How To Use AI To Predict Your Next Motivated Seller

Steve Trang: Everybody. Thank you for joining us for today's episode of Real Estate Disruptors. So we got Ty Garrett and Rami E with DataFlick, and they flew in from Knoxville, Tennessee to talk about how to use AI on your next motivated seller. I am on a mission to create a 100 millionaires, and the information on this podcast alone is enough for you to become a millionaire in the next five to seven years. If you take consistent action, you'll become one.

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And if you get value out of the show, please tag it from below. Share this episode right now. That way we can all grow together. And this is a live show, so please ask your questions for Rami and Ty to answer. And I've been talking to him for even an hour before the show.

They're a wealth of information. Make sure you guys ask your questions. Alright? You ready?

Rami E: Let's do it.

Ty Garrett: Let's do it.

Steve: Alright. So first question is, really, what was your life like right before real estate?

Rami E: Got it. You can actually take the.

Ty: Sure. So, it all started. We were working in a wholesale operation with, Tiffany and Josh and I, over at Hills Homes who now own Results Driven. So we were doing that, and we started back in 2019 just on the wholesaling side. Right?

And we were doing everything from, like, flips all the way to a lot of wholesale deals, building rental portfolios, all that good stuff. I was running dispositions for them and overseeing rehabs, project management, things like that. Rami was, working on the acquisition side.

Rami E: Yes. Follow-up quickly pushed into acquisitions. It was pretty abrupt, but it was solid. You've learned a ton that way.

Ty: For sure.

Steve: What what attracted you guys to go work over there?

Ty: Yeah. So before we got started, so I was this is back in, like, 2018. So I've done a ton of, like, educational programs, courses, things like that, and I realized pretty fast that, like, I couldn't really do it alone. So I was posting a ton in, like, different masterminds that we are part of, like, courses, things like that in in just communities in general. And so I tried to launch marketing by myself and got a little bit of traction.

Like, I did bandit signs and things like that and realized pretty fast that, like, I needed help. So I started posting in all these communities and things, you know, to ask for that help. And Tiffany and Josh actually, you know, just offered me a job, and they were like, hey. Do you wanna come up and move from Knoxville to Columbus to kinda, like, break this down and, like, learn more about it? Three months into doing that, that was when I called up Rami, and I was like, hey.

You wanna learn about real estate?

Rami E: Yeah. So for me, it was a very different story. Graduated with psychology from UT, did landscaping for six months after that, so completely unrelated.

Steve: Landscaping as in, like, you were the one that was trimming or you're working at the landscaping company?

Rami E: Yeah. I was working for the landscaping company, but I was trimming the hedges, moving.

Steve: Gotcha.

Rami E: Like, that was the physical labor. Yeah. So I did that for six months then was like, you know, add this degree. I should try try to utilize this. Started looking at grad schools, things of that nature.

A passing in the family actually happened, so that kinda stalled that. But from there, Ty called me. He was like, hey. I know you're in limbo. This follow-up specialist role just opened up.

Come on up. Give it a try. So I did, and there we were.

Steve: So I obviously spent a lot of time talking about sales and teaching sales, and a giant part of sales is psychology. Do you find your degree in psychology to be helpful for you in any way?

Rami E: So it's actually very interesting. I feel like the main thing that I took from that, from just psychology all in all, is empathy, not sympathy.

Ty: Mhmm.

Rami E: Right? You never wanna be like, oh, I'm so sorry that happened. But or, like, I feel what you're saying, etcetera, because you're not gonna be able to Mhmm. Relate to that person that way because that's their unique experience.

Steve: It's inauthentic.

Rami E: Exactly. So the main thing that I took from it was more of a, sounds like you went through a very hard time and almost being, like, an extra value add to them. Right. That's probably the biggest takeaway I got that I applied to sales.

Steve: Gotcha. Yeah. Okay. Cool. So you're doing follow-up.

Yes. And then you got pushed moved into acquisitions.

Rami E: Yes. So

Steve: You're so this is Yeah.

Ty: You gotta break that down too.

Steve: Oh, so you're in 2019. Is it is it 2019?

Ty: Yeah. So we went to Columbus, like, right in the middle of 2019, a little bit early in the year.

Steve: So you guys are both from Knoxville? Yes. You guys both went

Rami E: to Columbus? Correct.

Ty: Correct.

Steve: Working in a sales office.

Ty: That we never met any of these people.

Steve: Okay. I know

Ty: it sounds crazy. But But how

Steve: was that experience? You know? Because, like, a lot of people actually, one thing's that I've been pushing. I actually said this on a live yesterday, last couple days. It's, like, the best way to get started is actually work for somebody.

Ty: 100%.

Steve: Because they say, like, you know, you you you watch the YouTube videos, like, stuff we recorded here. Right? You watch the YouTube videos. You can, buy the courses, this and that. You do everything yourself, but there's a lot of challenges.

And, like, there there are things that you have questions, and it's hard to get those answers

Ty: a 100%.

Steve: So what was it like? Like, how how valuable was your experience working for someone that's in the business?

Rami E: Very, very valuable. So one of the biggest things was right when we moved up, they had a very big team. It was about a month or two after being there. At this point, I don't have a lot of money. I'm living on Taj's floor with a mattress.

Tiffany actually let me borrow. Right? And he's in a 600 square foot studio.

Ty: Yeah. So So Ty.

Rami E: Very much just making it how we can, but they restructured their whole team.

Ty: Mhmm.

Rami E: So major layoffs, the remaining, like, two people on the sales and disposition are Ty and I. So that was a big just push into that acquisition.

Steve: Sales was easy.

Ty: Well, so the in the entire organization, we went from 15 to just us to overnight. Okay. And then it was like, I was heading all of the dispositions, all of our project management for, like, you know, essentially exiting, and he was doing the front end on, like, all the acquiring. Yes. Wow.

So it was absolute chaos and, like, tons and tons of hours I put in every week, but I think that was kind of, like, what expedited our learning curve, I

Steve: would say. Absolutely.

Rami E: A 100%. And they were learning as well. Right? It was felt just like a big community of all of us being like, okay. We're restructuring.

Start from the beginning. Let's figure it out. And I still remember the day, it was during our morning sales meeting. Right? We're all just talking about how we can prove, etcetera, and we get a call in.

Tiffany picks it up, made the follow-up, and she's like, Romy, you're gonna be acquisition starting today. I was like, what? She's like, yeah. I'm a pass you to my acquisition give me a minute and give me the script, and that was the first just, like, thrown into the fire. But I think that's what really drove me to learn as much as I did.

Ty: Gotcha. 100%.

Steve: So how do you go from acquisition manager and disposition manager into, DataFlick? Like, that's

Rami E: not a easy job. Yeah.

Ty: For sure. For sure. So we were working there for about a year, And the whole goal originally, I think, was just so we could go and, like, learn and kinda get our feet wet because we had a lot of well, at this time, you weren't even in the real estate space yet.

Rami E: It was

Ty: just me, like, investing in, like, all these educational programs and stuff. So as I'm going through this, I got a lot of, like, almost theory, and then we had a lot of, like, you know, experience almost at a start up, which, was amazing. And now we're like, okay. We're ready to kinda do this ourselves. And a big part of this too, like, we're super into, like, you know, friends, family.

Like, they're huge part of our lives. So we obviously are pretty homesick at this point too because we just kinda up and left. And so this was actually we seriously, seriously started considering it in November, December 2019 just because that was the holidays were, you know, kinda hitting pretty hard, I would say. And so, you know, we put our notice, and we're like, hey. Let's go back to Knoxville, try and do this ourselves.

And that was in January to February Yes. Of 2020 when we came back.

Rami E: I stayed for an extra month, kinda tried to help them get another hire to replace me. I didn't wanna leave them hanging. Right? They taught me so much. Very thankful.

Sure. And we

Ty: were helping build, like, a lot of the structure that they, you know, implemented still today and things like that. Right. Not so much.

Rami E: No. A 100%. So we came back, I believe, 02/01/2020. It was, like, the first day back, and we heard this rumor of, like, oh, COVID nineteen is going around. And we're like, where the odds that really affects us?

Yeah. And I

Ty: don't behold. I remember we were, like, sitting in our apartment and we were talking about, like, you know, is, like, could this be an issue, like, for us starting our business, like, in Knoxville and stuff. And we're like, it'll be fine. It won't it won't be a big impact. Obviously, pretty

Steve: slight difference. Yep.

Ty: So we get back. We start doing a lot of, like, different things just to, like, test and segment out. Like, hey. This is, like, working. This is not.

Like, this is how we wanna do it. And we were buying data because, obviously, you have to do you have you have to have all the data to actually launch the marketing. And we realized pretty quickly that it was super fragmented as an industry, and it was insanely expensive. Yeah. So at first, I was like, okay.

Like, let's see if we can aggregate our own data, like, build our own, you know, systems around this. So I started learning, like, code by hand and realized pretty fast that it was gonna be very inefficient for me, like, do that, try and run marketing, and try and, like, launch, like, a real estate company. Mhmm. So we made our first hire. We didn't know it was for DataFlick yet, but just, like, a hire to help us run the data side.

Mhmm. And that was just, like, scraping public resources and, like, you know, going to the county, like, getting PDFs, like, data entering, like, all of that in.

Steve: It was at this time, you weren't necessarily starting DataFlick. It was Correct. We're doing a wholesaling company in Knoxville. And while we're doing this, we need to get better data. Exactly.

Rami E: 100%.

Ty: Okay. And so now we, like, you know, build our own, like, little system, and we're like, hey. This could be this could be pretty good. And so when we're, like, doing this, we realize that, hey. We need money because there's so many, like, educational things that talk about, like, oh, you can get into this with no money, but, like, they don't talk about marketing dollars and, like, all the other expenses that kinda add up really fast.

So we were trying to find some funding to, like, get the real estate company started, but, really, it ended up turning into DataFlix.

Rami E: Yeah. Pretty fast. So the hire, his name's Gabe, still the best hire we've made to this day. Love the guy. But he was so good with compiling that data.

And even skip tracing on the back end, like, really getting that to be a cohesive process, he did phenomenal. But it was mainly all for that wholesaling company until we figured out, hey. We actually really have something on the data side that we can actually help other people out with and bring growth. I remember Ethan. So a good friend of ours, Ethan.

He was one of the first ones that we, like, helped compile data for Right.

Ty: Mhmm.

Rami E: And tried to use it like that. And he started seeing success, and that was, I guess, the unofficial start of it all. Right. But yeah.

Steve: It's that proof of concept. It's kinda like doing your first wholesale deal.

Ty: Exactly.

Steve: So What was it that you guys felt like you landed on that was different? Because you could buy that. I mean, at this time, right, was on, was coming up. Batch or they were doing really well. Yeah.

Batch is coming up. Like, there's a lot of different data providers out there. Probably radar has been around forever. What what was it that you guys felt like you guys landed on that was different?

Ty: So I learned a technique of, like, data governance of just, like, tracking, like, how accurate things were and just different techniques with Gabe on implementing that. And I started, we started buying some data from different providers, and we realized pretty quickly that the quality was pretty low and very, like, outdated.

Rami E: Mhmm.

Ty: So when I realized, like, hey. We can just pull directly and kinda build our own systems. It's way better than, like, what we were able to buy at the time. And, obviously, things have changed a lot over the last, like, three years, but, like, at the time, like, that was just the best we could do, and it was very, very affordable for us. So it just kinda, like, was a double edged sword there.

So we started doing, you know, list stacking of every degree that you can probably think of and actually doing, like, you know, hey. This person transacted. Let's remove them from the database and, like, doing a lot of, like, cleaning and stuff like that. And we were talking to, you know, Ethan, which is the guy who's testing kind of applying some proof of concept and things like that. We realized pretty fast that on the cleaning process and, like, actually optimizing the data, like, I have no idea how to do that.

Mhmm.

Rami E: We're

Ty: like, that's interesting.

Rami E: Because,

Ty: like, that seems pretty easy to us at this point. So that was kind of the first building block. And then we realized, like, when we just stack all these lists together, like, some of these list sizes are massive, and, like, we don't have the marketing output to even get close to that. Yeah. So then it became, like, how much more efficient can we really make this?

And then that was when we were like you know, I was just looking up ways to do that. I stumbled on AI machine learning. And when I was in college, which I dropped out at the time, but in the first two years, I was studying computer science and accounting. So, like, I understood, like, conceptually, like, what AI machine learning could do. So then we really started taking, like, the, you know, that whole process that I outlined and then overlaying the AI machine learning aspects at, like, the most basic basic level.

So it's like, you know, if there are five attributes of, like, list stacking here, that would be moved up the rung as far as, like, the modeling goes. And then we realized, like, oh, we can start, like, talking about, like, age and, like, you know, just basic basic, like, demographic things.

Steve: Right.

Ty: And then it just kept growing and growing and growing, and then we realized, like, this could be a product. Mhmm. So the same people that we were trying to get funding from for the real estate side, we were like, instead of that, can we just get funding for DataFlick? And the big thing that was huge at that point was not necessarily getting the funding to run DataFlick, but it was getting that marketing budget to do extensive testing, like, on the models we were creating.

Steve: Right.

Ty: So we were spending at peak maybe, like, $4,050,000 a month in marketing, and it wasn't necessarily to, you know, generate wholesale deals or real estate deals in general, but it was more of optimizing the systems to be, like, ready for production for other users and things like that. And this whole concept our process is taking place, like, mid twenty twenty all the way up into September 2021 when DataFlick actually launched.

Steve: Got it.

Ty: So it was in r and d for about a year and

Steve: a half, roughly. So you're doing r and d with your own data that you guys were compiling?

Rami E: Correct.

Steve: And you guys were running your wholesaling business Correct. Based off this data as well as Ethan and a couple other people?

Rami E: Yes. It got to the point where eventually, we found it more valuable for all our time to be in that data side Mhmm. And, like, we do our testing through those money partners from then on. Mhmm. Because, obviously, we still need to test everything.

We need to make sure it works.

Ty: Right. And, like, we're we talked before the show, but, like, focus, we think, is pretty much everything. So we're, like, we probably should, like, really focus on DataFlick if we're, you know, gonna, like, be serious about it as opposed to just the housing side.

Rami E: Mhmm.

Ty: And it's been super tempting, obviously, this whole time to, like, launch a housing company. But Right.

Rami E: You know? We've probably had hundreds of conversations about, well, what if? Just just wondering. Just like one little one little campaign here.

Ty: So, like, it was important for us to almost, like, launch DataFlick by testing it in different places, like, all around The US. Mhmm. So it was great because we learned so much so fast because we were having, like, these in-depth conversations with all these people testing the systems, and that was just invaluable, like, testing to make DataFlip kinda what it is today, if that makes sense.

Steve: Yeah. So you're saying September or two years ago?

Rami E: Two years ago.

Steve: Or two years ago is when you guys officially launched DataFlick. Mhmm. And it was about a year and a half of testing with other people. So how many clients, quote, unquote clients, right, partners, whatever you guys wanna call it, were you working with before you launched it?

Ty: There was probably about five, and they and we strategically, like, placed them in different areas in The US. So, like, Florida, Upstate New York, Columbus in the Midwest. And we had a couple people testing in Las Vegas, but we had some people doing California stuff. But, you know, it was just kinda all over the place at the time for them because COVID and the housing market and everything. So it was really hard to get, like, reliable, you know, testing there.

But the goal was to just be in as many places in The US as we could be for testing.

Rami E: And on top of that, one of the most beautiful things about AI and machine learning is if it's a true deep learning model, it will improve improve over time. Mhmm. It'll get better and better over time. So, like, the version we were way back then for those people in Las Vegas versus where we are now is, like, night and day. True.

Steve: Right. So talk to me about that experience, like, launching it. Like, when you say launch it, make it an official business, what does that mean exactly?

Rami E: So it was the two money partners I'm talking about are in a group called We Buy Houses. So so We Buy Houses conference. There were about fifteen, twenty investors there.

Ty: Yeah. I think twenty, twenty five, something like that. Okay. So imagine, like, just like a standard, like, two day event kind of thing.

Rami E: And I remember it was our first time ever even talking to a group that size, right, much less actually pitching DataFlip. So I was living in Ty's rental at the time. It was me and him there. He was house hacking. And he was in one room.

I was in another. We had our script of our PowerPoint, and we were just pacing, rehearsing for hours and hours on end. Yeah. So nervous for this very first pitch.

Ty: Mhmm.

Rami E: But from that, we got, like, five users, and that was the start of it all.

Steve: Got it. Okay. So then what was it been like since you started it? Because this is a completely different, venture. Right?

Like, you guys worked at a smaller wholesaling company, and then you guys now there's a completely different environment. Very different.

Rami E: Yeah. Yeah.

Steve: What was it like launching this?

Ty: So, obviously, we're super in education. So we're in a ton of other, like, communities that are more on, like, the software side. Mhmm. So that was a whole other journey that we were kinda going down because it's like, we need to know a lot about housing, but we also need to know a lot about launching a software company. Mhmm.

So, like, during this whole time of r and d period, we were almost prepping the launch for that as well and, you know, making hires and things like that. So when we launched, it happened very fast. Like, it so, like, September 2021, the first three months were pretty slow, but a year later, we were occupying because we sell by a county license. So we had about a 185 counties occupied in twelve months, and we grew, like, 600%

Rami E: in

Ty: twelve months. So that whole process from a, like, learning curve perspective, but also, like, growth pains was I mean, it was super exciting at the time, but, like, looking back, it's very stressful.

Rami E: Really thinking about it now seems like the very common theme with us is get thrown in the fire and make it work. Right?

Ty: Sure. Do you

Steve: guys experience doubt along the way? Oh, yeah. What kind of doubts?

Rami E: So mix of doubt and what's it called? Essentially, wondering if you deserve to be where you are. Right? Imposterous and real at

Ty: this time, I think.

Rami E: Yeah. Right? Like, this is our first time working with this many people, really trying to service them all, make them see the best. So if for some reason the data wasn't working for them or their callers weren't performing right Mhmm. For me, as the acquisitions and customer success, I took took a pretty big toll on me mentally.

Right? That's what kept me up at night being like, how can I get this better?

Ty: What Right.

Rami E: Is really the missing link with their data?

Ty: Yeah. 100%.

Steve: And, who helped you guys along the way?

Ty: So we talk a lot about this too, but, like, we have some amazing support from, like, friends, family, and just, like, everyone in our lives. Like, you know, obviously, there's a lot of skepticism because a lot of our friends and family weren't very entrepreneurial. So, like, a lot of this what's wholesaling? Or, like, you guys are doing real estate, but you're also doing software. Like, it was a very, like, you know, tough transition for people to kinda see.

But we're like, regardless of that, they were always very supportive and, like, I don't think we would have made it without, like, a lot of that.

Rami E: Yeah. There's them and, like, the people who helped us in the industry. Right? Yeah. Like, once again, starting off and kinda growing up through it.

Tiff and Josh were really there. Tyler Evans and Ren with, like, Padley and Cameron Hall, they have been great supporters as well. Cameron loves testing.

Steve: Yeah.

Rami E: That is his favorite thing on Earth. He's like, oh, you have this idea? Yeah. Give it to me. Oh, yeah.

You wanna try it, please? Yeah. So on the business side, having those partners who do support us on the money side, but also the testing and pushing us further and further. But as he said too, like, without the family and friends support being like, you guys are gonna do it. You're gonna make it.

The company's gonna do great. Just keep going. Yeah. That's been a massive piece. What are

Steve: some of the biggest wins, that you guys that you guys have experienced along the way?

Ty: I mean, to touch on the real estate aspect too, like, we were, doing these test sequences ourselves sometimes as well, but they're very small, like, campaigns. So an example of handwriting machine that we put in our house. So if you don't know what that is, it's basically a big almost like three d printer,

Rami E: and

Ty: it holds a pen and actually handwrites letters for you.

Steve: It's actually a company right here.

Rami E: We've just heard about it. Yeah. We were talking

Ty: to them, like, right before.

Steve: Yeah. So yeah. They they're they're a nationwide provider. So, they were talking about their part what was it? There's some big real estate educator, like Gotcha.

Traditional real estate. Mhmm. Yeah. They're working with.

Rami E: So there's no doubt in my mind, those handwriting machines are so much more advanced than ours was.

Ty: Yeah. This is, like, the cheapest version where, like, let's test it. I mean, it could be fun. So yeah. Go ahead.

Yeah. Yeah.

Rami E: We're living together at that time. Right? It's us and two other roommates. We turn the sunroom into our office.

Ty: Mhmm.

Rami E: You walk in, and there's just boxes and boxes of envelopes, paper, random pens. And we would give the machine the pen, plug it through the software to where it's literally handwriting it itself, but you'd have to keep watch over it. Because if that pen ran out of ink, it would go through another 20 envelopes of just, like, dry writing. Oh my gosh. Yeah.

And then we bought a pretty crappy as well, but $200 folding machine that we would then run these through that would get jammed 50% of the time. And we were hand stuffing all these, like, it was

Ty: Hand stamping, like, everything.

Rami E: It was a nightmare. But

Ty: But regardless, let's win them. So we did, like, a I mean, like, a three to 5,000 record campaign.

Rami E: Very small.

Ty: Super, super small. And it was taking, like, just some of our testing data. And we launched this campaign first, like, week and a half. We get an insane deal, and it was a 28 k wholesale. And that was, like, a math because that at that point, like, we've been testing DataFlick for, like, six months.

And then just to see this, you know, first try, 5,000 mailers, 28,000, and that was I mean, you know how it goes. Like, from there on, we're like, okay. Like, we can do this. Like, it

Steve: is something.

Ty: Yeah. It changed, like, the entire perspective because we were like, we can do this. Like, it makes sense.

Rami E: Mhmm. So that's a great win on the actual

Ty: Real estate side.

Rami E: Real estate. On the data side, it's probably so we're able to look at the past transactions that occur and how many that are on our list. Mhmm. Essentially, having a governance of how well we're performing in that county, seeing that number rise, like, the big jumps Mhmm. That those are huge for me.

Ty: Yeah. And this is, like, in terms of, like, the precision and accuracy of, like, say there were a 100 transactions in a county last month. Like, how many do we predict? How big was the list size? How efficient were we?

Like, metrics of performance, like, for our models Yeah. Which is an interesting, like, KPI segment that, like, we have.

Rami E: Pretty nerdy win, but big win for me.

Steve: Oh, that's a massive win. Right? I mean, because one of the things that we talked about, prior to the show was, you know, you're able to predict about 71%

Ty: Correct.

Steve: Of the homeowners is gonna sell. Correct.

Rami E: On the investor transactions for that 71%.

Ty: Yeah. And then on, like, listings, it's about, like, 60 to 65 depending on the area.

Steve: Right. So you're talking about governance, which is a word I've never heard inside of real estate. Right? So talk to me about what governance is.

Ty: Right. So in machine learning, there are tons of models that are curated for almost like fact checking. So it's like, if x happens, then we know from that that we need to, like, hey. Someone go look at this internally from, like, a date our data science team. So if a say we have a county and last month that the list size is 50,000.

But next month, before we, like, launch to a user, the list size is 200,000. So it detects these anomalies of governance to make sure, like, hey. We're doing something right or, hey. We're doing something wrong, and we need to have a team member go look at it. Mhmm.

So we overlay that technology into all of our users' counties as well. So, like, before they get a list, there's all of these governance, like, checks are created based on, like, the models themselves. So that could be list size if last month the accuracy was lower than, like, what we wanted it to be. Things like that. And it gets very, very granular when you're talking about, like so if we have a transaction that have or not a transaction, but if we see any inaccuracy in our database compared to, like, the county auditor, like, we have all of those metrics going as well just to make sure people are getting really accurate data

Rami E: Yeah.

Ty: As far as, like, the homeowner information, things like that. So there are dozens governance models.

Rami E: And on top of that, so that's governance on the machine learning side. Right? There's also governance on the actual real estate side, and I apply that more to making sure that person's the right fit for that list size and the mile transactions occurring. Right? If you go into a massive county, like, we're in Maricopa, it has 1.5 total 1,500,000 total residential properties or something crazy like that.

Right? That's gonna be a massive list to get through. If you have one or two callers or something like that, you're just not gonna efficiently get through that. Yeah. So that's another form of governance we can kind of apply on the beforehand Mhmm.

To make sure, hey. Even before you sign up with us, let's make sure what you're wanting is realistic. Sure.

Steve: So, you know, we're talking about machine learning, and then on time, we're talking about AI. You know? And right now, AI is a really hot, hot topic. Before we get into AI and how to use it, I just wanna get, you know, to touch on the difference between machine learning and AI because it it's it seems to me when I hear marketing and people talking about this, it's the same thing when it's not.

Ty: It's not the same thing.

Steve: Can you talk, touch on that, please?

Ty: The simplest way to understand it is so AI is trying to replicate human like behavior, artificial intelligence. So it would be like almost like how a human would so chat GPT is obviously a very, very hot topic. Right. So that an AI example of that is the conversational ability that has and if you don't know what ChatGPT is, it's essentially a conversational AI tool where you can have conversations about whatever topic that you need help with.

Rami E: Mhmm.

Ty: So it's like, hey, Chagibuty, can you help me write this SEO article? And it's like, sure. What are we writing about? So things like that. So that is an example of how artificial intelligence is mimicking human behavior.

Mhmm. Whereas machine learning, more talks about actual predictive technology. So the most common way to do this is something called a regression model. A regression model just talks about here are all the things that historically have happened, and it can be anything about data or events, things like that. And the machine learning aspect of it is predicting what would happen based on historical data or concept.

Right. So when we're combining all of these technologies together, there are tons of use cases and, you know, hundreds of

Steve: We're gonna talk about a lot of this.

Ty: Yeah. Of course. Yeah. But that is, like, the simplest way, and it gets very, very granular from there.

Rami E: But

Steve: High level AI is trying to get the computer to act like a person would.

Ty: Correct.

Steve: With the data. Machine learning is just give it all the data in the world and have it try to give us the best guess, on future data. Exactly.

Ty: So an example, like, very, very simple for, like, real estate. So we're using AI to analyze, like, demographic behavior as an example. So, like, here are the events that are happening in someone's life. And based on, like, human behavior in the past, this is what would have someone sell also based on a machine learning model, which is the historical data of transactions that actually happened. Mhmm.

Just, like, as a use case for, like, how our models work.

Steve: Right. And then as far as DataFlick then, the way you guys work is based off of machine learning. Correct.

Ty: And we're overlaying some AI elements that are almost taking, like, the human behavior concepts of, like, life events in pea that are in people's lives and then tailoring that with our machine learning models based on the historical data.

Steve: Okay. So then, I I guess, the first question then. Right? I mean, I imagine other people would would wanna ask you. There's a lot of data providers out there.

I've talked about a lot of them. Why DataFlick?

Ty: You wanna handle it?

Rami E: Yeah. I'll take it. So for us, and once again, difference between that AI and actual machine learning.

Ty: Mhmm.

Rami E: There aren't a ton of companies out there actually doing that AI paired with machine learning aspect to create predictive analysis. And you can stack a ton of data. You can do all that, which is great. And if you can do it right, you can target a ton of people. 100% get in front of the right people.

Our whole mission statement, our whole goal is to cut that down while still actually getting in front of the right people.

Ty: Mhmm.

Rami E: Right? So a huge part of this gets into marketing dollars. How we set that's a really big overhead. Mailers go anywhere from 50¢ to a dollar 50.

Steve: Right.

Rami E: Text cost money, all all that good stuff. If we can cut that down by providing just better upfront data now I always describe the data as the gas to the car. Right? You still need the tires, engine, transmission, etcetera. But what you put in definitely will come back out on that back end as well.

Steve: So different octane. Yeah.

Rami E: A 100%.

Steve: It's true. So I'm gonna speak what I know, and you tell me where where I got the data wrong. Right? So you look at you know, there's Bash, there's PropStream. There's there's eighty twenty.

I'm a big fan of them. Invest Machine, big fan of them. Right? Those are great pro great data providers. And probably radar as well.

Right? You got, like, the tiers. They're, like, a 100 to $200 a month. Right? And you got the higher tiers.

You got eighty twenty investment machine. My understanding of those is they're pulling public record directly, right, like, the counties. Counties or or wherever else the public records are coming from. And you got Adantic, which has been the big bad boy for the longest time. Right?

And my understanding from them is machine learning. Right? It's the regression model we're talking about is that here are all the people that sold to a cash investor. Here are their 400 data points or whatever it is approximately from public records. Not public records.

From, other data companies. Data providers. Right. Data brokers. Data brokers.

There you go. Right? Whether it's Melissa data, Adam data, whatever. Right? They're pulling all the data points, and it's saying, based off of the people that sold in the past, here are the people that I'll probably sell given their current data points.

Is DataFlick basically replicating that model?

Ty: So we parse it out into so we have two primary flagship products. So it's our realtor product and our investor product.

Steve: Sure.

Ty: So the machine learning aspect of this to talk about, like, the regression models.

Rami E: Mhmm.

Ty: So first, we're gonna look at our the realtor product. These are all the people who sold on market to the MLS. Mhmm. Very different group of people who are selling Or

Steve: the very different data points.

Ty: Exactly. So we're taking those two pillars of data, and that is our regression models for specifically each product respectively. So it's like, here are all the people who sold off market to an investor. Here are all the people who sold on market with a realtor. So that is our training data.

The and I have no idea what other providers or what other companies do, but the way we do it is we have kind of three pillars. So it's our housing data and our demographic data and then all of our skip tracing sources. Mhmm. We could touch on the skip tracing, but for now, let's focus on just the actual property data and the demographic data. So in totality, there's roughly about 900 attributes per household that we're tracking.

Nine hundred? Nine hundred.

Steve: Okay.

Ty: And that's everything from income modeling all the way to, like, education or and life events and things like that. So it's like, hey. Based on the last five years, like, this is the kind of trajectory of, like, this person has moved up in housing. Like, they went from an apartment they're renting all the way to, like, you know, a upper middle class home. Mhmm.

So all of those life events, all that demographic data modeled into, you know, those 400 demographical attributes. And then we're taking all of these property characteristics from, like, tax assessors and mortgage, like, mortgage recorder databases and things like that. So we take both of those and then apply the historical data for each of those respectively. And then we model all that and put it into a score from zero to 100. Mhmm.

The higher the score, the more likely they are to sell. So, internally, we're running about eight different models to come up with that one score that, like, our users actually see.

Steve: So it's very complicated. So there's a score based off the person's data points? Correct. So There's a score based off of the house or the or property they live in.

Rami E: 100%. Yep. So there's a lot of commonalities out there between the average length that a house actually gets purchased to when it gets resold and things like that. And, obviously, that will vary depending the county, depending the area, price point, etcetera. But that is the thing our systems really look into and track.

Steve: Is it looking at that and I'm asking this is very selfishly because I've used you guys here in Maricopa County. Does it say then with your machine learning data that because you were saying in we have a 190 different, predominant neighborhoods here. What was it?

Ty: Oh, yeah. So, there's a 119 neighborhoods in Maricopa Mhmm. Primary. So if you were to take the top 25 neighborhoods based on investor transactions Mhmm. They occupy 70% of the total investor transactions that actually happen.

Steve: Like the eighty twenty rule. Exactly. So we got 25. Yep. So of those 25, do you guys have in your data modeled that we have a faster turnover in these 25 neighborhoods versus other of the remaining 119?

Rami E: To a degree. Right? So we're making these predictions. I'm not gonna say, like, everything I tell you right now is a 100% truth and go with it. Yeah.

But to a degree, we can almost hypothesize based off what's happened in the past and that historical data.

Steve: Okay. So I guess another way to ask the question is, you know, talking about the modeling data, like, based off the house, is it, like, in Maricopa County? Are we using this one thing where, like, we say I don't know what the number is in Maricopa County right now, but everyone says, you know, somewhere, like, around seven years. Right? Homeownership.

Ty: Five to seven years is pretty average.

Steve: Right. So are we saying seven years of homeownership for all Maricopa County, or do you say, like, hey. In this neighborhood, it's seven years. This neighborhood is three years. I'm just seeing how granular you're getting

Rami E: with this.

Ty: So we have four levels of neighborhoods.

Steve: Okay.

Ty: So it's macro, standard neighborhood, and then it goes to sub neighborhood Mhmm. And subdivision. So every one of those, like, subsected levels, they all have their own metrics of performance that we're also tracking and overlaying it.

Steve: Got it. And then another question I have is because we had Robert Wednesday on the show, right, with InvestorLift. That guy is freaking genius. Right? And one thing he was talking about is Facebook has this profile for you and all your life events

Speaker 3: so that when you do sign

Steve: up for Facebook, already has this. I think you called it ghost data if I recall correctly. Ghost data. Right. With your guys' data, do you guys have a snapshot of this person today, or do you have this person's life event?

Like, life story in your database.

Ty: I know you you can explain the lookalike style.

Rami E: Yeah. I would say a mix. So we definitely have pretty up to date data on that demographic side, but, also, we are looking at years of historical data as well because we need that build out. Now a really cool thing that some of our users are doing is with Facebook's look alike audience. For people who don't know what that is, let's say you have 10 profiles.

Facebook can go through and say we found a, b, and c are very common across 10. So we're gonna find 10 new ones to reach out to. Right? Pretty simple. People are able to do that with our data.

So now you're stacking two machine learning models on top of each other to then find new people who are just as likely to transact. Got

Ty: it. We're almost, like, creating market share. So it's like there may have been a 100 transactions last month, but we we're giving you the people who also look like those people. So you can almost create more opportunities that way as well.

Steve: So you're saying you're taking the audience or you're taking the the list, motivated homeowners potential motivated homeowners. Correct. Right? People that look like they're gonna sell and putting that in Facebook. So my question here then is how do we transfer that?

Because my understanding with, Facebook and Lookalike Audiences, like, when you upload your your list because we tried doing this. We we upload our list. The the match rate seems to be consistently not the best only because when they sign up for Facebook, their email address and data is a little bit different than what we have for them. So how's that match rate looking?

Ty: So for us, the way that we tell people to do it and some like, we've heard circumstances where people are like, hey. Like, you know, we got all of our list uploaded, and then we've had certain people who are, like, 30% upload what happened. So we don't exactly know how Facebook does it, obviously, but we tell people to take the so on our skip tracing, we have something called the last reported date. And the way that our skip tracing actually works is we buy about 6,000 public and private sources, and that can be everything from a utility bill all the way to a gym membership. Mhmm.

And we run the date of birth, first name, last name, and property address through those 6,000 sources. And we say, hey. There's an active account that matches that information, and it was last active on x date. So our skip tracing will give you the last reported date of that activity date through one of the 6,000 sources.

Steve: Yeah.

Ty: So from that, we will take, hey. Here are the three most recent actual last reported dates for each number. Mhmm. Upload that into the Facebook custom audience. Got it.

Because, statistically, that has a much higher likelihood to actually find the person that an email does. People do upload two emails, but, I mean, I I have, like, eight emails. I don't know how many you have, but it's just unlikely you're gonna get the right match. Right? Right.

Steve: Yeah. So then, going back to your your your modeling the person, the the property, and you mentioned the skip tracing. How how does that Correct. How does that become a factor in the likelihood someone's gonna sell?

Ty: So it's not necessarily the skip tracing database is tying into the likelihood to sell, but we're using AI technology to find the skip tracing numbers. So, like, from those 6,000 sources outlining, like, the, you know, process I just did. So we're saying, like, hey. Statistically, based on historical data, like, we've had these people connect with, like, this last reported date. Because, like, what we started to do is aggregate actual connects from our users on phone numbers.

So it's like, hey. Here's this cold calling campaign, and here are all the verified. Like, this is their phone number, and we're actually tying that data back in as well Mhmm. To try and, like, increase the accuracy of our skip tracing database. So that's the AI element of that.

Mhmm. But it's not necessarily tying back to the likelihood of selfies.

Steve: Got it. Was there anything else you wanna add to that?

Rami E: No. He covered it pretty well, actually.

Steve: Alright. So then hopefully, then we were able to kinda, illustrate, for everyone that's listening. Right? So because there's there's all these different data products. I just wanna make sure, like, we're when we're saying what DataFlick is, we're clearly, describing it.

So it's machine learning based with AI assistance, the the how to identify, the most likely seller.

Ty: Exactly. Correct.

Steve: So, one question we talk a lot a lot about, and this might be a little bit challenging, is, like, if someone wanted to go and replicate your business. Right? Like, someone comes on, like, you know, if someone wants to build a wholesaling business, how are they doing it? So if someone wanted to, you know, compete against DataFlick, right, what are some like, what are the things that you guys have figured out along the way to get to where you guys are?

Rami E: Sure. So we have during those initial r and d days

Ty: Mhmm.

Rami E: We had to completely scratch our whole system.

Ty: Like, seven times. Like,

Rami E: this was years of building the well, a year at that point of truly building it out to realize that it wasn't at the level that we needed it to be by any means. Back then, we made the mistake of hiring a developer that we shouldn't have. Mhmm. Right? It wasn't we skimped out on price a little because we're starting up.

Right? Small, trying to

Steve: get late. We've done that. Yeah.

Rami E: I'll get a little greedy. Okay.

Ty: That makes sense.

Rami E: But to actually get to the point we are now, it's been a very long journey of a ton of trial and error and a lot of funding.

Ty: Yeah. So the other aspect that's pretty interesting. So we everyone talks a lot about machine learning, how it improves month over month because you get more data and, like, it just improves month over month. And so I'm a big proponent of, like, the economic moat, You're familiar with the concept. Those who aren't, it's basically, this is your company and, like, how can we build a moat around this company to keep other people away from it?

Sure. So our best moat is building that historical database because you can't get it unless you're actively, like, storing it month over month. So for someone to even try to copy it, they're first gonna have to get all of that historical data. Mhmm. And by the time that they get to where we are today, we're obviously gonna be, like, that much further ahead.

Steve: That's the argument that's been made, like, supposedly, when no one will ever cash Tesla,

Ty: is that Exactly.

Steve: They even though they have all the I don't think they have any patents. Right? Like, everything's, like, open records. Like, here's how you Yeah.

Ty: Yeah. Open source it. Yeah.

Steve: Here's how you build a car our car batteries. Right? Here's exactly how to do it. Yeah. Good luck.

But they have been tracking all the driving behavior for AI and and and, autopilot. So they have all the records stored on how to they've recorded all this driving behavior, and that's supposedly their advantage.

Rami E: Yes.

Steve: So that being said, that makes total sense to me. But if someone were to try to compete against you. Right? First thing you guys did was the funding, and then you guys started buying data. Yes.

So you have to buy data from how many different data brokers are you guys buying from?

Ty: We have about 14,000 sources.

Steve: 14,000 sources.

Ty: Yeah. We spend, like, 90,000 a month just on raw information.

Rami E: It's if you're doing this for yourself, unless you plan to take over the whole US, it is

Ty: It's not really financially feasible.

Steve: Yeah. Right. So 14,000 different data records. Data scientist is something I've heard thrown a lot thrown around a lot. Right?

It's like I've got this many different data scientists working for me. Right? In the conversation we had before the call or before the show, I actually believe you guys have data scientists. Right? It's true.

So, explain to me or explain for people listening what a data scientist is, and then how many you guys have.

Ty: So for us, tech their technical titles are machine learning engineers. Mhmm. So they there are three of them just on, like, that aspect. Data science is more on so it's like the data science team, and there's, like, subcategories of people that kinda go in that. So an example is we have a data engineer.

So that person is just handling, like, data governance. Yeah. So it's like, you know, are we aggregating databases together, like cleaning them, making sure they're maintained, like so the most common example is, you know, there's probably anywhere from, like, one to 200,000 transactions a month, and all that has to be not only updated, but then reuploaded from model adjustments as well. Adjustments

Rami E: as well.

Ty: And month over month, it optimizing on, like, the other properties who didn't sell and things like that. So that would be like a data engineer. And then a machine learning engineer is someone who just focuses on, like, actual models based on the regression models we've mentioned earlier in the show. So we have three machine learning engineers, and they all three have, PhDs. Yeah.

So they're pretty talented guys.

Steve: They know a thing or two. So going into we're talking about the title of the show. Right? How do you use AI to predict your next motivated homeowner? So then how do you use AI to predict your next motivated homeowner?

Ty: So And I know, like, when

Steve: we're talking for the show, there's, like, four different applications or four different ways we apply apply AI. So go ahead.

Ty: So we have all of our different models that we've, you know, stringed together. So, like, we have our investor model and then our realtor model. And, obviously, with the investor side, like, we're just using the training data of people who sold off market to an investor and then all those other sources combining together to almost enrich and, like, almost uncover insights. It's probably the best way to put it.

Steve: I guess, like because, like, prior to the show, you know, one of the cool things I get, and there's a immense benefit for myself, is, you know, the guest comes in and they hang out, and then, you know, they talk to the media team and they talk to my wholesale team. Right? And so, like, you and you guys and Bino, our our dispo guy totally geeked out on how to use AI in Dispo. So what were some of the things you were sharing with Bino?

Rami E: Yeah. So a big thing we talked with him about was chat g b t and the four point o model they just released. These conversational AI, you can do so much with. It's ridiculous. You can use them to format even the emails you're willing to reach out to these prospects with.

If you have the list of your prospects and certain bios or things like that with them, ChatGPT can write out literally individualized emails ready to just send out to all these that you can upload into a blast software, things of that nature. Right? So just optimizing that in ways where he can get so much more done in such little time Yeah. Was the main thing we talked about, and he was ecstatic.

Ty: So in a direct example would be if you're a wholesaler, you have your VIP list. Or if you don't, you should. So, like, here's, like, your 10 to, like, 20 best clients. And you you should have, like maybe deals they bought from you in the past. Maybe it's some bios on them or, like, their company, things like that.

So, like, I've seen people upload data like that and then automatically spell, like, custom tailored messages of, you know, emails of, like, hey. Here's this deal. Here are all the property details. So I can another example would be, I've seen people use it for creating real estate listing descriptions.

Rami E: Mhmm.

Ty: So you're almost like creating a description for every house that you're trying to dispo Mhmm. While giving them that custom tailored touch of, you know, like, almost customization for that person specifically. Right. And this is something that can be done in, like like, 20 emails in twenty seconds. Maybe.

Yeah. Maybe.

Steve: Probably Send an email about this property, custom tailored to this person's profile.

Ty: Yep. And that's just, like, dispo for, like, one very small example, I would say.

Rami E: And on top of that, you could almost write it out in a way to where if so it's a system that can also memorize. Right? So if it knows the average buy box of this VIP seller, it can almost auto apply this property that you're looking at to the sellers that actually fit that proper buy box as well, which is just taking a whole another step further.

Ty: And, like, highlighting the benefits of the property based on the buy box that that person wants. And this could all just be in Excel, and you're just, like, copy paste it in.

Steve: Yeah. Crazy. So one thing that's come up, you know, they're talking about how AI is, you know, gonna be coming into our industry and and disrupting our industry. Right? I made the argument that no matter what happens, you still need salespeople.

Right? Like, no matter what happens, you need something on the other end of the homeowner that can demonstrate empathy, something we talked about in the beginning of the show. So I'm hearing people talking right now how they can use AI for, texting and cold calling. So what are you guys thoughts for for texting and cold calling?

Rami E: So text, I think there's a big difference between that initial marketing reach out versus the acquisition manager who's actually showing the empathy and walking them through the deal. Right? That's two separate divisions in my mind. Yeah. Text is pretty straightforward, I think, though.

Because if you're looking at a conversational AI, if it just has some training data on what your VA or you yourself, these conversations you've had Mhmm. With these people, it can pretty much predict what to say and pump it out to at least get you the lead. Yeah. Cold call, that's something we've kinda talked about, and it could be very interesting.

Ty: Mhmm.

Rami E: So there's certain voice AIs. Right now, that's primarily tied around celebrities, and people will make funny videos of, like, Joe Biden, Barack Obama playing a video game or something. Right? But if you can get it to just be a generic voice, that's the primary voice. And you have voice recognition, so, essentially, the seller, when they say something, you're able to process it.

Once again, based off that training data, come up with a response that then this AI replies back. Mhmm. You could almost automate cold calling, which is just insane thing to think about.

Ty: Some other, like, kinda low hanging fruit from that. So, obviously, people who are cold calling primarily use overseas people to do it, and accents can be an issue or a deterrent to some people that they're prospecting on. So that voice AI technology can combine with a perfect dialect of, like, whatever accent, whatever language that you want.

Rami E: Whatever area. If you're in the South, having more of a Southern 20 versus New York. Right?

Ty: Yeah. And I mean, like, that is stuff that exists today 100%.

Steve: Yeah.

Ty: And I think, you know, to me, like, if someone could if you're using a cold call overseas and their English is, like, you know, comprehensively very good and the dialect is or, you know, just how they speak is the issue, that can be fixed. Right. Now all of a sudden, you know, it changes everything, I think.

Rami E: Like, to get American cold callers, like, $15.20 dollars an hour versus Plus a vision. Being six, seven.

Steve: Right.

Rami E: Now you're able to do that fixing that accent piece and having them still be great at their job, which they are.

Steve: So what I'm hearing right now is the texting component, the initial outbound texting component, and the first couple of replies, you believe that could be done right now.

Ty: 100%. Yes. So the other thing that I actually just thought about as you said that so I don't know about you guys, but when we were doing the texting, I absolutely hated writing all of the templates that the texting platform had to use because you have to have such a large variance

Rami E: for

Ty: it to not get blocked to spam. Not to get

Steve: not to get caught by the carriers.

Ty: Exactly. And I mean, you could literally give a list of the blacklisted words they're not supposed to use, and it would produce 200 variations of that

Rami E: Perfectly.

Ty: In, like, five months. Yeah. And, like, that would have taken us hours.

Rami E: For for perspective, we suck at writing. I will say that. Like, that's just not our forte, not our go to. Our website was mainly written by AI, in fact. Yeah.

But that's still a challenging thing that I think a lot of people deal with, how to get that initial touch out while not getting blocked by the carriers, things of that nature.

Steve: Yeah. So,

Rami E: yeah, that's a phenomenal application.

Steve: So maybe this would be a new venture after this episode. The three of us can do

Rami E: Yeah.

Steve: Something like that. So that's for for for the initial text blast.

Rami E: Yes.

Steve: So what are some other applications? So we just talk about Dispo a little bit. What are some other application potentially using AI for acquisitions?

Ty: I saw a really cool startup, Cam Mitcham, because I know who they are. So they, they were doing a pre seed funding for it's actually a really cool concept, but they were tailoring people who would call, like, a suicide prevention hotline. And, you know, the big issue with that is when that person receives that call, it's very, very serious. Right? Mhmm.

So guiding that conversation correctly is crucial. Mhmm. And their whole niche started out to be, like, let's focus on this, you know, conversation. And as it's happening in lifetime, an AI system will say, hey. Here's how you should definitely, like, steer this conversation

Rami E: Mhmm.

Ty: Based on all the historical data that it's been compiled with.

Steve: Alright. Kinda like using Gong, Gong AI.

Ty: Yes. I've seen some stuff about them as well. So they were like that was their original, like, purpose. They made a massive pivot because they realized they're, you know, not very strong market there, and they switch it to sales. So as you're having a conversation through acquisitions for virtually anything, you know, you could set the parameters beforehand and say, like, hey.

Here is a company that is doing b to b sales for, I don't know, logistics. Mhmm. And so in that niche, let's say that we, like, here's all this training data on calls that we've seen in the past for this, and every objection is being handled based on, like, hey. Here are the three ways you can respond to that objection on a sales call in lifetime. Mhmm.

So if you overlay that kind of technology on the acquisition side, I mean, I think it would streamline training. I don't know the percentage, but it would be rapid improvement, I think. And

Rami E: the acquisition side, but also if you apply that to the initial with the callers once again. Yeah. This is you're not going all the way to automate your callers. Right? But just having them because training callers, you're generally relying on a manager or you have them in house, which takes a lot of time.

Ty: Mhmm.

Rami E: But they're able to have a lifetime script being printed out that they can just follow along with.

Ty: Mhmm.

Rami E: That's half the battle.

Steve: So we have in, in our, organization, Ian. Right? He's, the first guy we actually hired to be part of our one of our sales trainers.

Rami E: Mhmm.

Steve: Right? And, he was screwing around one day. He's like, let's make some videos. Right? And he's like I think it was Jasper is what he was like.

Hey, Jasper.

Rami E: Yeah. Yep.

Steve: How was Steve Trang answer this question? Yep. And, there's enough data out there of me Mhmm. Where he gave an answer, and he's like, that's exactly It's

Ty: pretty close. Right?

Rami E: How's he

Steve: gonna answer that question? Like, dang. That's crazy. That's what that's what's out there.

Rami E: Yeah.

Steve: So you and I had had a conversation, before, but one other application is that, you know, salespeople are notorious for not taking great notes.

Ty: No. Terrible. I'm so bad about it.

Rami E: Yeah.

Steve: And so you we were talking about an application, that we could have it where the call is recorded, I think, using CallRail.

Ty: Yes.

Steve: Right? And it's sent to ChatTBT, and then it would transcribe it, and and give bullet point notes into the CRM.

Ty: There are so many companies

Rami E: that do it. To say. So this is actually getting very, very common. Even Zoom has, like, Otter dot AI application.

Steve: Well, that's just a transcription

Rami E: Exactly.

Steve: But a bullet point summary summary.

Ty: Yeah. So it's, like, not only summaries I've seen, I've seen, like, action items and, like, levels of improvement based on the call if it's a sales call, which is, like, that's insane. Because, like, even from, like, a sales perspective, if you're a sales manager, you can almost, like, like, get all that information and then just use that to streamline, like, just focusing on, like, improving your salespeople as opposed to, like, listening to every call and, like, it just is very tedious. And, like, that could be applied for cold callers especially because, like, I've definitely seen and heard complaints about people saying, like, hey. These cold call notes are terrible

Rami E: Mhmm.

Ty: And not anymore. Alright. So that's just, like, a few things I think you could overlay. And all that technology, like, exists right now,

Steve: 100%. And then another thing we were talking about is, you know, you guys have how many clients do you guys have now?

Rami E: About seventy, seventy five ish.

Ty: Yeah. Across about And, 80 counties.

Steve: Yeah. And your prod your product's not, you know, the cheapest.

Rami E: It's not.

Ty: It's not.

Steve: So, so you get to talk to operators that are doing real business. So what are you guys finding is is the most effective use. Right? So they've got the data. They're buying the data from you guys.

What is the most effective way of using it? And I'm asking this because you had suggested prior to the show that cold calling is not the most effective. Yes. So what are the best ways to leverage data? So I've I've I've got a data flick.

I bought the data. What is the best way to actually use, use the data?

Rami E: Definitely. So cold call has its place. Right? In terms of efficiently getting through a list to capture that market share, that's where it really lacks unless you have, like, 15 callers. Yeah.

Right? Because then you're making up the ground, but that could get pricey. For us, it's very much omnichannel or multichannel marketing where you are hitting these people from just the angle that they are most likely to respond to. And, of course, predicting that is a pretty hard thing to initially do.

Steve: Yeah.

Rami E: Yeah. So hitting them from all those angles is almost pretty crucial. If I know on my list in the past three months, we've predicted 70% of these transactions that occurred, which is our average. Mhmm. It's just getting in front of them.

And that's why we include that skip tracing in our service to kind of encourage that as well. But the whole thing is we really want you to be able to have that marketing dollars and have that angle to get in front of them the best.

Steve: So let's talk about the, what that means then. Right? Omnipresence. Right? Because, you know, yeah, obviously, the more ways you get in front of them, the more effective.

And and it's it's, cumulative. Right? If you can get in front of multiple channels, all the channels are more effective.

Ty: 100%.

Steve: So when you're saying omnipresent, like, what are the different channels you guys are recommending?

Rami E: Definitely. So the most common one that I see people use today are callers and texters. Right? Text is pretty efficient with getting through a list for the price, a 100%. But building out your brand is actually a really important piece because that's gonna affect these response rates in general.

So for direct mail, for example, on postcards, I've seen people pair it with their TV commercials, and it'll go from a point 5% response rate to about a 2%, which for postcards is pretty massive depending the area you're in, of course. So building out that way to where you're getting that inbound flow and outbound flow to that pretty high degree is pretty important Yeah. In my opinion.

Steve: So that's TV and direct mail. And so was so things we talk about is TV, direct mail, texting, and cold calling. Am I missing anything, or is that all of them?

Ty: Touch on the geofencing as well.

Rami E: You can take that.

Ty: So, like, something that's in beta right now is there's a concept called geofencing. For those who don't know what that is, that's essentially drawing a almost like a polygon or targeting a very specific area geographically. Yeah. And when someone goes in that area or if they have gone in this geographic area, people do ads towards it. So, like, the, I mean, I've seen this from anywhere from, like, say, Dick's Sporting Goods.

There's a fishing department. And they can actually track if someone has gone specifically in that fishing department, then they will send, like, actual ads catered to phishing. Mhmm. So that would be, like, an example of, like, an ecommerce use case. But for us, we've been, like, beta testing with a couple different users.

Like, say we have this property address. As soon as they cross that boundary of the actual polygon area of their address and mailing address specifically just because they live there, they will start to get tailored ads that are specifically focusing you know, the highest level would be focusing on the distressed factor that they're actually experiencing.

Steve: Yeah.

Ty: So, like, say it was bankruptcy. You would have an ad that's, you know, lightly touching on bankruptcy, just to make it feel like very personalized to them. Mhmm. So that helps build a ton of brand equity. This is also more on the realtor side too.

But, like, if they're seeing you virtually everywhere and then you send a direct mail piece, then you cold call, cold text them. It's amazing results.

Steve: Right.

Ty: So, like, eventually, that that's kinda, like, where we're going as a company is to focus less on, like, here's, like, one campaign that works. But if you don't wanna do an omnichannel approach, then we're probably not, like, the best fit for you in general. Because I think, like, from a saturation perspective, if you're not, like, doing omnichannel at this point, I think that it's gonna be very, very hard to build, like, predictability. And you probably see that more than I do.

Rami E: Oh, a 100%. The amount of time someone will have one or two cold callers. We kinda talked about this already. Right? And going to, like, Maricopa Yeah.

Saying, oh, yeah. I can hit up that whole area with one caller. You're just not gonna match the person doing seven different marketing campaign. Right. Never will.

Steve: So the argument here then is, what you're just suggesting a moment ago is if I'm gonna buy the data from DataFlick, I should be prepared to call them, texting, mail them, and then at some point, geofence

Ty: Exactly. Marketing.

Rami E: And even, like, if you have creative ways yourself that I haven't mentioned here that you think, oh, maybe I can do this and change it up and get ahead of the competition. Like, that's not a bad thing in my opinion at all.

Ty: It's interesting. We've seen some people do really well with door knocking,

Rami E: actually. Yes.

Ty: It's it's hard to build a team that wants to do that, but if you can get the right hires there, I mean, I'm I've we've seen, like, two, three deals a week come from door knocking from people because they're just taking, like, a foreclosure list or, like, a very, very high, like, distressed list, overlaying it with the AI of, like, this person is the most likely to sell that's also going through foreclosure, door knocking them one out of every 10 is, like, a legit opportunity.

Steve: Yeah.

Ty: So it can get very, very granular very fast. I think there's probably, like, 15 to 20 different sources that you could do from, like, SEO, PPC, YouTube ads. It just depends on, like, how big you wanna get and, like, you know, what the budget is, obviously.

Steve: And just to really dive a little bit deeper into this, you know, we're talking about geofencing. For those that aren't, familiar with the application. Right? Like, when I first heard about geofencing, it was basically, like, you go to a hotel conference. Right?

Now you're getting retargeted for, like, all the different products that they were selling at that hotel conference.

Ty: Exactly.

Steve: Right? Like, it it doesn't matter, if if your phone was somewhere inside that building, then after you left that building, you're getting retargeted for weeks to buy whatever products they were pitching at that at that event. So for you guys, I get some data from DataFlick. I know where they live. If their phone or the computer obviously, computer's not leaving.

But if their phone was inside their house, We are retargeting them on their device, Hulu, Peacock, whatever.

Ty: Streaming services.

Steve: Whatever streaming services they're they're watching, we're sending them video ads onto their device on whatever platforms they're on.

Rami E: Video ads, banner ads. Display ads. Yeah. Essentially, whatever fits in that budget of marketing so we can hit them efficiently enough to really build that brand awareness.

Steve: Yeah. And it's obscene what we're talking about here because Hulu is not very expensive to market on. Right? Yeah.

Ty: This is so the average cost in our testing campaign was 10,000 records for $3,500. It's about 35¢ a hit.

Steve: But to create the illusion of being everywhere, Very powerful. Right? Because if you wanna market on TV, I mean, you're starting at 10,000

Rami E: a month. Exactly.

Steve: Starting. Right? You're hitting a lot of people, but you're starting at 10,000. But in our market, I mean, Doug Hopkins is, like, the biggest fish. Right?

He's the Hulk in our pond. Yep. I believe he's spending, like, a $100 a month. But to create the illusion of being everywhere Very powerful.

Rami E: And this is so powerful because that illusion builds that, once again, brand authority.

Ty: Mhmm.

Rami E: So once you're tying this with those other marketing channels, you're gonna see an increase in those as well.

Ty: Yeah. And, like, I think it's super important to almost position your outreach to, like, play on that. It's like, hey. I don't know if you've seen us around. I don't know if you got our mailer.

It's, like, lightly touching it. And, like, I think in our first, like, beta test, it was, like, two weeks of geofence targeting of the 10,000. And the first week of cold calling, they're, like, four legit opportunities. Yeah.

Rami E: And generally about 50% close rate with that group.

Steve: Yeah.

Ty: So, like, it it's almost like taking cold calling, texting, direct mail, and every other, like, channel that's outbound to, like, the next level.

Steve: Well, this is definitely the next level. And this is something I actually looked at a long time ago. You know? So I was kinda I'm kinda curious how you guys are able to do it because I looked into geofencing years ago. Right?

And for me, when I was when I was looking at it, I was like, okay. So we can pull their IP address. And for our IP address, we can have an idea where they live. What we were doing, it was like a five mile radius. Like, this is completely useless information.

Useless.

Ty: Yeah. Right.

Rami E: And for us, we're almost doing it the other way around. Right? Right. Instead of tracking IP address first, we're doing it based off that mail address.

Steve: Yeah. So how are you guys able to pull? Are you guys pulling an IP address? Or what do you guys

Ty: So we're using, like, essentially and this is also, like, we're launching an entire division for this. By no means am I an expert in this. Yeah. But that's how we're hiring the people that are. So we are using a Polygon system, which is basically the boundary lines of the surveyed area for a property address.

So the second and active IP address that is on this platform crosses that boundary that makes them, you know, game to market to, essentially. And from that, like, you can really tailor, like, hey. Here's what we're like, this person's been hit. Let's do retargeting towards them. Mhmm.

So, like, an interesting metric that where you track from a KPI perspective on this, test sequences was how many people saw this ad typed in our like, the company name and the URL as an example.

Rami E: Mhmm.

Ty: So now we're getting them on the website. Mhmm. Now we can retarget them on Facebook, YouTube, everything. Yeah. But that's like I

Rami E: mean, we're

Ty: doing this high size level on that kind of stuff, but that's not where we're starting.

Rami E: Yeah. And adding to that. Right? We're talking about doing this on that very high level. Once you know, like, oh, we got this many impressions, this many people interact with the ad, this many people went to Google and searched our name after seeing the ad, etcetera.

In terms of retargeting with mailers and stuff, those solid 700 people, that's a very small list. You can send very high quality mailers to them them at

Ty: that point.

Steve: Definitely go door knock those guys.

Ty: Yeah. Exactly. Yeah. We gotta we gotta get in front of them. Right?

Because if they typed in your name after seeing that, they're obviously thinking about it.

Steve: They're raising their hand. Yeah.

Rami E: Yeah.

Steve: Can't raise your hand any more than that. Truly.

Rami E: Without literally typing in your name, you know, and becoming a lead. Yeah.

Ty: Kinda, like, build off that brand equity piece. Like, this is we're big fans of the month over month improvement, obviously. So, like, the longer you do this, the more brand equity you build and the more trust you naturally build, and that's pretty invaluable, I think.

Rami E: Yeah. Yeah. Adding to that, actually. So you said you've done geofencing before. It was kind of a nightmare.

Right?

Steve: Although I saw how my accuracy I was like, I'm not putting a dollar now.

Ty: Yeah. Fair.

Rami E: Fair. The guy we're bringing on, he's the geofencing before. Right? He's an expert in the field. That's what we want.

Essentially, he was like, yeah. With the $3,500 budget, don't expect anything until, like, month two. Right? Because it's really building that up, that stack effect. Once he utilized our list with it, he immediately saw a three times higher impression rate, three times higher Google search, etcetera.

And after the first week of calling those two week ads Mhmm. They may get two deals. Yeah. So it's already it's almost like we're completing each other in a pretty nice middle ground.

Ty: Well, and

Steve: what you guys have done in a way is you guys have taken outbound data because that's what we do. Right? We buy data and we we outbound market to it. Yep. And we turn that outbound data into inbound marketing.

Right? So, you know, like, we mail to them and they call us, but it's still, like, a lot of, outbound effort.

Ty: Sure.

Steve: It doesn't feel like they called us till we market it to them.

Rami E: Yeah.

Steve: Here, they it feels like they market it to us or they reach out to us unsolicited even though they're totally solicited.

Ty: Right. It feels a lot better, like, for the homeowner, I think. Yeah. Because you're like, oh, yeah. I've seen this guy's stuff all the time.

Yeah. Different vibes

Rami E: for sure.

Steve: Completely different vibes and then dramatically improves the quality of the lead.

Rami E: 100%.

Ty: And, like, to be honest, I think when they engage after that cold call, it really shows, like, they're probably more serious too, which I think is really important to note. Because we get so many people get tire kickers on, like, call and text, I think.

Steve: Yeah. And The quality of the appointment is way lower on cold calling.

Ty: Yeah. I mean, I think industry standard, last I checked, was, like, one out of every 55 cold call,

Rami E: text leads. Closer to 80.

Ty: Yeah. Just just depends. But, like, mail is, like, one out of every eight. Mhmm. So way, way better.

A lot less resources needed.

Steve: Yeah. So I definitely want to get to the audience questions before we do that. You mentioned something funny earlier about, the Grinch.

Rami E: Yes. Okay. So this doesn't this mainly applies to AI driven lists where you know there's market share on that list.

Ty: Mhmm.

Rami E: Right? But it's a concept I call perceived distress. So essentially how it works is let's say you have the Grinch and you have Cindy Lou. Now the Grinch has a judgment pre foreclosure, criminal felony, short term loan, everything under the sun. Right?

Our system is definitely gonna rank him higher versus Cindy Lou hoo who just has a

Ty: lien. Mhmm.

Rami E: Even though our system ranks him higher, that lien could be all she needs to sell, hence the perceived distress level. Because of that, in terms of cutting down lists, getting them to fit people's capability to get through the list, that efficiency rate. Right? It's very important to go first off their buy box, then really targeting the areas that we're actually seeing transactions that are actually worth hitting Mhmm. To cut down the list that way while still capturing the most amount of market share we can.

Yeah. And that's the whole concept behind perceived distress.

Ty: I think too, like, when people get so distressed that, like I mean, what where are they gonna go? Right? Like, how are they gonna get another place? Like, if they have all of these issues, like, they're not gonna be able to rent an apartment. Like, they're not gonna be able to buy another house and stuff like that.

So, like, at some point, they become so distressed that, like, they physically can't sell even though they probably want to.

Rami E: Unless there's a creative solution.

Ty: Right. And, like, we have people who are, like, just going after, like, sub twos right now because I think last the last episode that you guys did with Tim Harridge, like, two episodes ago, like, he was talking about how much, like, equity is available, but also, like, how good of rates people have in the total US and, like, just targeting, like, those kinds of people. You know? And I think that doing that kind of targeting really gets around, like, the whole issue about them being too distressed as well. Yeah.

Just having a lot of, like, arrows in your quiver, if you will.

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Steve: Alright. So we got Danny Fee in Vegas saying this conversation is fire. Thank you, Danny. I was really looking forward to this, and I think it's really timely. You know?

Like, there are a lot of people that suddenly became AI experts overnight. It kinda reminds me, so I started real estate 2007. You know? And Great time to start. Yeah.

Great great, great time to start. Right? A lot of stress. A lot of lot of, unnecessary scar tissue are still from those times. But in that time, you know, they were saying social media is the next thing, and video is the next thing.

And by the way, they went wrong around this time, right, from 2009 to 2011. The problem was not the message. The problem was the profits. Right? Like, every person that was trying to pitch me social media services and video services was a failed loan officer or a failed realtor.

Ty: Right? Yeah. They just jumped over on the bandwagon.

Steve: Yeah. Like, okay. So you couldn't sell houses, and now you're gonna try to sell me social media. Right? So, like, it was very, very aggravating.

And then about three months ago, maybe, when did when did CHTPG come, like, really hot? January. January.

Ty: Year. Yeah.

Steve: This year. Okay.

Rami E: Yeah. And then four point o really pushed it to the next level. Yeah.

Steve: So in January, everyone became an AI expert overnight. Right?

Ty: That's true.

Steve: So I really was looking forward to this conversation because, obviously, you guys have been geeking out on this for for some time now. So yeah. So when Danny says this conversation is fired, absolutely. I've been looking forward to this conversation. And, you know, we've had some conversations prior to this.

So, question from Danny is, do you track burglaries? That's an interesting question.

Ty: So we do. I'm trying to, like, think of the right way to frame this. So criminal records specifically, and and that's subsected out by misdemeanors and criminal felonies. So those are very different things. For those who don't know, misdemeanor would be like a traffic ticket at the lowest level.

And then a criminal conviction can be very, very serious things like arson, murder, things like that.

Rami E: Burglary.

Ty: Burglary. Yes. So, yes, we do track that, and it's built into our models as well.

Steve: But you're tracking on the person's side?

Rami E: Yes.

Steve: I think his his his interest because, he's talking about, you know, is that a Oh, I'm also talking into. Yeah.

Ty: So those are built into our models. One of the things that we're doing pretty soon actually is actually segmenting every single list in our data points. And then, like, obviously, it's applied in our models, but it's gonna be like, hey. Here's the score, and then here are the top 20 distressed factors for every single And criminal convictions is one as well. Yeah.

And it's a very, like, effective list, I would say. Like, it's very niche, but if someone has a criminal felony and they have a house, like, they're going to jail. So, like, they gotta probably sell. Right?

Steve: Well, that's like we have the criminal list. So we have that in here. Like, so, we have so I started a nonprofit organization only to pull data from Maricopa County. So so I have I'm able to pull all the criminal convictions in Maricopa County. Interesting.

Right? I'm sorry. Not convictions. Trials. I can pull everyone that's going to trial Oh.

Ty: For That's even yeah. That's even better. They're not convicted yet.

Rami E: But Yeah. They're not convicted. They might be thinking I need to talk going to trial.

Steve: Yeah. So I have access to this data. Right? And so, you know, this is you're you're having it, like, after. Right?

Like, after they Yeah.

Ty: They've been convicted on both of these, and that's for each of them respectively.

Steve: So that's just, you know, fun fact for the day. That's hilarious. Danny Fee also has a follow-up question is, do you have any features that Square to likely have an owner being open to create a financing from exiting?

Ty: So we don't have a model for it, but we have a filtering system for it. So, like, interest rates is obviously a big thing and so is equity.

Rami E: Mhmm.

Ty: So a lot of people are going towards, like, a free and clear or high equity to overlay into their stack list, which is great. Mhmm. I think I would say free and clear has the most volume of investor transactions, but you have to sort through a lot of hot garbage to get there. Yeah. This

Steve: is how it is. Oh, absolutely.

Ty: But yeah. So we are tracking that based on, like, equity and based on, like, an interest rate if we've got it. Because if they have, like, less than 10% equity, but they do wanna sell, they're gonna be a lot more open to that

Rami E: Mhmm.

Ty: Because they have a great rate, and they have not enough equity to sell completely to an investor. Right. And I think you've been we've had some inquiries on this. Yes. You probably know more, but that's

Rami E: None. That's the best way to say. We don't really have a model built out for it, but we have certain indicators I think we can look towards to really capitalize off that. Yeah.

Steve: Yeah. Well, so what you guys need, and I don't know how you guys do this with data, but to feed in your machine learning, is here are all the homeowners that sold their house, create a financing, and then you guys can filter that out.

Ty: So we have done some digging into that.

Steve: It's so Well, that goes it's hidden intentionally.

Ty: Yeah. It's it's a whole

Rami E: other ride. Hard subset to find.

Steve: Yeah. Like and so you guys gonna have to talk to PACE. It's like, hey. If you guys can submit to me

Ty: Yeah. All the sub two deals. All that training data, like, bring them bring it to us.

Steve: Right. Then after you have the training data, then you could potentially

Rami E: A 100%. Figure that out.

Steve: Because that's all you're missing is the is the feeder data that says they're all the people sold sub two or created.

Rami E: Realistically, having that training data is very important, but having that constant line to continue the training data so it can get better and better over time.

Steve: Absolutely. You guys gonna definitely have to talk to him. And then, you know, I completely failed to mention this early before we went to the break. So, you guys I had some questions here about, how accurate is the data. So you guys are doing something I think is kinda obscene.

Right? In that you guys are giving away your data's your skip tracing, or 5,000 records. Yes. Right.

Ty: For the show.

Steve: For the show. Yes. So for anyone who's watching the show right now, if you guys were to go to the dataflick.com Yep. And you guys do disrupt 5,000, all caps you know, Rami was very clear. Please.

All caps, disrupt d I s r u p t 5,000, you guys can get 5,000 free skip trace records. Correct. Now it's a coupon.

Rami E: It's a

Steve: one time use.

Rami E: Yes.

Steve: So if you submit a record list of 300, that's all you get.

Ty: Yeah. If you're gonna do it, I would highly advise double checking that it's at least 5,000 because you're gonna be leaving money on the table if you don't have it.

Rami E: A very important thing to also add is these are 5,000 hits, not 5,000 records. Right?

Steve: Yeah.

Rami E: That hit rate on our data is at least generally about 92%. But with formatting things like that, it may be worth it to put in, like, 7,000, 8,000 to make sure you get the full value.

Steve: Oh, that makes sense. Yes. There you go. Right? Which, again, I think it's kind of obscene that you guys have given away 5,000 records.

But you guys want that disrupt 5,000 to get 5,000 free records. Question from Omar. So just, again, to answer his question, how

Ty: accurate is tracing data? Get that question a lot. So the best way well, you get this question more than I

Rami E: So accuracy is pretty hard to find. Right? Hit rates, pretty easy. Right? You gave us this many.

On average, 92%, depending on formatting, may go down to 80, whatever. In terms of accuracy, that is super hard to track simply because you're relying so much on the person using the list, the call system, all of that. And it's almost an impossibility unless we're having some API feed all that data back into our system constantly.

Ty: And we started to implement, like, that kind of governance piece that we talked about earlier where it's like, hey. Here's a list of all the verified sellers from our users, and these are people who they found through call and text. And, obviously, they use our skip tracing. So we can go back and trace, like, hey. Here's this number.

Let's tag it as accurate. And we can we're trying to build those systems of metrics to measure performance, but it's a very long tail play.

Steve: And how can you

Rami E: So there's some illegal ways we could do it, but we're not trying to be illegal. Right? Essentially, with skip tracing, there's certain tiers to it. And the marketing tier, which everyone generally uses, you're only allowed to pull from certain databases. Then there's what's called restricted databases, which generally goes up in profile, like debt collection, police officers, law enforcement, etcetera.

Ty: Government usage probably the best way to put it.

Steve: A debt collection company as well.

Rami E: Well, debt collectors being part of that, you have access to a more restricted data set that we're not allowed to use for marketing purposes. Mhmm. However and this, once again, pretty borderline illegal. But if you ran just a sample of our list through that center to be able to see how many of these numbers actually do overlap, you'd be able to get pretty good accuracy rating.

Ty: Yeah. So long answer. We can't give you an exact accuracy, but no one really can. So if they tell you that, they're probably doing some stuff they shouldn't be doing.

Rami E: Yeah. Or they've done just hundreds and hundreds of thousands of cold calls themselves and have tracked all that to have a base level.

Steve: So the best thing to do is maybe take an existing data set, you'll trace the 5,000 for free with you guys, and they just compare

Ty: And then give it a give it a run.

Rami E: Yeah.

Steve: Yeah. And, you know, he's talking about illegal. Right? Like, I asked you guys some questions earlier. You guys, like, we can't do that.

Ty: Like, we could, but we

Rami E: can't. Very much. And I

Steve: was asking you, you know, specifically, like, could you, it would be helpful for me if you can give me a list of every homeowner whose credit score went from 720 to 650 in a week. Because if their credit score from 720 to 650, it's only a matter of time. Either they they stop paying the mortgage or it's a matter of time until their mortgage becomes a problem. Correct.

Ty: Yes. Most of the time.

Steve: Right. It turns out it's illegal to pull that data.

Rami E: Yes. It is. Yeah. Once again, there's certain ways to model it and have indicate indicators towards it, but to target that specifically Yeah. Is not the most legal activity.

Steve: Not Not the most legal, but, man, I'd really love to have that data. So let's see. Follow-up question or a different question from Marcio on YouTube. What type of motivated seller leads can data afflict system detect? So do you guys track bankruptcies, option auctions, high equities, lien, etcetera?

Rami E: All those that you just said. Yes.

Ty: Mhmm. So there's, like, 19 to 20 core ones. The only two that we don't do are probate and divorce. However, we do do pre probate. If you don't know what that is, basically, like, it's the period before we enter probate court, essentially.

You never know.

Steve: It's it's a question that comes up. Right? What's the difference between pre probate and probate?

Ty: Yeah. And the reason we don't do that is because in some states, it's very, very, very difficult to get. Like, an example would be New York. If you wanted a probate or divorce list, you know, from that actual like, to get that, you have to be a probate attorney or a divorce attorney. So, like, getting it is very, very difficult to

Steve: do. Yeah.

Ty: And then as far as the other list, every list that you can imagine, we are tracking it. And that's from, like, some of our more, like, unique stuff, like criminal felonies.

Rami E: Short term loan inquiries.

Ty: Yes. So short term loan is one of my favorites. It's basically, like, if someone's trying to get a payday loan, we see that those inquiries. And if they're trying to get cash fast, most of the time, they would be open to an offer.

Steve: So you guys have access to that data point those data points?

Ty: Yes. It's built into our models.

Steve: Yeah. I mean, it seems like a really strong data point. I

Ty: think it's fire. It's my favorite one.

Rami E: We like that. Zombie properties is also one that I very much like. Mhmm. It's essentially if it's vacant and about to go into foreclosure. Yeah.

Steve: Well, I mean, maybe something we could talk about is an idea I've had, but I've never gone and executed, which is advertising at all the payday loan places. Right?

Ty: Yeah. I love that idea. And, like, almost sorry. Go ahead.

Steve: I was gonna say with geofencing

Rami E: I was about to say, yep.

Steve: We could just geofence all the payday loan places. We could do that.

Rami E: And then start doing targeted ads to that followed by a mailer or a call Yeah. Or text to push them in that direction after a two week period. Yeah. Right.

Ty: I mean 100%.

Steve: We're we're gonna talk about, like, you know, geofencing that was a payday loan place. Yeah. Geofencing, we can retarget them. And then once they show up on the website, if they click on the ad, now we can retarget them forever on Facebook and everything else.

Rami E: Mhmm. A 100. And we can even tie that payday back to our system to see this user, their mailing address, are they on our list for a high likelihood to sell or not. So it's even a extra layer of governance to save on marketing.

Steve: Yeah. I mean, we can geek out on this stuff all day.

Rami E: Yeah. How much time we got?

Steve: Alright. So, Russian, Nico, and IG. So, DM, geofencing pricing, even though relatively affordable seems like barrier to entry for most. However, the question is, what does a good Hulu ad look like? Okay.

So if you were to create an ad on Hulu, what would it look like?

Ty: Obviously, we don't I can't just show you right off the bat, but if you for those who are listening, just type in, I would start with Opendoor and Offerpad. Those are going to be top tier, like, highest level, those people. Because I think they actually have their commercials on their YouTube, just because they're running paid ads to the actual YouTube channel. So most of the time what people do is they'll, like, post the video on YouTube and then drive traffic towards it. So those will be two incredible examples.

The other thing, I've seen a few ads on, like, webuyhouses.com. So maybe look up their YouTube channel. They'll have some. And are they ads? Yeah.

Those are some That should be a good start. Yeah.

Rami E: Yeah. And, like, these commercials, at least with the initial testing, don't have to be the best commercials in the world by any means. Right?

Ty: Think of them as, like, virtual bandit signs. Like, don't overthink it. Yeah. And, like, I've seen about a 1,500 for, like, a decent commercial. You're trying to get crazy, like, 3 to 4,000.

Mhmm. But, like, with our geofencing stuff, as far as, like, the pricing goes to do that, our users are gonna have access to that for free. And then we're probably gonna make it a paid, like, standalone service as well for people who aren't current users.

Steve: How long till that's rolled out?

Ty: That's the question. We we're we're trying to figure out the right amount of testing before deploying it, and I think we're gonna gradually, like, break it out to, like, more and more users every month.

Rami E: Mhmm.

Ty: But I would say, like, thirty to sixty days.

Rami E: Yeah. So the biggest thing that we kinda talked about with this is it's a stacking effect. Right? So month after month, you should expect more traction as your brand name builds all of that. So we wanna give it adequate time to truly ramp up as much as it can so we can see those results.

Ty: At least where it's like, here's what month one looked like, month two looked like, just to have a baseline. Okay.

Rami E: I was

Steve: gonna say because we're ready to test.

Rami E: Yeah. Of course. Once again, we'll talk after the show.

Steve: I guess a a follow-up question. Right? Because, you know, Omar's question about how accurate is is the record. You guys appear to be underpricing the market at 8¢. Right?

How are you guys able to do it? Because, like, I typically see 14¢, and if you spend, like, a large amount, it gets down to 10¢.

Ty: I mean, like, I feel like our whole thing is we saw I I don't wanna say price gouging is the right word. But for us, like, we feel like we can produce a good product for 8¢ that we're also happy with Mhmm. And we know people will get success with. And we're kinda taking the the mindset of, like, we want people to crush, so they obviously stay with us and get more data and things like that. Right.

And I think when people are charging, like, 15¢, 20¢ a hit, like, some people do, like, I don't know how you can be profitable, to be honest.

Rami E: Yeah. Unless it's like detective level research, like one off pools. Mhmm.

Ty: Yeah. There's definitely a place for, like, that crazy, like, caliber 100. Get tracing. Right? Like, if you're trying to door knock these people and you got, like, a list of a 100 people, maybe do, like, the 20¢ hits that are, like, private investigation level.

Right. But we're just trying to be, like, an affordable option for people.

Steve: Gotcha. I was gonna have another question after that. Dang it. So Ryle says he loves all this information. K m a g e on YouTube.

What is the difference between DataFlick and Authentic?

Ty: Yes. You I mean, you get that objection.

Rami E: I I do get this question a lot. Obviously, I've never looked at Audantic's, like, data to the degree of, like, knowing what their models are, knowing their raw data sources because I just don't think they'd give it to me if I ask. You know? But so getting past the model part, which there's decent overlap with our model. In fact, I know some users who are using both us and Audantic.

Mhmm. And one thing they love to do is stack us together in mail. Right? Because if it's on two completely independent MLAI models, it's most likely the right one to hit. Per Sean list.

Yes. But then it gets into the structure. Right? So for us right now, we're a six month agreement paid monthly.

Ty: Mhmm.

Rami E: And we include skip tracing into our list as well because we wanna, once again, really encourage that multilevel marketing.

Ty: Mhmm.

Rami E: Now if you have any external list, let's say you pull a prop stream or a batch list and you want to skip, if you're a user, we'll skip those at 6¢ a record.

Steve: Mhmm. So even cheaper.

Ty: I will say to build off of that, there are some people always ask us, like, hey. Do we need to pull, like, other data points, like, with your data? Mostly, no. But you can get some really good list from the county that, like, you physically cannot get anywhere else. So an example would be, like, environmental liens or, like

Rami E: Water shut off.

Ty: Water shut off is a great list. So, like

Steve: It's actually the reason why I started that nonprofit to to begin with, but What was

Rami E: the not

Ty: was the

Rami E: larger talk more about this nonprofit after the show as well.

Ty: But, like, overlaying those lists in, and skip tracing them, that's something what we also do.

Steve: Yeah.

Ty: And, like, there are people who, I would say, do that pretty regularly.

Rami E: Yeah. 100%.

Steve: Yeah. So one question I I've had in the past. Right? Because I wonder, you know, you're you're charging in your instance, 8¢ a record, other instances, you know, 15¢ a record, whatever. Is it a situation where you're pulling it once and then you're charging multiple times?

Right? Because, like, let's just say Maricopa County, where you said it's the third largest county in the country. And every wholesaler seems to be here, it feels like. Right? Like, because this is, a, the guru capital of the world.

And then on top of that, we have a bunch of people starting here. So if we were to skip trace a house up the street, 123 Main Street, right, the most popular address in in the world, if I was to skip trace it, right, could you not house that record and then not have to pay for it again, I don't know, for a week or a month, whatever.

Rami E: Yeah. So there's definitely time limits on it. Right? You don't want the number to get old and stale. You wanna refresh that as often as you can.

Yeah. But not if two people back to back skip the same record. Right. Yeah. It's already housed in our system for sure.

Steve: It's already housed in our system. So when someone's charging 15¢ a record and their cost is, I don't know, 3¢

Rami E: Yeah.

Steve: I have no idea.

Rami E: It depends on if they're white labeling it or if they're pulling it themselves. Yeah.

Steve: So if they're pulling it themselves, they might have paid 3¢ for that record, and they're selling it five times for 15¢ each.

Ty: Multiple to multiple people. Multiple.

Steve: Yeah. Is that accurate, hypothesis?

Rami E: Yes. Yeah.

Ty: So it's interesting too. Like, the I have no idea how other people do it. Mhmm. But, like, most of the industry is repurposing the same databases. And, Well, they

Steve: hear what I hear all the time is, like, everyone's just resounding IDI. But, anyway, continue.

Rami E: Okay. Yeah.

Ty: I didn't wanna say that. That's what most people do. So for us, we don't work with them at all. They're not one of our sources. But we take those 6,000 sources, and we update that last reported date every month on all of our users' skip tracing data.

Mhmm. So that's, like, I guess, the big difference. Because, like, the numbers may be the same, but that last reported date could change a lot. Mhmm. So, like, say for example, I've looked up, you know, family members who we know and, like, depending on, like, our source databases, someone may have a active phone number, like, two years ago, but that's still their number.

Like, it just could be how it fall like, falls. But I've also seen examples where, say, we've had a, you you know, list that we skipped or, sorry, record, and then a new source comes up or they change a source that they're actively using. And now all of a sudden, they go from a last reported date from 2020 to this month. So that completely changes the order of the skip tracing that we're providing people. And that's something I really haven't seen people do.

Rami E: And on top of that, using it almost as a filtration system as well. So what we found and this was through initial testing. Right? So every county, different. Don't shoot me if this isn't your calculation.

But, once we started almost having caps on that last reported date using as a extra governance, extra filtration, we saw it remove about 50% of wrong numbers while still kind of preserving the accuracy of the list, which is pretty massive in terms of, once again, the efficiency piece with cold calling.

Ty: Right.

Rami E: I mean, back in the day, we were giving out, like, 10 numbers per record, which it, looking back, is a nightmare.

Ty: Yeah. And that's, like, pretty common practice for most, like, other skip tracing.

Steve: So you're saying of the four numbers we get, up to the four numbers we get, all four of them were accurately reported at one point.

Ty: In the last two years, they were reported.

Steve: Interesting.

Ty: Yeah. That's a huge part that I actually left out. Yeah.

Steve: So because I I know we talked about, you know, like, at least one of them was. We're using all four at one point another were reported

Rami E: Yes.

Ty: In the last two years. So a great example, I've looked at my my parents' address many times as test sequences. So we used to have a home phone. Mhmm. That last reported date on that was the same year we cut the line off, which was, like, back in 2012.

Rami E: Right.

Steve: It's almost like a credit report. Exactly.

Ty: It's refreshed every month.

Steve: Last reported date.

Ty: And we run monthly updates on all source databases, which obviously changes the skip tracing a lot.

Steve: Right. So, guys, you know, the please fire away with your question. I mean, I see a bunch of eyeballs in here, and, I I think that to get access to this wealth of information is is very, very uncommon. So please, ask more questions here and, like, you know, we can geek out all day, but I wanna make sure you guys get your questions answered. So, Ernesto on YouTube, the phone numbers provided, are they scrubbed to see if the phone number is disconnected or connected?

Ty: Yes. So we run through two other governance databases. So one of them is we remove business lines and, like, VOIPs, if you're familiar with those. And so, like, we actually run those databases or sorry, run those records through, like, Google as an example. And we have a system to detect if it's associated with a business line.

And, obviously, that's not good, so we remove them. The other aspect is we do remove the TCPA litigation risk records. So it's like people who have filed lawsuits or could be seen as high risk to doing direct outreach to them. So we remove those, no questions asked. And then we also overlay an element of if you want DNC removed, you can choose to do that, but it's an option because I know some people

Steve: are fearless out here. I mean, if you're gonna not call the DNC, I don't know. Any deals you can do. I agree.

Ty: I I've seen some insane stats on this where it's like 60%. 50 to 60% of all skip trace numbers are on the DNC.

Rami E: Well, on top of that, unnamed user a, we will say, does indeed reach out to the DNC, and 60% of their deals come from cold calling and texting the DNC.

Steve: Yeah. I don't know how you could run a profitable business Yeah. Opting out of it.

Ty: But massive disclaimer, we do not condone doing that.

Rami E: Yes. A 100%. We have Yeah. The system there so you can check the button and not have to worry about it.

Ty: Or not check the button.

Rami E: I was Completely up to you. But once again

Steve: As a business owner, you gotta make a business decision. And I my, like, my implication on which business you need to make is pretty strong. What's the approximate cost in geotargeting, a payday loan type of insurance, and does VPN factor in IP targeting? So if someone you know, we just had this idea. Right?

We're gonna geofence payday loan companies. Right? Yes. What's it gonna cost to just geofence those?

Rami E: It heavily depends on the traffic to the payday places. So that's the very interesting part of this.

Ty: A good metric. So about 10,000 records cost about 3,500 to, I mean, absolutely blanket these people. Like, they will see you everywhere. And and that's, like, also been tested of, like, you need that 35¢ per month hit is per, like, record that you're reaching out to is a good rule of thumb based on our metrics of testing thus far. Yeah.

And I wanna say that you can actually tailor the campaign. Like, whatever the campaign is, so say we're going to payday loan Mhmm. Example. So that's, like, should still be 35¢ a hit. It doesn't really matter the targeting.

Yeah.

Steve: This is completely different than what we talked about so far. But is there a way, for us to skip trace someone that shows up on our website? Because I know that was a service that was provided before by someone else, but they never rolled it out, I think, for legal reasons. So I show up on dataflick.com.

Ty: Mhmm.

Steve: Could you still trace me based off your IP address? And that's the reason why I did the whole IP address. Interesting.

Ty: So I don't know that question. I could give you a good shot at it, but I just don't know 100% for certain because the IP address stuff is technically like a privacy falls under the privacy act stuff. I think you can't do that. I think it's well, we can do that, but we

Rami E: can't do that. That's And

Steve: I think that's exactly what it was. I think, like, because we were he was gonna roll it out, and then it never rolled out.

Ty: I I know, like, I mean, if you're trying to do this at, like, the very granular, you could actually probably export those IP addresses and have a VA take them. And then there are so many providers of IP address reverse lookup, same technology as reverse skip tracing lookup. But you just type in the IP address, it'll spit out a person take person, skip trace manually. Probably the best bet for that.

Steve: Yeah. And that's where I was going, like, within five five miles. Like, this is Yeah.

Ty: I know. That's

Steve: That's not helpful

Rami E: at all. Good. Yeah.

Steve: Yeah. So guys, keep firing away on your questions. I'm gonna ask some more questions over here. So, you know, you guys, obviously, when you started this, there it sounded like you were landscaping, kinda like a little bit lost. You were doing marketing, but still kinda young and kind of figuring this out.

Have you guys figured out your your purpose, your why, why you're doing what you're doing?

Ty: You wanna start?

Rami E: Sure. So, once again, for me, friends and family is massive. Right? I have a massive family, and friends are, like, everything. They helped me through the hard times, when I was going through a lot, when just everything.

Right? So, yes, taking care of them is massive, massive, massive on my list. But even more than that is I love when my friends succeed. So for example, I have a cousin, Logan. Hey, Logan.

He's a chef, and he's a sous chef right now working his way to be head chef doing phenomenal. If I could help him open a restaurant one day, like, that is what keeps me going a 180%. Or, like, my brother's always talked to he's a mechanical engineer.

Steve: I know you have

Rami E: the engineering background too. So he always just loves tinkering with things. Mhmm.

Steve: If I

Rami E: could help fund an invention of his one day, something like that, like, that's definitely my why.

Steve: Awesome. How about you?

Ty: I think the whole, like, entrepreneurial, like, journey kinda started with freedom because I was like, I really like, I was actually started in mortgage and was fired from a mortgage job, and I was like, I I don't wanna rely on other people ever again. So that's, like, what started that journey. And it's sort of freedom. But now I think, like, as I've gotten, like, older, like, more mature, it's turned into very similar to, like, him, like, you know, supporting and, like, giving that level of freedom to, like, my future family is massive. Mhmm.

Because, like, I don't want, you know, them to feel, like, that they have to do something. I would much rather them do something that they, like, enjoy and want to do. And, like, I think there's a lot of ways that our, like, understanding and knowledge through tech could, like, make a pretty big impact, like, in other ways, like, just around the world. So, like Mhmm.

Rami E: You

Ty: know, we have friends who are working on tech projects on the machine learning space that are tailored to strictly, like, you know, detecting cancer as an example. It's like my mom's a radiation therapist. So she, you know, it deals a lot with that kind of stuff and, like, just there are so many use cases where machine learning can be used and, like, AI in general to, like, better the world and even, like, through, like, green technology and stuff like that. And, like, that stuff's, like, super important to us. Yeah.

So I think it's kinda morph and it's very dynamic. Like, it changes a lot. But

Steve: One thing we didn't talk about, you were in mortgages. What were you doing in mortgages?

Ty: Yeah. So, the origin story kinda goes where I dropped out of college, and then I went from that into, so my parents' neighbor, he was an executive at a mortgage company in Tennessee. Pretty large. Not like, you know, institutional level, but they probably have about, like, 90 branches in Tennessee, so decent size. And so I asked him, like, hey.

Kind of a job. I wanted wanted real estate, but I don't wanna go to school for it. And so he was like, we actually have a an appraisal, like, risk management job Mhmm. That's opening up, but you're gonna have to, like, get an appraiser license. I was like, alright.

Well, I just dropped out. Nothing else to do. So I just went and do that whole process, which is pretty, like, in-depth. I don't know how much you or the viewers know, but, like, you basically have to do about eight weeks of, like, actual education and, like, take an exam and then hang your license under someone who's already an appraiser and things like that. So I essentially did that, joined a mortgage company to, like, lead risk management for, like, the loans that we were actually, like, approving or not approving based on the appraisal.

And from there, like, I learned a lot about underwriting real estate, which was great for, like, flips and stuff.

Steve: Great experience.

Ty: Then from there, I was, like, need some sales experience. So in the same company, I got my mortgage license to be a loan originator and did that for, like, six months. Hated it. And I was underneath someone, so I was technically an assistant because it's the same kind of, like, like, trajectory. And, like, we were pretty slow, so, like, I was fired from that job and then offered the appraisal risk job.

And I was, like, back. And I was, like, don't wanna do that at all. Yeah. And that's kinda what started the, like, the whole, like, I wanna do real estate thing. And that was when I was, I just, like, house hacked as well and, like, had roommates.

So I was living for free and just putting all the money I was making back in education. And from there, that was when I got into Fortune Builders, which I don't even think that they're, you know, an entity anymore as far.

Steve: They just closed or something.

Ty: Yeah. So that's where I started. It's crazy expensive, massive risk, and, learned a lot really fast, and that was, like, the big community that I got connected with through Tiff and Josh. And then three months later,

Rami E: I was

Ty: like, Romy, gotta do this real estate. And how

Steve: did you guys get connected?

Rami E: We have known each other since sixth grade. Yeah. Yep. Went to school in Alcoa, Tennessee. I got him to play soccer in eighth grade.

That was the start.

Ty: Like so, yeah. Super humble beginnings. Like, I mean, through that, like, whole time of, like, learning, like, we were doing Insta Cart, DoorDash at night, like, you know, saving all of our money, living off of virtually nothing because we were I had house hacks, so we were just doing that. Mhmm. And then going to Columbus was, like, the first job I had since, like, I've been in the mortgage space, and it was obviously just to, like, learn as much as I could.

And even then, like, I mean, we you lived on the floor.

Rami E: Oh, yeah. I had no money.

Ty: We had, like, 650

Rami E: square foot studio a mattress. I would've slept on his couch for three months, hands down. Like

Ty: and now we were just super hungry. And, like, we I think, like, a big thing for us is, like, we were so dedicated and focused on making this work that we were willing to do anything.

Steve: Yeah. What's you guys' big struggle right now?

Rami E: Right now?

Ty: I would say, like, we're just trying to get to the point of, like, having a nice foundation of scaling. So, like, DataFlix itself, like, we haven't spent anything on marketing, and we've grown completely organically to this point. So, like, we're almost laying the foundation of, like, you know, products that we think will really move the needle and making sure that we have a nice foundation to, like, make really good hires and to expand more than we already have. And, yeah, like, just making sure that that's done right is very complex.

Rami E: Right. A very common thing you hear is don't work in your business, work on your business. Right? And I think right now, we are definitely working on the business. Our whole goal is to keep this growing as much as we can, but there's still things that we're definitely in in the business at the moment as well.

Steve: Yeah. And what are you guys' superpower?

Ty: You wanna go first?

Rami E: Is this, like, me about mine or me about his?

Steve: It's easier if you guys talk about each other's. Alright.

Ty: Oh gosh. That's a hard question. So for Rami, I think he I mean, we talked a lot about empathy here. So, like, Rami's ability to work with other people, whether it be, like, another, like, employee or, like, me or just, like, our clients or users or whomever, his just conviction to make sure that they do well and, like, the level of care that he has Mhmm. Is incredible.

And his ability to, like, just care for people and, like, be a really good friend or, like, a really good business partner or a great, like, software to a user, I think is, like, how why we're here. Like, we've made it this far because of that level of conviction that he's had Mhmm. Through this whole process, I think.

Steve: Yeah. What what is Thais' superpower?

Rami E: I would say Tai's just honestly the ability to keep pushing us forward. Like, I know I definitely have those moments where I'm like, oh, we should do this, this, this, but I always have that voice in the back of my brain, like, oh, but look at the bank account. Oh, look at this. Look at that. Ty is just phenomenal at painting the picture of what's to come with the company.

Like, hey. What if we type this in? What if we add this integration here? And, like, there's a lot of big things that we didn't talk about on this show because we're just not there yet that I know will be included in the future because he's constantly pushing us.

Steve: Yeah. So

Rami E: I would say constantly look out for the future and guiding us on that path. Yeah. It's probably

Steve: him to prepare. Thing was possible.

Rami E: 100%.

Steve: Gotcha. Thanks, man. Which failure did you guys learn the most from?

Rami E: So many.

Ty: Oh, man. Like, it's gotta be hiring. So, like, for us, we have gone through, you know, hired and fired. I don't wanna say, like, dozens, but probably, like, 15, like, different people at least in the last, like, three years that I was so sure were great, and they just weren't. So, like, really honing in on, like, what we needed.

And a lot of that, like, failure, I think, is, like, on me for not really building the systems right. And, like, when I've looked back on it in the past, like, these people were almost set up for failure. Mhmm.

Rami E: So I

Ty: think, like, focusing on hiring in general and, like, learning some really hard lessons on that front was

Rami E: So I'd say it's a mix, though. Right? Because we're also, once again, pretty bootstrap back then. Like, we're at a point where we were trying to make this amazing system happen with not a lot of amazing resources to create. So I think a lot of that comes down to sure.

We were we definitely learned a ton. We weren't nearly as educated as we should have been on that hiring process or what the results should look like as much, but a big part was the pressure of where we were. Yeah. And I think that got to us I wanna say got to us, but made us make some poor decisions on that front.

Ty: Things were probably more rushed than they needed to be, I think. Yeah. And, like, just slowing down having to that consistency.

Rami E: Like, if we had the dev team we had now and just decide to build that out at the very beginning, it'd be a very different ballgame. Right? Right?

Steve: But, you know, you go back to hiring. You know, Ren is someone that, you know, obviously, we all know pretty well. He actually has, like, you know, part of our sales leadership training is, like, how to effectively identify and and keep the best people because that's one of the hardest things. Right? Because, like, how do we truly figure out if this guy is good or not?

Because I'm like you. Everyone I've hired, I thought was going to be amazing.

Ty: You're like, banger. I I know they'll be awesome. This is

Steve: I hit another grand slam.

Ty: Yeah. That's exactly all

Rami E: I know.

Steve: And then it turns out they're not all grand slams.

Rami E: No. Actually, some

Steve: of them are strikeouts. So, question here from Paul. How can I, subscribe or or check us out? Again, go to dataflick.com, disrupt 5,000, all caps, and you can demo, all of this. Oh, he said he just ran it.

Yeah. Data flick is doing better. Oh, cool. So he already did he already did it. That's pretty quick.

Awesome. Nice.

Rami E: Did he do all 5,000 hits?

Steve: I don't know. But that was pretty quick, in a matter of minutes. Perfect. So I want you guys to, think about some last thoughts, you guys wanna leave the listeners with. I'm gonna make a couple quick announcements, and then we'll we'll go with that.

So, guys, you know, I personally see an opportunity in this market. I'm excited to seize the moment. Actually, had some pretty exciting conversation I was sharing with you earlier, about, you know, some opportunities and how we might be able to capitalize

Rami E: on it. Mhmm.

Steve: So, if you guys have capital and you don't know where to get started, you can invest with us. Or if you have killer deals that you need help to close on, you can partner with us. Go to, teamwithsteve.com, and maybe we can do business together. And if you guys have value today, you know, I hope you guys did because I got a ton of value today. Like, subscribe, share, and comment.

And then tune in next week. We got Jack Bosch. He is the land guy, of all the people. I think he's the one that is, been doing it longest and and knows the most. So what are some last thoughts you wanna leave the listeners with?

I'll start with you, Rami.

Rami E: The biggest thing is let the data tell the story, I guess. Mhmm. Like, I feel like too many people go off of, oh, I heard leans was really good today or I heard this or that. Even right now, we're building out what we're calling kind of a market map where even if you're not using our data, we can look in your county and say, yeah. In your county, there were 7,500 liens, and from those 7,500, 500 of them sold to an investor Mhmm.

In the past three months or just example. Right? But let the data do the talking is the main thing. Follow your KPIs, actually track everything, just but trust the data.

Steve: Yeah. Trust the data. That's the hard part.

Rami E: That really is.

Steve: Because we wanna go against that. We wanna go with our gut, but the data is screaming at you.

Rami E: 100%.

Steve: Go with the data.

Ty: So we talked a lot about this before, the show and everything, but I think, like, the biggest testament to, like, what led to our success was just being really consistent and focused. Mhmm. And I really believe that if you are like, I wanna do this business, you know, it's not real estate, whatever it is, If you're completely dedicated and, like, consistent and focused on that, it's gonna happen. It's just a matter of time. And there's so many times where we're like, we almost quit.

I mean, we're like, close. And I'm obviously very thankful if we didn't, but I think it's just, like, enlightening people that that's pretty normal. And you have to remember to just be consistent and focused on one thing. Because, obviously, I my brain never stops. I'm always calling.

I'm like, what? Can we do this? Probably shouldn't do it. But I'm the most guilty of that, but I think that's something that we've learned over the years is insanely important. And I wish I had, like, had more focus, like, earlier in my life, I think.

Steve: Yeah. Well, I think it's guilty. A lot of us are guilty, especially, you know, if you're an entrepreneur. Right? So, you know, we talk about, the importance of focus and how easy it is to get distracted.

The other thing too is about consistent. And that's what the one thing I put in my message. Right? You know, if you'll take consistent action. Love it.

And, I I I did a presentation a few weeks ago, and I I shared the asterisk. Right? We all talk about I'll do whatever it takes. Like, how many of us said I'll do whatever it takes? But there's an asterisk there because I'll do whatever it takes, and the asterisk underneath that is except for being consistent.

Like, that's the one thing.

Ty: Right.

Steve: I'll do whatever it takes to be to be successful except for being consistent. Because I think a lot of us kinda let ourselves off the hook Mhmm. With the consistency component.

Ty: 100%, man.

Steve: Yeah. If someone wants to get ahold of you guys, how how should they do that?

Ty: I believe that there are some links in the show notes. Mhmm. One is for an investor demo if that interests you. And, obviously, the skip tracing coupon, make sure to use that. But I think, if you just follow the links that should be in the show notes, That is probably the best way.

Rami E: Schedule a call with me. Love to talk to you. I got you guys.

Steve: See it right here.

Ty: Info at DataFlick is also, like, the generic email that people just send when they have inquiries or, like, questions and things like that. But, yeah. So

Steve: Alright. Yeah. Perfect.

Ty: Thank you so much. Thanks, man.

Steve: Thank you. Thank you. Alright. Thank you guys for watching. I'll see you guys next week.