Alex Lirtsman — CorralData | Why Most Companies Are Only "Data-Adjacent"

Alex Lirtsman spent ~15 years solving data problems by hand for brands like the NBA, Pfizer, and Sweetgreen — then sold his agency and built CorralData to automate the entire thing. In this episode, he makes the case that most "data-driven" companies are really just "data-adjacent," explains why the metrics leaders celebrate are often vanity metrics, and walks through what it takes to move from read-only dashboards to AI that actually takes action.

You'll Learn:

• Why most companies are "data-adjacent" instead of data-driven — and how to tell which one you are
• Why appointments, leads, and ROAS are vanity metrics, and what to optimize instead (LTV:CAC by channel and location)
• How bridging EMR, CRM, call tracking, and ad data unlocks profit you currently can't see
• What "we need AI" really means — and why effectiveness matters more than efficiency
• How to move from read-only dashboards to AI that acts, safely (guardrails + human-in-the-loop)
• What a PE-backed med-spa platform changed after unifying its data
• Why brand and data are the only two assets you can't rebuild

Chapters:

00:00 - Intro: who is Alex Lirtsman and what is CorralData

01:07 - The one thing he wants you to do: just get started

01:47 - From digital agency to automating data ("Shopify for data")

02:47 - Why he turned 15 years of consulting into a platform

04:06 - Why every report and dashboard has a dead end

05:33 - What changed - technologically and culturally - to make this possible

07:41 - How CorralData actually works: connect, warehouse, semantic layer

10:32 - Beyond dashboards: talking to your data and acting on it

12:26 - The hidden value in EMR/ERP data - and bridging systems

14:55 - Data-adjacent vs. data-driven

17:23 - What teaching at Columbia/NYU taught him about usable data

18:54 - From read-only AI to autonomous execution

19:55 - Guardrails: just because you can doesn't mean you should

22:10 - Where leaders go wrong when they say "we need AI"

25:27 - Case study: a PE-backed med-spa optimizing appointments vs. revenue

29:15 - How long it takes to unify EMR + CRM + paid media

32:47 - What changed first once the data was connected

34:40 - ROI: customers growing ~7x faster

🎧 Listen on Spotify: https://open.spotify.com/show/3OlTNvh2FGlE4VJyCrMJVE

📺 Watch on YouTube: https://www.youtube.com/@JustCuriousAI

🔗 More Expert Interviews: https://checkpluris.com/expert-interviews

Proof of Work is the podcast of Pluris, a platform connecting investors and operators with the world's leading applied AI experts. Each episode features the builders, operators, and investors who've actually put AI into production, turning it from buzzword to bottom line through sharp case studies and practical conversations. We explore how AI is used to grow revenue, expand margins, improve operations, and create measurable value inside real businesses.

Learn more at checkpluris.com

Explore more interviews and connect with experts at https://www.checkpluris.com

Subscribe to our newsletter at https://proof-of-work-ai.beehiiv.com/

Transcript

SPEAKER_00 0:00

Today's guest is Alex Lortzman, founder and CEO of Crowd Data and a 20-year leader in data, digital strategy, and AI transformation. Alex works with healthcare organizations, consumer brands, and private equity firms to unify fragmented data and turn it into real-time decisions and autonomous action. Today you'll hear why most companies are only data adjacent, how to connect marketing spend, actual revenues and profit, and what it takes to move from dashboards to AI that actually executes. Alex, welcome to Just Curious.

SPEAKER_01 0:29

Thank you for having me. I really appreciate it.

SPEAKER_00 0:31

What do you want listeners to walk away with from this conversation?

SPEAKER_01 0:35

I mean, I just want them to get started, right? Like I think the world is moving so quickly. And I think there's a lot of like a little bit of like fear of just like I think the easiest thing to do is just get started, right? Like start experimenting, start tinkering. I think that's like I guess like I've been teaching for a really long time as well. So it's how do you get someone to like actually change or do something? Just get into it versus reading about it, hearing about it.

SPEAKER_00 1:05

Just do it. Just do it. For those who don't know you, who is Alex Lurtsman and what is Coral Data?

SPEAKER_01 1:13

So uh my background is uh started in computer engineering, changed my major a bunch of times, went through sort of like a traditional banking background early on in my career, and ended up starting my own digital agency. Ran it for 15 years, uh, sold it to a large consulting firm, and uh decided that I'm gonna take some of the learnings from having people do this for large organizations, and we're gonna automate all of that. So the entire process of how do you work with data, right? Like how do you set up a data warehouse? How do you put the put data into a data warehouse? How do you build reporting? How do you make it actionable? And then lastly, it's how do you act on this data? So just my background has been doing this by hand. And over the past five years, we've really been focused on well, how do we automate all of these disparate systems and processes and do it in an autonomous way?

SPEAKER_00 2:08

Yeah. As you noted, you used to solve some of these problems manually through consulting. What made you decide to automate the entire process and into a platform?

SPEAKER_01 2:18

Outside of a non-compete, that I'm not allowed to do services work for a while. I think the reality is that, you know, the only organizations that were becoming, I would say that had a shot at actually using data to make really smart decisions quickly, disseminate those decisions across their organizations, were the big tech companies, the very large banks, and some sort of like tiny instances. I think it was just really hard for large organizations to be able to do this and small organizations as well. So the goal was how do we do for data what a Shopify did for e-commerce or MailChimp did for email, right? Like, how do you put a bunch of these pieces together and make it all work and make it all work in a way that doesn't require a PhD?

SPEAKER_00 3:06

Yeah. What did your time at ReadySet teach you about how companies actually use or misuse data?

SPEAKER_01 3:14

I think it's everybody sort of like has this, like, if only we could see this, we can make better decisions, or only if I can have this report or this dashboard. And I think like what I've sort of realized is you give somebody data, they have more questions, right? It's sort of like this like constant search for knowledge, right? And I think as human beings, we're uh, or at least like hopefully we're like intellectually curious. And then if you're an intellectual cure, if you're an intellectually curious person, giving them something that has a dead end and a report or a dashboard always has a dead end, um, is actually sort of like counter uh intuitive to the types of uh employees that organize the top performing uh employees in an organization. Because the second you give somebody an answer, they should have a few more questions. And that process is like that process has just fundamentally needed to change, right? And so my focus, my focus early on in Corral was well, how do we automate this so that lag time between, oh, I have a follow-up question or I have an answer? Like, how do you shrink that? How do you remove as much friction from the system as possible by consolidating everything and just giving people answers, but also feeding into their intellectual curiosity versus fighting it?

SPEAKER_00 4:36

Yeah. What what's changed technologically over the last three or four years that makes Corral possible? I guess on the one hand, there's more data than ever. And then you also, through AI, have the ability to kind of synthesize it and ask questions of it. Like t tell us about the kind of unlock that has created this opportunity for you and also people who you work with.

SPEAKER_01 5:00

Yeah, I think some of the some of the unlocks are obviously technological. The ability for us to be able to process vast amounts of data or create data pipelines uh and data infrastructure for some of the world's largest organizations. In the past, this would be such a gargantuan effort to be able to monitor this, to be able to then use that data in an incredibly thoughtful way to give people the most relevant uh answer, not just an accurate answer, because I think accuracy is uh more or less a salt problem, but an accurate but also a highly relevant answer. This was just nearly impossible, even a year ago, let alone like three or four years ago. So I think that that's that is just fundamentally like different, just the ability to synthesize a ton of data, do it incredibly quickly, and do it in a way that we could also have a massive flywheel effect double, right? So we give somebody an answer, we're learning from this, right? Like we're constantly in this like iterative stage of a flywheel that's learning. But I think the other piece of this is like culturally, there's this uh people are becoming like tinkers, right? Like they don't want to settle for uh for the status quo. And I think that's incredibly energizing because that was sort of we were talking about this friction like four years ago when we were like first starting Corral Data, just like we want to remove this friction, and now everyone's like, I have uh Claude or I have Chat GPT, they're removing that friction already, so they really get it and it could really resonate with them.

SPEAKER_00 6:33

Yeah. Can you help me and listeners, viewers, like conceptualize like how Corral works? I know we're gonna walk through a case study, but like we talk about data. What sorts of data? How do you kind of bring it together? Like, how does that work? And then also, like, how do you make it more than a dashboard?

SPEAKER_01 6:53

Yeah, yeah, yeah. Um, and obviously, like dashboards are important, sort of not knocking dashboards, and in Corral you get dashboards out of the box. The way that the entire process works, as a user at least, is you log into Corral, you connect all of your data sources. So it could be your EMR systems, it could be your Google Analytics or Adobe Analytics, it could be your Netsuite, your Sage Intact, it could be marketing, finance, operations. We don't really care. And if there is an API, our robots will go out and actually build the integration for you. So that's number one. Number two is we put this data into a dedicated uh data warehouse. And for some of our customers, especially our largest enterprise customers, they might have their own data warehouse. So that data goes to their data warehouse. And then the next piece of this is we essentially figure out based on all of our knowledge and experience today, how do we how do we sort of like start to do two things? One is understand the semantic layer of like, what is every table about? What is every column about? How should they be joined across multiple tables? Like there's a lot of work that goes on in just unifying, transforming, doing the data hygiene, data cleansing of that data. And then the second piece of it is how do you do like this, like almost like modeling of all of this data? Because we're in the industries we work in, the learnings from one company in terms of how we do data modeling is now uh applicable to others. So we work a lot in healthcare, we do a lot of work in healthcare. So our learnings from uh med spas or physical therapy or behavioral health, and sort of like you name it, like all of these verticals have maybe different definitions for different things and different EMR platforms. And all of that modeling isn't something that like you usually find by Googling something or asking Claude. They you find it by uh you find it by users actually using your platform, and that sort of this flywheel allows us to add customers really, really quickly. Or something that typically takes, you know, like these integrations. Like if you want to build a Power BI or a look or tableau, and you want it to sit across all of your various platforms, like your EMR systems, your Net Suite, uh your ad platforms, you want it to all work together. This is like a six-month to a year effort, and on our end, this is like uh a solved problem. You know what I mean? And so all of that, we then do two additional things, right? So it's all the reporting, which I think is still has some value because that's how people are you trained to think about sort of their data. And then the second thing is just like being able to talk to this data, and not on me, how is my revenue uh month over month or what is my LTV to CAC by channel? I mean, how do I optimize my paid media to be able to grow my LTV without sacrificing any revenue, knowing that blah, blah, blah. Like you could go absolutely, you could give it all the constraints, and it's not just about what happened, it's about sitting uh uh having data that is pre-aggregated, easily digestible for uh for the robots to for the large language models to now act, not just sort of not just sort of like answer your question, but now also what we're starting to do is letting customers act on this data. So we give you an answer, but we also give you recommendations on what you should do about this and prompt you if you want to act on it, right? Like, do you want to change people's schedules based on what we're seeing in terms of demand forecasting? Do you want to change your campaigns? Uh, do you want to change sort of like your uh follow-up emails, like anything you could sort of imagine that, you know, like if you go to a large language model, you asked it to do this, but now we're doing this in uh in a secure governed way. And in healthcare, it's also a lot about like uh uh HIPAA compliance as well. Like, how do you do this and not share more PHI data than is absolutely necessary when you are doing any shares? How is everyone have a business associates agreement and being able to track all the way this all the way through?

SPEAKER_00 11:05

Yeah. Are there common data sources that have surprising value creation opportunities around this? Like data that, you know, wasn't being sort of properly analyzed before, but through your robots is now actionable? Uh you know, is it is it unique to every situation, or are there some like pockets of data where you're like, holy smokes, like this actually can be like incredibly valuable. We just never were able to see it before.

SPEAKER_01 11:35

Yeah, I would say one is on its own. I think EMR systems have like a wealth of data. Um ERP systems have a wealth of data that is just like not easy to mine, even financial systems like sort of like that are not easy to mine. But I think like that that's that's not a really hard problem. I think like the bigger opportunities when you sort of like bridge the two together. So if you take an EMR system and a Google Analytics and your meta ads and your Google ads, and you sort of bridge all of this together, now you're talking, now you're really cooking with fire, because then you're able to answer a question that is fundamentally has been impossible to answer, which is like, what is what is my LTB to CAC, right? By campaign, like I mentioned. And even like simple dashboards, like being able to answer this question is really, really hard. And I know it sounds really easy, but it's really, really hard to connect a bunch of systems together, especially in a healthcare environment. And even outside of healthcare, uh, we have a bunch of massive retail customers that are spending hundreds of millions of dollars on the media and they still don't understand not everything is trackable, not everything is sort of like measurable. So, how do you how do you allow the robots uh to be able to have this incredibly digestible data that they can just now start to like connect the dots on and say it's like, hey, actually, I know uh I know it says that our uh meta roas, but in reality our meta roas is actually five because what you've been attributing to this campaign should be like just that level of like nuance when you connect everything together is just like it's almost like putting glasses, like putting glasses on before you could see.

SPEAKER_00 13:22

Yeah. You've used the term data adjacent versus data driven, and maybe you've kind of touched on like what that is, but you want to build on that at all? Like what what is being kind of data adjacent look like and what is data driven look like?

SPEAKER_01 13:37

I think data adjacent is like everybody, everybody wants to be data driven. Everyone thinks like even some organizations think that they're data driven, but like what they're really doing is like they're looking at a report, right? Like, and so they're very adjacent to like what is like they're very like they're sort of like adjacent to this problem, and they're still they don't really trust the data, you know. Like I think until you have like complete trust, you're always going to be data adjacent, right? Until you have until nuance is baked in. I think that's the thing is like a lot of times I'll speak to especially like private equity portcodes or even private equity firms, is yeah, but sometimes like we want to go with our gut. And what that tells me is like, and I think I go with my gut a lot too, right? I think a lot of times for data decision making, you need to be able to build in the nuances, the context to make sure that when you have data that is contradictory to your point of view, that all of that has been baked in, right? Because every single time that happens, what you do is like you sort of you have a little bit less trust. And that sort of like ends up building over time. And I think as an organization, though, where it's really dangerous is when eventually that trust in the data erodes and then you become sort of this data adjacent, no matter what. So that's where like we're so focused on context and nuance, right? Like the only way that you can provide people with good, not just sort of like accurate recommendations and answers and actions, but uh helpful, right? You need actual context and nuance. And I think that that's a really hard thing to do. I think that's a really hard thing to do when you just like, I'm just gonna drop a spreadsheet into Claude and expect an answer. I think that's that's hard. Sometimes I think it's substantially faster than doing anything yourself, but context and nuance still matters.

SPEAKER_00 15:35

Tell me about your time teaching at Columbia and NYU and Baruch and how that has informed how you think about making data usable for non-technical operators.

SPEAKER_01 15:47

I'm trying to like sort of like draw some parallels here, but it's just I mean, I think I think teaching is really presenting information, right? Like, so I don't know, like at the end of the day, uh my goal is how do I present recommendations? I think uh we sort of went from like present, like I think we started with like answering questions. I think most of our customers, they're sort of like they've moved on from like, I need an answer to what's like to it's like what should I actually do? Here's a challenge, or there's a challenge that we've already spotted. What should you do? Um, and I think a lot of it is like to that point around context, presenting this information, making it hyper-relevant. And I think teaching is the same thing, right? Like when you're teaching, you you have to provide enough relevance for somebody that doesn't work in this field to immediately understand what you're saying, be engaged by this, and then want to actually dive deeper. And I think like teaching students is no different than presenting information. You are just driving somebody to engage deeper with the content.

SPEAKER_00 16:55

Yeah. You were uh talking earlier about moving beyond insights to actually taking action. What does that shift from kind of read-only AI to autonomous execution look like in practice?

SPEAKER_01 17:09

Absolutely terrifying in some ways and absolutely like interesting, terrifying, a bunch of whoa moments, and it's like, oh, we probably shouldn't do this. So I think it's like uh you sort of go through like a range of emotions when you start to see some of this. What we've been adding is when we started Corral, a lot of it was just like, well, we'll connect your data and we'll tell you what what happened, and then we're like, oh, we'll tell you what to do about it. And now we're like, we'll provide you recommendations that are like personalized to you. I think right now a lot of it is like, well, we could change people's schedules, we can change your app campaigns, we could pretty much do anything because not only do we have read access, but in some instances we have right access. So I think it's because you can do something doesn't mean you should. So I think one of the things that we're sort of like trying to figure out is how do we provide the right guardrails when uh agents can pretty much do anything to make sure that, well, we've learned from the recommendations that we've given you in the past what you're comfortable with. We're giving you better recommendations. Now we're allowing you, as the human in the loop, to act on the recommendations we're giving you right within our platform without leaving, right? Like you want to change somebody's schedules, great, based on our recommendations at campaigns, great, go for it. I think one of the things that we're trying to figure out is like, what is the level of autonomy that we want to give these agents? Because like we don't want them making medical decisions, we want them making sort of decisions that can impact somebody's livelihood, right? So, like, and then how do you sort of like draw that line? So I think one of the things that I would say that the bulk of our RD right now is on this autonomous piece of it, is like, I think the autonomous problem has been solved, at least on our end. I think the problem that we are solving right now is when somebody decides to have our agents do something. How do we do like a double check on this? Is like, well, are you sure we should do this? So, because you can set them up to do a lot of things, and we want to make sure that people are being very thoughtful and mindful about sort of like that kind of stuff. So we're sort of starting to roll that kind of stuff out. Like, well, we don't we are comfortable with you changing ad campaigns, but not ad campaigns that are impacting more than X amount of dollars. We are comfortable with you changing scheduling, but only if you're doing this a few days in advance so people have a heads up around it. Like, so like those are the types of guardrails that we're putting in here. And uh also like we're we're actually trying to slow down, sort of like uh in some ways where we have the tools, and now it's how do we provide like both the user experience and the context now to the end user to make smart decisions with it with that information.

SPEAKER_00 20:03

Yeah. On AI, when when leaders come to you and they say we need AI, where do they most often go wrong out of the gate?

SPEAKER_01 20:13

I mean, probably sort of asking that, like stating that question, and uh we need AI, I think it's more uh I think that's like probably the first place. Look, I think sort of at the end of the day, people just want what they say they want AI, what they're actually saying is they want efficiency. They want efficiency and they want effectiveness, right? And I would say effectiveness is probably even substantially more important. I would say maybe a year ago, the questions were our board says we need AI, or our uh P firm is mandating we need AI, but like nothing's really changed. But uh 10 years ago, they were still driving uh for uh operational effectiveness. We just have the we have the tooling now that is almost like a magic wand to drive operational effectiveness. And that's like the area where we play a lot in. So I think a lot of times when they come and they say they want AI or sort of like a derivative of that phrasing, I think what they're actually sort of saying is like, how do we upskill our employees? And then how do we do this in a governed, safe, and easy to adopt way, like in an effortless way. And those two things are sometimes polar opposite, right?

SPEAKER_00 21:23

So yeah, can you add up on that?

SPEAKER_01 21:26

Yeah, so like I think if you want something that's like locked down, safe, secure, governed, like usually this is like, well, this is just like how this project's going to die. And I don't think it has to do, I don't think that's the case anymore. I think where we sort of made a nice niche for ourselves is being able to do all of that and doing this effortlessly, right? Because I think like sort of that magic of AI, it's like having your cake and eating it too. And I think usually when customers come to us. That is essentially what they're looking for is this van diagram of like, I want I want to upscale my team, I want this to be absolutely effortless and non-effortless for our organization to adapt top to bottom. Because if you think about sort of uh how we started working with AI agents as uh on a personal level, it's just like chatbots, right? And chatbots have like a one-to-one relationship, right? And when organizations are coming to us, they're sort of thinking about this as like, well, we still need to have a one-to-many relationship. We still want to make sure that it's governed, that everybody's getting the same answers, that the data is modeled exactly the same way, that somebody is two different people are going to get the same insight because we have the same exact organizational context, right? And I think that that's like, and we want to make this really easy, right? Like we so I think that's like the place where uh where we have a lot of these conversations right now.

SPEAKER_00 22:55

Yeah. I'd love to walk through uh an example of how you work. You shared with me an example of a med spa private equity fact med spa client who came to you with um the problem around having kind of optimized for appointment bookings. Um tell me a little bit more about that story and you know what the problem looked like and how you all help them um solve it.

SPEAKER_01 23:25

Yeah, yeah. And like, and obviously for context, like we work across a bunch of industries, but we like uh healthcare and consumer. And then healthcare, we uh we focus on sort of like a lot of these verticals, like MetSpa being one of them, because it is uh there's a lot of uh P attention and we work with uh a lot of P firms. In an organization like the one you're referencing in the case study, it's very similar across that entire industry, which is a P own firm owns 50, 80, 100 MetSpa locations, right? And what they typically do is they uh the port goes and their marketing agencies, they'll end up optimizing towards appointments, right? Because somebody comes to your website, it's not an e-commerce site. It's it's sort of like a promise that somebody's going to show up, right? So now the marketing agencies and Meta and Google are actually optimizing towards somebody clicking on a link, putting in their information, and selecting a calendar date. Whether they show up or not, you don't know. And there's typically a 30% no-share rate, right? And what they spend when they show up, you also don't know. So are they coming in? Is this sort of like a one and done? Are they going to be a loyal customer? Are they going to subscribe to some? Are they going to become a member? Like, what is the like what are the treatments that they're signing up for? Like, there's so much context that you don't know when you're optimizing to an appointment. And the bulk, absolute bulk of the value creation is that delta between optimizing to an appointment and optimizing towards actual lifetime value that you're going to get from the from this patient, right? And this applies across any verticals or industries we work with. But that requires connecting a bunch of different data together that requires processing, that requires processing a lot of this data. So there is no simple way of doing this by hand. Um, there's also literally regulation against doing this by hand, because if you're doing this by hand, you're downloading, uh, you're sending spreadsheets to your agency, or your agency is downloading data from your EMR system, and you're trying to match things, and this whole process is impossible. And then, let me complicate this slightly, is so you know what campaigns are performing, how you optimize LTB connect. Well, now you want to send this data back to the app platform. Well, how do you do this also in a HIPAA compliant way? So I think like uh a lot of this is just like being able to do this in the same in one in one platform from beginning to end, and having that complete visibility, and then being able to act on that data by sending this to uh the paid media platforms. And that's like a very, I would say like a fairly easy use case for our technology that doesn't require setup. But if you wanted to do this, let's say even like two or three years ago, just it's a nearly impossible problem to solve two or three years ago.

SPEAKER_00 26:24

Yeah. And so how long does it take to unify the EMR and the CRM and the paid media and the call tracking into a single system? What does that like look like, I guess, in this example?

SPEAKER_01 26:36

So it depends on how many years of data we need to sync down. I think like the most uh most time consuming process for all of this is in data synthesis. There's two time consuming aspects. Uh number one is data sync. So, right, you're syncing data from like an EMR system, but there's a I had a weird little thing come up. So you're syncing data from an EMR system, or you're syncing data from even something like a clavio. Doe or a hop spot, these things can take sometimes a few days to sync the data down the first time around. And so I think that that's one of the things that takes a long time. And then the second thing that takes a long time is like getting in people's heads, right? So, like we provide an onboarding team. Theoretically, with Corral, you don't need an onboarding team, but why we provide an onboarding team is to essentially ensure that we could get inside your head and try to understand your organization so we could give that context, whether it's custom instructions, any modeling, and the our AI writes all of these like models and everything else. But like a human has to be like, hey, what about this? Have you guys thought about this? It's like, oh yeah, well, who does this right now? Okay, can you make sure you add this? Can you make sure you have a conversation with our agents about how this process works or how this process currently works, or why you call why you uh measure your LTV this way, or you why you measure your rebooking rate that way? So a lot of these types of conversations early on are sort of like this onboarding process of getting inside of people's heads to get this institutional knowledge to be able to put that into our platform. So when somebody asks a question the first time around, they're like, it's a jaw-dropping moment.

SPEAKER_00 28:20

Yeah. And so what did you see when you did that and you unified all this data? You you kind of touched on this at the beginning, but like what did you see in terms of their process that was broken or a blind spot?

SPEAKER_01 28:34

I mean, I think like anytime you are looking at one thing, you it's a telescope, right? Like you can you, if you're hyper focused on the problem, you can miss the forest for the trees. And I think the way that like our job by unifying all of this is something it's like, well, actually, that's you can't optimize your media for this, or you can't optimize your operations or for this. Like you let us, we have a additional context, sort of like that an individual employee at a company might not have, right? And I think a lot of times, like even at our own company, like I'm still the glue, like a lot of times the glue of just like, hey, you should go talk to this person uh in uh you should go talk to this person in our data engineering team, or you should go talk to this person in our uh in our strategy team. And like a lot of times it's sort of like this glue. I think very similarly with uh data, it's like somebody's asking a question, not having all of the data in one place and being hyper focused on a problem without sort of like seeing the forest for the trees.

SPEAKER_00 29:42

Yeah. And and so once like everything was connected, what what changed inside the organization?

SPEAKER_01 29:51

Yeah, like in this specific case study, it's well now they can optimize cell to be the cack. You literally, and now you can predict uh you could predict your booking. So now you could optimize your scheduling. Now you could also understand which look where should you be opening new locations, right? Like all of these things get uh unlocked. It's again like almost like this flywheel effect of just like, I just unlocked A, B, C, and D, right? And those things are you you start to really run an operationally sound business. And I don't just mean this from like your return on that spend goes up. Your profitability, your profitability starts to go up when everybody in your organization is empowered to do the right thing at the right time and it's hyper-personalized to them.

SPEAKER_00 30:37

Yeah, I was just thinking that like everyone has a business equation, but they don't know what the coefficients or the even with the variables might be in that business equation. And so like what you help them unlock is like what that equation actually looks like.

SPEAKER_01 30:55

That and that equation changes too. I think that's the other piece of this, right? Like it's that equation changes based on uh seasonality, that equation is uh changes based on competition. Like I think that the other thing about this is like how do you have an ever-evolving equation based on all of the forces, uh positive and negative on an organization? Yeah. And all the decisions that an organization makes, not because nothing is in the vacuum, right?

SPEAKER_00 31:25

Yeah. Well, what is what is uh, and I I don't want to pin you on a specific number, although it'd love one. What what is an ROI on something like this look like?

SPEAKER_01 31:35

Well, I'll tell you this much. In we just did a study with in that specific space, customers that use our platform in that space are growing seven times faster than customers that don't. And obviously, it could be like a self-fulfilling prophecy, right? Like I'm a data guy, like so, like the follow the policy to that is that people that use our platform are probably substantially more efficient, and they're sort of using platform, uh, they're using our platform because they that's how they think already. So they're growing already like that. But that is sort of a number that is that I'm very proud of. It's just our customers are growing seven times faster on a single location basis. Across uh in that industry, it's seven times. Some of the industries are like anywhere between like four and eight, is sort of like a typical standard. And we're talking about the growing faster than the industry average, right? And again, there is uh something to be said about the customers we work with are probably hyper focused on growth and efficiency and effectiveness. But that does tell a pretty compelling story of why you would want to leverage whether it's using us or however you want to do this, but leveraging your data to make smarter decisions organizationally, not just giving people claude and saying go for it.

SPEAKER_00 32:55

Yeah.

SPEAKER_01 32:56

Well percentage of you should absolutely give people claude and say go for it.

SPEAKER_00 33:00

Yeah, totally. Well, what percentage of private equity backed multi-location businesses do you think use some kind of data platform or organizational visibility platform like this?

SPEAKER_01 33:14

I don't know. I like it in some uh industries a lot. I think it's where there's a lot of consolidation, right? So in industries where I mean, in the industries we operate in, we typically are the market leader in some of these industries, right? So there's no it's like the and not only are we the market leader, like most customers use us. So yeah, most customers are using a platform, right? Uh I think that changes depending on sort of the industry and how what type of P firm it is, right? If it's something like Dental, which has had a lot of consolidation, maybe a little less so. If it's like MetSpa's Behavioral Health, those types of industries, I think a lot more so. So again, it depends on the industry. And then we have a lot of retail customers, right, that are not in healthcare. And where they are using a platform like ours, it's really sort of a matter of uh, it might not be a matter of growth. A lot of times it's a matter of like survival, right? It just they need to get their house in order. And so we're seeing it sort of like the other way around, right? Especially in uh in some of these other industries where you absolutely need this so you could survive versus just grow.

SPEAKER_00 34:27

Yeah. Let me ask you a couple questions as we come to a close. One one is on private equity. You work closely with private equity firms. As you noted, that case study was a private equity-backed med spot company. How does the presence of a PE sponsor change how quickly or effectively companies adopt kind of data and act on it?

SPEAKER_01 34:49

I mean, I've seen probably like only one time that a deal got like, you know, like, I would say like a deal got delayed because of a PE firm. I think a lot of times it's an accelerant. Yeah, like from my perspective, I have not seen a PE firm sort of look at this when the portco brings us to them and say, we shouldn't do this. I think it's like, how soon can we do this? And then similarly, we get brought in by PE firms to portcos. And yes, like sometimes uh everybody's like hoarding their, you know, sort of like their speed dums, and we don't want to share our data with our co uh with the uh with the PE firm. Uh but I would say for the most part the relationship has been uh an accelerant. Um, because I think everybody, especially if that if there's alignment between the PE firm, if there's alignment between the PE firm and the portco, we move very quickly.

SPEAKER_00 35:48

Looking ahead two to three years, what'll separate companies that truly win with AI and data from those that fall behind?

SPEAKER_01 35:57

I mean, I think you sort of it's data, right? Like it's sort of at the end of the day, like the models are getting smarter and smarter. The differentiation is uh for companies, it's brand. It's brand and data. That's it. Like there's nothing else, right? Like anybody can uh on our end, we're hyper focused on ensuring that the only way that we are able to do what we do right now isn't uh I think we have an amazing, beautifully built, very well engineered software. But at the end of the day, we win because there's a data flywheel, right? And I think the way that companies should be thinking about this is there's only two things that you have that are truly propriet, like truly ownable anymore. It's like it's your brand and your brand reputation and how you treat your customers and sort of everything else. So, like, so I think that's one, right? Like, and then the second thing, it's it's your data, right? Like, so a brand is a promise of an experience. So whether it's in healthcare or retail, are you living up to that promise, right? And then in data, it's do you have data to can constantly sort of like iterate and provide a better, uh, a better offering to your uh to your consumer or your patient or whoever it is, if you're in B2B to another business, right? I think that's that's how you win. And everything else is everything else is sort of um you could rebuild pretty much anything, but like data and brand, those are two things you cannot, those are two things that you cannot rebuild. Those are two things that somebody can take away from you.

SPEAKER_00 37:28

Alex, amazing. I appreciate uh your time. It was a great conversation. I really enjoyed it. Uh, thanks for coming on, just curious.

SPEAKER_01 37:35

Thank you so much. Thank you so much for having me. I appreciate all the questions.