Today, my guest is Patrick Comer, Founder and CEO at Lucid. Lucid is the first global exchange for market research sample which introduced programmatic buying and selling to the market research industry.

Prior to founding Lucid, Patrick has a decorated career in market research and consulting that spans 2 decades. He lives with his wife and children in New Orleans.

FIND PATRICK ONLINE:

Twitter: @comerpatrick

LinkedIn: https://www.linkedin.com/in/comerpatrick/ https://luc.id/

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www.happymr.com

Facebook: https://www.facebook.com/pg/happymrxp

Twitter: @happymrxp Instagram: @happymrxp

LinkedIn: https://www.linkedin.com/company/happymarketresearch/

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[00:00:00]

Hi, I’m Jamin Brazil, and you’re listening to the Happy Market Research Podcast. Today, my guest is Patrick Comer, founder and CEO of Lucid. Lucid is the first global exchange for market research sample which introduced programmatic buying and selling to the market research industry. Prior to founding Lucid, Patrick has a decorated career in marketing research and consulting that spans two decades. He lives with his wife and children in New Orleans. Patrick, thank you very much for being on the podcast today.

[00:00:28]

I am super excited to be here and to support you in this new venture. I’ve been excited to see what you’re going to be up to, and this seems to be the perfect fit, so glad to be here.

[00:00:37]

Ah, thanks very much.

[00:00:39]

Hell yeah. Rock and roll.

[00:00:39]

We all know that our parents and upbringing have an impact on who we are and what we do, varying degrees of course. What did your parents do, and how did that affect your career?

[00:00:49]

So my parents were both priests, which meant that I got to see my father do a sermon every single Sunday, and values and the articulation of those values were important in the household, but also obviously in church every Sunday. And I was reflecting on, what did my parents teach me? What were their core, fundamental lessons that I learned? And I was reflecting on my mother and how she really taught me that it’s not really about me, that the things that I do, the success that I’ve had with Lucid, at the end of the day, it’s not about Patrick Comer. I’m not trying to achieve things for me. It’s really about influencing and impacting the people around me. I’m really not the most important person in the room, and that’s a humbling thing to learn from your mother. And it’s really helped me, I think, be a CEO, because my job is to support the people around me, not to be the focal point myself. And I also reflect on my father, and his big lesson for me time and time again was that everyone mattered. I saw him so often spend an incredible amount of personal time with what would be conceivably the least important person in the room. And it was that presence that he had with everyone-helped me treat, quote, important people as human beings, but also the least among us as people of importance and people that need to be spent time with. And so I really look at those two things, that it’s not about me and everyone matters, as kind of the bedrock of my core values from my parents.

[00:02:36]

It’s interesting how the core values informs really who you are and what you spend your time doing. Entrepreneurship of course, and then one of the interesting things about the company that you started is where you started it, New Orleans. Can you talk to us a little bit about that?

[00:02:51]

I’m just really lucky I moved to New Orleans when I did. People always ask me, did you have some strategic plan of moving to New Orleans and starting a business? And it couldn’t be more complex than, I had just gotten married and my wife and I were starting a family. And she’s the youngest of 12, and so we decided to have that family be raised among family here in the Big Easy. So we moved three years after Katrina to New Orleans, and I thought that I was just going to connect with and network with the startup community here, the venture capital community, and find some company to join and help build. And the reality on the ground three years after Katrina is that there were no startups, and I was going to have to build something in order to contribute. The remarkable thing about a startup in New Orleans is that there are two passions at play. It’s really normal for a startup company or a new company to get excited about the mission of the business. We’re going to build this thing; we’re going to be disruptive; we’re going to grow fast; we’re going to get excited about it. That’s normal. But there’s always been this other passion, this other thing, which is the importance of contributing to the community, that our work was really helping the community, and job by job, hour by hour, rebuilding something. And it’s been a core part of our culture where impact on community is a big part of what we do, and is an equal partner to success of business in terms of how we spend our time and what’s important to us.

[00:04:25]

So thinking about those early days starting in New Orleans, wanting to make a meaningful-build a meaningful company that creates jobs, and that’s a-I’m hearing that’s a big motivator, did you start out with funding straightaway?

[00:04:38]

I was able to hash together about $350,000 out of the gate, which is funding. That’s not a non-trivial amount of money, but it’s not millions of dollars. And all that was from local investors and local friends and family, honestly, who was willing to support the venture. So it wasn’t a lot of money out of the gate.

[00:05:00]

And so straightaway, were you spending most of your time talking with customers, [INAUDIBLE] developing the technology?

[00:05:06]

Yes, sometimes, as you well know, entrepreneurship is part luck and timing and luck of timing more than anything else. I started the business in 2010, which is still in the end of a downturn, from 2008 to 2010. So it was just beginning to swing back. People were looking for alternatives, brands were looking for alternatives, market research firms were looking for alternatives, and how they were going to approach this survey business, this research task that they were looking at. And so most of my time on the ground early was revenue, getting clients, building relationships and building trust with those companies that were going to really help us scale in the future.

[00:05:46]

We talked relatively early on, I think it was about 12 months after you started Lucid-it was a big movement, right? There really hadn’t been adoption of anything like a platform-based sample transaction, right? This whole marketplace’s very early days-can you talk to us a little bit about the education of the market that had to take place?

[00:06:09]

Well, I call that pushing sand uphill, because we were-it was a constant battle. You feel like you’re moving something forward; it’s hard to tell when things actually catch on. But we were constantly talking about the future of the industry, which is kind of an odd thing for a new entry to be talking about, where all the industry can go in terms of researchers, and brands, and buyers and sellers of sample and data collection platforms. But we had a clear vision that there was a future state where a lot of this activity was easier and technology driven, versus a brokered process that was the standard when we got started. But it was really hard, because changing operational process within any organization is hard, because you’ve got habits formed, you’ve got behaviors, you’ve got perceptions about how things should or should not work. We were basically asking our clients to change their operational paradigm, and it was really, really hard, while at the same time trying to build a two-sided marketplace, which if I had known what I was actually trying to accomplish out of the gate, I might not have stated the company, because talking with other marketplace leaders, two-sided marketplaces are kind of the hardest business to create, because you’re building both sides at once, and there’s a constant chicken and the egg problem. So for years, it was building one side, then building the other, and convincing client by client that this change of operations, this change of approach from manual to automated, was going to have a significant impact on their ability to delight their clients. Over-it took probably four years for the word “programmatic” to be OK to say in public. And then it’s only been this year that automation has been normal, where clients come to us and say “We would like to transition our business to DIY and automation; how can you help”, whereas before, we were always calling them and saying “Have you heard about this thing we can achieve together? Would you like to learn more?” And that’s been a sea change in terms of how we approach our clients. So we’re over the hump in terms of sand uphill; there’s a lot of runway to go still.

[00:08:27]

It’s certainly exciting times. I love your perspective on, it’s been the last 12 months. I completely concur with that. But before we leave those early days, I want to talk a little bit about partnerships. Did you have any partners that helped you found the company? And then how did that partnership or lack of partnership impact you?

[00:08:42]

It’s a very interesting topic among entrepreneurs, is, should you have a co-founder or not? And I remember in business school at Columbia taking the entrepreneurship class after I’d been in a startup, because out of curiosity, what did academia think entrepreneurship was? And I remember the professor would just bring in founders every week and have them tell their story. And the thing that I learned most is that there is no single path up the mountain. There were some founders who were religious about having an equal co-founder partner, and they could see it no other way. And yet other founders who thought it was-they had to be the only founder and that was it; there was no other path for them. So what I pulled out of that entire conversation was, it’s really up to the founding team or person the path they want to take. There’s no correct or right answers-the right answer for them. For Lucid, my founding partner was kind of my brother Walton, who would fly down from New York every weekend to help me think about what kind of business would be worth doing. And he had a remarkable amount of influence preparing Lucid to become a marketplace, and really building the language and the business model out of the gate. But he didn’t join the business, and so when we started the company, it was me in my garage apartment. And that’s-when they talk about having a startup out of the garage, that literally happened with Lucid, and then to a co-working space and on. So one of the other challenges, it was in New Orleans, and in this town in 2010, not only had a lot of people left because of Katrina and not returned yet, but honestly, I don’t think any one of them knew what the word “sample” meant. So there was no industry knowledge anywhere. Every person I hired, I had to train from the ground up in terms of what this space was, what is research, what is insights, what is sample. Now the good news is, they learned to repeat what I said, so they didn’t have any preconceived notions, which is a positive and a negative. But it was just hard because there was no talent around that knew how to be a project manager, who knew how to program a survey, who knew what any of that was. So it was really from scratch literally; it was hard to get the ball rolling.

[00:11:00]

It’s almost like pushing sand up two hills at the same time, right?

[00:11:04]

I couldn’t just hire people that knew what I was talking about. That just didn’t exist.

[00:11:09]

How did you wind up-from an HR perspective, that sounds like a very difficult challenge.

[00:11:15]

Yeah. Because of this, because we’re in New Orleans, a remote employee is a more natural thing to have. And so we’ve had-yes, we’ve had the abundance of our team in New Orleans, but we always have team members across the country, and in Los Angeles and New York and other places. So that’s always been a part of our culture, that you can be anywhere and work for the company. And that’s out of necessity, because there was no one in New Orleans who really could join at the beginning.

[00:11:42]

Let’s shift gears a little bit. After you started the company, I think it was a few years, you wound up starting what is now I believe the largest sample-oriented event in the market research space, Samplecon. What’s the mission of Samplecon, and why did you start it?

[00:11:58]

Well, it’s funny. The-Samplecon was started because there was no user event for Lucid at the time. And so in 2013, we basically invited all of our friends who were working with us or wanted to work with us to New Orleans to talk about what we’re doing, where we’re going. And it was-that first Samplecon was very Lucid-specific. But once we had the conference, we realized that we’d missed the real value. The real value wasn’t about Lucid at all. The real value was that a lot of sample experts could talk about where the industry was going, its pros and cons, in a relatively safe space. Yes, they were among competitors, per se, but they weren’t at a more research-oriented or brand-oriented conference where their primary function is to sell, and all the content is about how brands approach research, or how researchers are approaching the process. And there wasn’t a place where you could go as sample experts to talk about sample itself, where the content was very specific about where the industry was going, and what were the challenges and opportunities in front of us. So we quickly pivoted to the next Samplecon was not about Lucid at all. Now, the challenge was that a lot of people had quickly associated Samplecon and Lucid, and so we-I think we had three Samplecons in 18 months. We were on this rush, because literally there had never been a place to have this conversation before. Over the last couple of years, it’s become its own independent entity with its own board. Sima Vasa at Paradigm Sample is now the chair of the board. I’m still a board member, but I officially roll off in March. So literally, it’s completely independent conference and process from Lucid, but it still has that mental tie to Lucid for obvious reasons, because we were the main driver of it out of the gate.

[00:14:01]

Thinking about the early days in LA of your career, you started at, I believe, OTX, which pioneered the River methodology.

[00:14:09]

Well, I do have a point of clarification. The River, in my mind, was founded by DMS, by Chuck Miller, long before any of us showed up, or at least I showed up, to the scene in ’96, ’97. And the DMS/AOL patents are the River patents. We at OTX always held a distinction between River, which is a Web intercept model, and routing, which was yield management aggregation of many different sample suppliers. I will make that subtle distinction because I think it’s important to give credit where credit is due, that Chuck Miller, in my mind, is really the godfather of modern sampling from that team which included a lot of people like Mike Billingsley and others that were part of the initial AOL/DMS team. They really were pioneering very, very early. The big insight that OTX had is that it’s valuable to pull together many different sample sources. The norm at the time, in say 2000 to 2007, was that most research agencies would use one, maybe two, sample companies. All of their work would go to one or two. Shelley Zalis, the CEO and founder of OTX, her thesis was that she didn’t want any one or two companies to have that much influence over the research and quality of research, but also the ability or not ability to deliver a project. So she went down the opposite path, which was, we want as many suppliers as possible. That gave OTX the control over quality, the control over delivery, and let’s be honest, the control over price. And it was that mentality that created the first versions of routing, and blending, and aggregation. All those things that are normal today that everyone uses across the board, all that was kind of early days at OTX.

[00:16:02]

So you fast forward, it’s now very obvious-congratulations-that a marketplace wins.

[00:16:09]

Boo-yah.

[00:16:09]

I know.

[00:16:09]

I’m glad it’s [INAUDIBLE] now, because for a long time we weren’t so sure.

[00:16:14]

I remember.

[00:16:17]

I’m glad I was right in 2010, right?

[00:16:19]

What three challenges-I like threes. What three challenges did you have to overcome in order to disrupt the space?

[00:16:26]

I would say the biggest challenge is that most people in research and in brands didn’t understand where sample actually came from. So for years, the expectation was that there’s this fantasy panel that’s really, really big, that delivers all the things. It’s double opt-in, and you can buy it, and it’s special and perfect and good. And that’s the story that myself and everyone else in sample had been telling buyers for years and years. But the simple reality by 2010 was that just didn’t exist anymore. There were great panels; don’t get me wrong. But the reality of sampling was aggregation; it was routing; it was River. It was all this sourcing and blending. The biggest challenge was that the researchers and brands had been sold a bill of goods in that respect. And getting them over the hump of understanding and trust that what’s actually happening in sample today is still good, and maybe even better than that expectation that they’d-the myth that they’d been sold, was the hardest part. It’s still hard today. There’s still the prevalence of belief of this panel process that started in 2000 to 2005, when the reality is that every sample company uses their own assets. They use various marketplaces, various brokers, they use routing. They use all those yield management capabilities to deliver a quality product. It’s just that most buyers either don’t want to or are afraid to go down the rabbit hole with their sample partners. And that was probably the biggest disconnect was the difference between what buyers thought sample was and what it actually was.

[00:18:22]

So a lot of underneath the hood there, right?

[00:18:25]

Yeah.

[00:18:27]

And I think that kinda serves the overall point, which is trust is paramount in this space because what you just said is complex, and actually the execution is infinitely more complex. And brands just don’t have that opportunity for the head space for that.

[00:18:43]

They don’t. And they don’t, and I think that was one of the challenges was as brands and as research partners wanted to have greater control and greater insight into sampling, you had that learning curve to be honest, and that’s always been a challenge. So I talked a little about pushing sand uphill, convincing them of the model, but there was, again, it’s going down the rabbit hole. And would often say to an individual client, “Do you really wanna go down the rabbit hole of how a sample actually is created today?” And to be honest, there’s still people who don’t. They would prefer it to be the other story.

[00:19:26]

I definitely get the sunshine and rainbows view, but I think reflecting back to the early days of e-Rewards, which of course you remember, they were commanding a very different price point than the rest of the market, all predicated on better sample. The point is that people were buying based on the brand promise. And I do think that that definitely plays a major role still today, especially in the framework of an open marketplace like what you’ve created. And so to that end, how are you, or are you monitoring overall sample quality?

[00:20:04]

Well, I love talking about sample quality because it means so many different things to so many different people. I think that for most part, people have a hard time defining with numbers what quality actually is. You know bad quality when you see it in the data. That’s easy. But without a bad data performance scenario, you, it’s hard to measure necessarily good quality. And so the way we look at quality at Lucid is, one, you gotta start with table stakes, which means anti-fraud, high security, and basically uniqueness of respondents across supply. And that comes from our, not only investment in millions of dollars but also research of how other marketplaces handle security fraud and quality control from a multiple vendors standpoint, companies like eBay or Amazon, and really understanding the investment required from a marketplace to control for those factors. And it’s an ongoing fight to manage fraud and manage security across a marketplace of any size. And then you have data quality, and data quality assumes that the respondent is real. But is the data actually any good? One of the thesis that we came up with is that our clients, the buyers, are reviewing all of the data in every single survey, and so they know what quality is. So we created concept of an acceptance score, which is, how much of every supplier’s sample is actually accepted by the buyer after they’ve scrubbed it? And of course we have to weight it by, surveying by buyer in order to get a good score that’s fair to all participants. But what it does show is, on a relative basis, is once supplier rejected or accepted more than the other supplier across the board. And that’s really helped us understand which suppliers are performing and which ones are not without us having to come and define what data quality is as a marketplace. The other thing that we look at is consistency, and this is of course very important for things like trackers and for pre-imposed or any kind of data replication scenario. And so because we see so much supply so often, we can actually measure its consistency period over period. And right now we measure the consistency of all the vendors on a quarterly basis. And then finally, we’re able to measure each supplier against an offline norm, which I think is really important to understand the difference between a panel or a sample source and what’s normally expected in a market. And that’s a scenario where we work with Chuck Miller to design that survey instrument and measure against offline norms. But it’s important to note that we built the quality program at Lucid by partnering with the largest research firms out there. So we partnered with Hall and Partners, Lightspeed, Ipsos, and three or four other. Like GFK as an example. All of those companies helped design our quality instruments so that they could measure the supply chain on our end.

[00:23:13]

That’s super interesting how you guys are addressing that. I know some companies, some agencies are incorporating not just Captcha but also red herrings-

[00:23:24]

Absolutely.

[00:23:24]

Type questions in order to help control that side on there. And so one of the things I find interesting is how it’s almost self-solving as research companies become better at fraud detection and bots get smarter and smarter. There’s still earth elements that can be incorporated to protect the value of the insights.

[00:23:46]

Fraud is a constant cat-and-mouse game. I remember at OTX in 2003, when we were infiltrated by some, or we thought were some Russian hackers who had built a script to automatically sign up for various panels using Yahoo. And then had found a mechanism to create completes through auto-completion of surveys. So this was 2003. And we found that it was happening because every single ID from a particular supplier was the same version of an email address but up by one digit every single time. And that was a big enlightened moment in 2003. We were like, oh, it’s not the suppliers necessarily. It’s that these outside forces are coming in who are trying to gain the system for their own benefit, and really started all the different types of security and fraud detection you can use. Red herring is a great version of that. Attention checks, Captcha. A lot of the work we’re doing now is actually more technical than human based where we’re requiring server to server connections between platforms so that a user can’t manipulate a link and create a survey or create a complete on their end. I know that’s in the weeds, but what it really means is we’ve had a huge investment on technology, not just with us but between data collection partners and survey platforms and sample suppliers to make the process of sampling more secure and make it less able for a third party who’s trying to beat the system to even get into the game, period. And that’s had a huge impact on that acceptance rate, and we track that very closely obviously. And it’s actually gone down precipitously over the past year, mainly because of technology integrations with all the players in the business to eliminate the ability for a person to actually intercept part of the process and game it. So that’s had a huge impact on success of removing fraud from insights.

[00:25:52]

Recently at IAAX, they had the pitch competition as part of the Atlanta-based event. A lot of fun. I was blown away by the volume of blockchain startups. But how are you seeing blockchain impact our space? Specific in the sample area, obviously.

[00:26:09]

Well, I think the first thing it’s doing is sucking up all the innovation. I say this kinda tongue and cheek. That literally it’s with the rise and fall of the price of Bitcoin, the amount of investment time, energy, and attention has waxed and waned, which I think is just a sign of the times. It kinda reminds me of the Internet bubble. At that time I was on the let’s-go-Internet side of the equation, and there was so many existing players that said all the same things about the Internet that people are saying today about blockchain. It’s very reminiscent of, oh, you can’t do this. The technology won’t actually work. There are too many things to be integrated for it to be successful. At the same time, you have the blockchain innovators who are saying words like, “We’re gonna disrupt the panel and it’s gonna change everything.” And I think it’s gonna play out similarly to the Internet bubble, meaning that they’re probably right on the blockchain side. But it’s the time scale of when they’re right that’s gonna really matter. We’re going through a process at Lucid today of trying to partner with as many blockchain companies out there to truly understand their approach. As a marketplace, we want to support different products and services that help improve the value of research. And if blockchain can reduce fraud, if blockchain can reduce or increase uniqueness or improve incentives, there are lots of mechanisms where if the blockchain thesis is correct, the value of a survey increases. And so we wanna support platforms and technologies that do that. And so we look at ourselves as being an engagement or a scaling process for blockchain versus us trying to solve it ourselves. When will it become important is a different question. And I just don’t know the answer, if it’s next year or five years from now, but I do believe that blockchain will become an important part of how we deliver and execute insights across the board.

[00:28:20]

What’s interesting is where you guys sit in context of most other companies in the sample space is you have visibility of the individual respondents. So it seems like it creates this interesting intersection point of visibility across that individual person. And to that end, it seems like it would make sense if there was blockchain developed inside of your panel, for brands to start incorporating their panels. I call them panels. What I mean is their customer lists, really, to be able to deploy alongside of what you’ve got going on.

[00:28:57]

If a brand could add their CRM data with confidence and with security and privacy built in and then be able to tie that back to insight data seamlessly, so everything that, all the data’s been collected via CRM or point of sale now becomes part of the data stream of every single survey moment that you create, whether that survey is for custom research but also for CSET. It all is tied together but also protected from that, from data leakage, because one of the big challenges if you start using your CRM database and opening it up to a lot of different providers in order to expand your data insight ability, there’s data leakage. But with blockchain, there would be no data leakage because of the nature of the blockchain itself.

[00:29:43]

There is a taller hill with more sand, my friend.

[00:29:50]

Well, you probably would not be surprised that our friends at IAAX and Lindy have helped us understand that we can play a role not where we try to solve for blockchain, but to support the blockchain providers and how they’re approaching the broader marketplace. And I’ll go back to we’re blockchain neutral. We have no desire to “pick a winner” or to create a blockchain company who are programmed within Lucid. Our goal is to increase the value and trust of the marketplace, and if a blockchain platform does that, then it’s very important for us to scale it very quickly. So that’s the scenario where if suddenly fraud can be reduced or the value of the data stream in a survey could be increased, then it’s in our best interest to help that technology, whether it’s blockchain or anything else scale pretty quickly. And we are in a unique place in the industry because we run the marketplace, and we have a ton of information around all the different respondents that are taking all the different surveys globally right now.

[00:30:56]

If I understand the technology correctly, I don’t think that there could be 20 winners in this space, right? So there is definitely an opportunity for a king-maker play here. So it’ll be neat to see how that all unfolds.

[00:31:07]

I don’t know, honestly. I honestly don’t know how all that would play out, and if there’s a winner-take-all blockchain scenario, or if there’s a scenario where different chains actually focus on different parts of the problem set. I do think one of the challenges of a lot of the blockchains kinda pitch decks that I’ve seen is trying to be all things to all people and tackle everything. And I believe that’s more likely that traction will occur because different parties will focus on different aspects of the value chain with this technology, and then it will scale from there. Honestly, I think that it will be quite impossible to “king make” this early in the evolution of blockchain and insights.

[00:31:49]

For sure. And a little bit more on this subject. The economic model is gonna be interesting too because that kinda gets to my point of while you could have lots of specific use cases, everybody’s gotta get paid as we all know.

[00:32:03]

Apparently everyone wants to get paid.

[00:32:05]

So anyway, so I wanna talk a little bit about what you’ve got going on. What do you have that is adding a lot of value to the marketplace? Both in terms of from brands’ perspectives as well as agencies.

[00:32:18]

So one of the big things we’re really focused on right now is helping our clients better understand the quality of the data that they actually house. And a lot of this is focused on advertising and marketing in the brand space itself. And we started working on advertising effectiveness a couple of years ago with our proof products where we could take the scale of the marketplace and apply it to helping brands understand lift and effectiveness of their work. And in doing that and in analyzing the data with them, we quickly realized that one of the biggest problem areas is not how good is my creative or how well am I executing the campaign. It’s that data that I’m using to target in the first place is terrible. It’s like the open secret in advertising is that the data itself isn’t very good at targeting, and there have been a number of articles that had come out that it’s, there’s a 50/50 shot of everyone’s ability to target just for gender, which is funny because we almost have a 50/50 shot even without trying any targeting. So our clients on the agency side and on the research side have really been focused on, how do we know that the data that we’re using, whether it’s our internal data, our third-party data, is actually targeting for the right audience itself? Before we even go into a campaign, before we even start. So we’ve built a whole product, which is getting a lot of momentum right now, which we call data score. And we go ahead and test using our platform whether or not the audience in your data set is who you think it is. A simple example would be a luxury car intender. And sometimes these data sets are created from a lot of math and a lot of behavioral models where people have clicked on certain ads or searched for certain things or been to certain Web sites, and they’re bucketed as luxury car intenders. And the number of luxury car intender cookies or identifiers that you can purchase far outnumbers the population in the United States. And so there’s obviously something wrong here. So we literally take that data set and we ask the people. We have one of the largest platforms in the planet that can ask people questions at scale. And so we use that capability to measure, of that data set, how much of it is the audience that you’re looking for against what you would normally expect in the population? And we’ve gotten a huge amount of support from the ARF, from our D and P partners who wanna be able to differentiate quality on their platform between data sets. Obviously the brands want to be spending the money in the right way with the right audiences. And agencies wanna be able to demonstrate to their brand clients that the work that they’re doing is actually improving. And so taking a data set and measuring it is a way for them to pick a data set that’s gonna perform better than based upon the brand associated with it. So this data score product is building a lot of momentum. You’re gonna see more and more work on that along those lines over the next coming months in the back half of the year as we scale it up.

[00:35:32]

Very exciting. My guest today has been Patrick Comer, founder and CEO of Lucid. Patrick, thank you very much for joining me.