seem like it could be one big company in a way. Right. So how do you see that happen? changing over time?

[Daniel Yanisse] Yep. So even for us go for background checks. There's lots of components, the Criminal Code and DMV ones are some of the big ones we started with. But a lot of our customers wants what you said, like credit checks, employment verification, education verifications, verifying credit credentials and licenses doing rep is there's lots of things. We have 36. Actually, it's good record screenings as an offering to customers. And we're definitely growing into those adjacent ones. So that's a part of our expansion and what customers are buying in general in their background check. We do partner with a lot of companies for those additional screenings. And we do partner with one of the companies you mentioned, we are partnering with the work number, who is big on employment verifications. And I'm also excited about the new newcomers in the space like companies like Argyle and to work that we are so close to and and partnering with.

[Auren Hoffman] Now, if you're then when you were starting Checkr, the background is the background checks, companies were super fragmented. As you mentioned, there's like 1000 of them, no one probably had more than 10 or 15% market share. That's a great place to attack, especially if the play overall has low NPS. If you if you were advising like a true work or argyl because they're attacking this space, you have the work number, the work number has, I don't know maybe over 50% market share a harder place to attack How should those companies if you were advising them, how should they think about attacking a market where there's a there's a clear winner?

[Daniel Yanisse] Yeah, I mean, it's it's harder, like the work number has been smart as a business and you know, sometimes it takes decades to build a defensible data business No. LexisNexis is another example or this. And, and so they've built kind of an exchange of data, right, like, they go to the employer, the employer, you know, uses them to the employment verification and gives them their company employment records in exchange. If you again and Co Op that happened like I did I call up exactly. So they've built over 1020 years of data Co Op. So that is hard to disrupt because, you know, it's hard to go to at&t, one of the big customers there and say at&t, you know, in addition to the work number, can you work with us and give us your data, there's no motivation to work with the second, you know, vendor. So that's, that's, that's what it's tricky to to disrupt their business sometimes, especially when there's data Co Op from from the customer. So I think the, you know, I think it's, again, it's going back to the customers and the MPs, so you got to find the pain point of the customer, a customer is not going to change or buy something new, if there's no pains, it's like, what is the pain for the customer. There are trends like privacy and security and user experience where you know, good trends that are a bit hard to do for bigger companies. So So that's, that's going back to the pain. And then I do think, what I like about the argyl model, or even plaid, what they've done as a company, is you have to come at the problem, you can't just like frontally compete with the existing player and do the same thing to do just faster, cheaper, that's not going to work with probably enough value, you have to come at it tangentially and end with a completely different value proper approach. And so I like blood or our guide approach, which is saying, hey, let's put the consumer back in charge of their own data. And that goes well with all of the you know, privacy trends and data trends. And let's say, hey, it's the consumers data. So so we can use the consumer to give us permission to access the data. So I feel it instead of going to the business to get the data about the consumer going directly to the to the consumer, I think is, is a smart, it's a smart financial approach and, and could potentially work to create an alternative, we

[Auren Hoffman] think about the whole hiring process. Like it's super hard, it's expensive, both from the job seeker side, and from the employer side. lot of companies are bad, a lot of job seekers are bad at it. Background checks are kind of like the very last step or one of the last steps in the process. Like Are there other parts that you wish? Like there was another company like Checkr that was working to improve?

[Daniel Yanisse] Yeah, I mean, I can think of their own hiring, we do a Checkr. Hiring is super hard. You're right, like it's every step is hard. It's full of human bias and inaccuracies. And at the end of the day, you make a big decision for your, for your job, and for hiring someone based on just chatting with them, or, you know, for a few hours, it's kind of crazy. But it is what it is. I don't know, I think the job search to me sounds just so inefficient. It's kind of like you're throwing your resume and applying it clicking apply to 10s and 10s of jobs. And it's like a black hole. And from the employer side is the same thing and getting 1000s of resumes and candidates and you have to triage door. I do feel like it's quite painful and manual and technology could maybe do more than than what's available currently. But but it is hard to to get to this caliber LinkedIn or indeed and build better products there. So it's a hubs, hub space, very focused on the human,

[Auren Hoffman] it's also so there's credentials are so important to basically go through that filter. And obviously, if you go back to like your original point about getting more people access to employment, a lot of the people who most need access to the people who don't have like credentials, but they do have a lot of potential. Is there some way of I don't know, putting in a tester I know some other type of thing in the process, the low friction thing that could, you know, tease out these high potential people.

[Daniel Yanisse] Yeah, I mean, there's tons and tons of companies and startups doing tests and assignments and assessments and video interviews. There's hundreds and hundreds of tools, select psychological tests and skill tests. So I think I think those are good. It is just so dependent on each job and it's interesting there's no there's no standardization yet or are super good at so I think that's interesting space, but it's still early and so custom invaluable or I don't know what you know, some things that are maybe closer to what we do we're looking at is the especially on the more know frontline workers and and not knowledge workers like like more, you know, lower wages jobs. There's really an embed imbalance between the employer and the worker like there's no LinkedIn for For Non knowledge work from the outside, it's very hard for workers to know how much they're going to make on platforms or on website, what's a good employer or not, you know, like, we have? What's the review website for Glassdoor? Glassdoor, but you know, there's not Glassdoor for everything. So I do think more transparency for the workers could be something valuable to help people know where to apply and what what jobs would be good for them. So So that's something I think, would have potential and would help both the workers and the businesses.

[Auren Hoffman] Now over the years, when I've gotten to know you, I've, I've asked you a lot of questions about like, just general company building, you're kind of a student of building companies like what are what are some of the things maybe you've changed your mind about company building over the years?

[Daniel Yanisse] I mean, that's a big topic, lots of learnings. You know, seven years since I started, actually, like, every year, I learned a lot and changed my mind on many things. I mean, I would say one, one thing that then I'm learning and working on like, even now I'm company building is, you know, on culture and culture building, I'm, I'm an engineer founders, lots of engineer founders, I like things to be like, you know, very organized with plans, you know, because, because, because I like them very detail oriented like this. And also, as an engineer, I like to fix what's broken. So I always focus on like, what's broken, constructive feedback, but to improve, I'm always in that mindset, or of this is good. But you know, also, I would say, for company building, it's important, like two things like, it's important to you can't prescribe and control everything, like maybe an engineer like me would like so adding more flexibility in the system. It's okay, if not everything is like, a perfect process. And, and leaving, you know, some chaos is actually good sometimes for innovation. And then on the positive versus things that can be improved. As a CEO, like if you focus too much on the negatives always, and things that can improve, it's not as positive and exciting over and motivating over culture. So one thing I really learned is, it's really important to spend time, celebrating the wins a lot more positive than the negatives, even if there's a list of a million things to improve, like, talking and communicating constantly about, like, What's going well, and the positives and the things we're proud and we achieved, That, to me was not a natural thing. But I really learned to do more of that. And I've seen, the benefits are pretty awesome. Like, it creates a lot more positivity and motivation in the company to then go fix the things that are broken. So I'd say it's mostly that kind of communication and culture building, and also the mindset of, of the leader or CEO on on feedback. And how

[Auren Hoffman] do you think I mean, there's these things that are broken, every company is riddled with stuff that's broken everywhere. And but and then there's also, which is great, you can fix those, and you kind of know what the ROI is fixing those things. And then there's the things that are starting to work pretty well. And if you invested more time on those, you may have an unbounded opportunity of growing that how do you how do you know how to put what resources toward which side?

[Daniel Yanisse] Yeah, I mean, I, it's hard like I you never know perfectly it's. And I don't know if we know it either. But I would say I tend to be too much focused on fixing the broken things, rather than investing into the things that work. I think that's also an important thing is like, focus on what's working with growing, which is also more exciting and double down on what's going well, rather than spending, you know, all the time fixing these things that are broken. So it is a balance, we need to do some of both. And it's important to step back to prioritize, you know, company goals initiatives for a longer period of time. But that's definitely a shift that mentally I've made and I'm working on making, how it's going to have no ROI to double down on what's working when rather than trying to fake what fix what's the what's everything that's that's not working. It's okay to have broken things. Like all companies have broken things and all companies

[Auren Hoffman] are a mess inside. Yeah, absolutely. Yeah. Couple personal questions. So I've noticed there's just like a massive number of French founders in Silicon Valley weigh more than even people, let's say from Germany, which also has like, you know, incredible engineers, etc. In some even there's some investors like Jason Lemkin, who basically made a name for himself by like, just investing in French founders and I personally invested in over six French founders, like, you know, gorgeous air byte stream lit, etc. Like, do you have a theory as to like why this is a phenomena or is it just like random that is happening or what why do you think this is going on?

[Daniel Yanisse] Yeah, I don't know. So I don't know if I don't know if that's a bias because maybe we're seeing the French founders. But sounds like maybe some data points.

[Auren Hoffman] I guess it could be just my own experience. I don't know. 

[Daniel Yanisse] So I'm not sure. I mean, I can say for me, so first, like, so I'm, I'm French, but my parents are immigrants. So, you know, I'm an immigrant, myself coming to the US. I think even parents emigrated to France.

[Auren Hoffman] And then you immigrated here like a double immigrant?

[Daniel Yanisse] That's right. Yeah. Yeah. Yeah. And I think there is a button of immigrants, you know, starting more companies, I think more than half of companies are started by immigrants. So that's a pattern. And I think a lot of French people immigrate from France, me great from France and go to other countries. I think, even growing up in France, culturally, it's very much a trends for young college grads to finish college or even before college or going to other countries to get international experience.

[Auren Hoffman] Okay, so you work for like, if you're in the US, and you are from McKinsey, you usually stay in the US worse, like if you're in if you're a French student, you go work for McKinsey, you might go to London, you might go to Hong Kong, you might go to some other type of place.

[Daniel Yanisse] Yeah, that's right. It's interesting, right? Couldn't be tied to the, you know, Imperial history of France in the UK, as well, who, you know, just went all over the world. And so that continues in it, there's a lot of French people who just go work all over the world. I don't know if there's also like the risk taking in the DNA in the culture. There are some cultures that have more risk taking or less risk taking, France is quite risk taking. 

[Auren Hoffman] Even though it's fairly risk taking it does seem like a lot of the well known French entrepreneurs live in the US so they didn't they chose to, for whatever reason, do it in France.

[Daniel Yanisse] Yeah, I mean, when I left France, because it was not the best place to start a company, it was not as you know, in the US, I think the US is a great place to start companies, because there's that culture of being pioneers and taking risk. And, and, and doing you own thing that is really encouraged and celebrated. In France, and many European countries, it used to not be a celebrate, I think it's changing since I left, you know, over 10 years ago, it's changing, and there's more and more startups in ecosystem and culture. But back then years ago, you know, the dream path for for college grad or engineer was not to start a startup it was to go work in a big company and things like that. And then when I found the US and the startup ecosystem here is just much it was much easier to start a company here. And that is that positivity and optimism, like much more optimistic view of taking risk than in France, which sometimes can be a bit more, you know, realistic or sometimes pessimistic on taking risk.

[Auren Hoffman] Interesting. Our last question we ask all of our guests, if you could go back in time, what advice would you give to your your younger self?

[Daniel Yanisse] That's a good question. I would say, finding the company. I mean, things I struggled with were, you know, team building and hiring the right people learning how to manage that with the first time in my life, I had to manage people. And you know, it takes time and it was kind of under pressure accelerating learning to become a leader in manage so. So I would say, yeah, taking the time to build connections, and really, and building closer connections with with employees and the leaders and being a bit more flexible. I think that there'll be one thing I went through a lot of iterations to put together leadership teams and hire the right people. So investing more in that I think it's very important. I learned a lot through that process. And then maybe another yet another advice more on like business and product, like when you get investors and VC money, as a founder, you feel that pressure to grow and do returns and revenue. And so it of course, that's important, and that's good. But it's important to not be too much under that question. I've talked to a lot of founders who really feel heavily that pressure of the of the growth and financials in revenue. It's important to grow and of course, that's what we want to do, but do add, I was maybe a bit too obsessed by the bookings and the revenue growth into doing the company's strategy and direction. So I would advise myself and maybe others, too. The best way to grow is actually to build a great product, delight customers, and focus, like focus on less things instead. Because as founders, we kind of like to do a lot of things, we have ideas, focus on one or two and really try to say no to things, great product to solve customer problems, and then delight the customer. And if you do those things to grow up, the financial growth will come as an outcome, you have to be kind of confident that you will come in I know it's hard. But that's maybe advice for myself. Come on, don't stress as much about the revenue and the growth. It's a byproduct of doing product and customer delight when

[Auren Hoffman] All right, that's awesome. That's great advice. I follow you on Twitter, where do people go if they want to learn more about you? Is that the best place? Or where should they go to see you on the interwebs?

[Daniel Yanisse] Yeah, I haven't been, you know, great. And then a lot on social media, but I'm actually starting to spend more time there. I'm quite active, more on LinkedIn, and I'm growing into Twitter. So yeah, LinkedIn and Twitter, follow me and I'm gonna interact and be more and more presence

[Auren Hoffman] Thank you so much for being with us on World of DaaS.

[[Daniel Yanisse]] Thank you. 

‍[Music playing]

[Auren Hoffman] Thanks for listening. If you enjoyed this show, consider rating this podcast and leaving a review. For more World of DaaS (DaaS is D-A-A-S), you can subscribe on Spotify or Apple Podcasts. Also check out YouTube for the videos. You can find me on Twitter at @auren (A-U-R-E-N). I’d love to hear from you.