Episode 29 - Interview with Dave Anderson CEO & Founder of EvenFlow AI

Good evening, gearheads and grease monkeys. It's Fixed Ops Focus, the only podcast where absorption rates are higher than our blood pressure, with Dave Rogers and Steve Shaw. Welcome, welcome. What's going on, Dave Rogers? How you doing today, sir?

Doing fantastic there, Steve Shaw. First day of baseball season.

Baseball season, Cardinals playing today. I'm sitting here recording a video, missing my Cardinals. Yeah, definitely. Definitely a little bummed out about that, but we'll get to, we'll get to see a replay at least, you know.

There you go.

Hey, I see you brought a friend today. You know, the last time we got together, we were talking, or one of the, a couple, couple days ago, we were talking about appointments or non-appointments. And I kind of was in that non-appointment camp. You know, I'm kind of like, bring it in right now. And you were a little bit in the, let's make an appointment, but it's okay if you're not. So I see you brought a buddy with you here to kind of give you a little, ammunition about making appointments, right?

Yeah. To bring some muscle into the conversation. You know, you and your, you know, run it ramp— running ragged with no appointments and everything else. So yeah, we have Dave Anderson from Even Flow AI, and he's put a little bit of science to the appointment world. And welcome aboard, David. Nice to— nice to have you on.

Before we get started, I got to ask you a question. Do you specifically bring people on that are bald? Is that like This helps support the argument.

Pretty much one of the second, the first or second criteria, they have to have no hair.

Okay, good. I just want to make sure we got that. Sorry about that, Dave. I didn't mean to interrupt your story.

No worries.

Tell us about you. Tell us about Evenflo. Tell us about appointments. What do you think? I'll let you guys drive this conversation.

Sure. I'll just say since I'm biased, cause I built a company that is helping service departments schedule more intelligent appointments. So I'll just put it out there. I'm for appointments. I'm not against it. I'm for it.

Very good.

Very good.

Yeah. So we've talked about it before and, you know, we actually met briefly at NADA and had a nice little chat. You know, I think we've talked about it before. Steve and I have too. And I think there's room for everybody to leave some, you know, you gotta leave some walk-in time or, you know, upsell time or, you know, I had a conversation with somebody today and, you know, they for some reason they still, you know, book right up to the edge of the day on Mondays. And I'm like, you don't leave any room for emergencies on the weekend, you know? So I don't think any of us are against, you know, walk-ins or emergency appointments, but, you know, I think to base your business on just willy-nilly and just see what walks in the door is kind of, it just kind of takes, you know, some of the, I don't know, just takes some of the business out of the business, doesn't it? You know, I can have a hot dog truck and just hope people come to my store during the day, right?

Yeah. Well, you know, like it's, uh, another analogy you could think about is, um, you know, would you run a restaurant and not know what food is delivered in the morning and say like, uh, oh, I guess we didn't get any onions, so we better change our menu around. Right. Like, so, um, I, you know, I think about it as like any operation, you kind of want to run it like as best as possible as quote unquote a factory. And part of that is controlling the inputs into the factory to maximize the output. Right. And so I get it. Service, it's really difficult because you've got a lot of variability. Like Monday's busier than Tuesday, but maybe not every Monday. You got a lot of unpredictability, as you mentioned, Dave, right? Because you got walk-ins, you got no-shows, people are early, people are late. What people schedule for is different than what's on the repair order. You know, like if I have 100,000 miles on my Odyssey for an oil change, probably going to get something more on my RO, right? So this, all this variability and unpredictability makes it hard to control the inputs. But as best as possible, you got to try to do that to optimize sort of the factory, quote unquote factory in my mind. So.

And what do you, and so you have software that, how does it kind of work? I mean, you know, give us a little bit of overview and give the audience a little overview.

Yeah, well, before I go there, let me, if you don't mind, I just, 'cause Damon was talking about in the other, you know, how leveraging other practices, so industries. And I just was thinking about like, how to tie it or get to think about like the whole appointment. And I can bring it back to Evenflo if you guys are interested, but like, um, um, maybe the first thing is like an emergency room, right? Like, um, you know, why does it take 6 hours for when you go to the emergency room? It's an emergency, right? Like, what's the problem? Right? It should be fast. And there's really two problems that drive it. First of all, unfortunately, Steve, this will go first against you. It's like, it's 100% walk-ins. So there's no appointments. In emergency room. The second challenge is everything's variable. And, you know, it's like, Dave, you said in your other podcast, like you get 5 Cherokees with, you know, check engine light, you get 5 different jobs, right? And you think about emergency room, well, I have an ear infection or I need stitches or I have a stroke or I have a heart attack, right? The, the level of resources and the type of resources that's required is hugely variable. And so, and then you got the unpredictability of who's showing up. You know, and so that's why like an emergency room, it's like 6 hours, right? It's, you think it would be fast, but you know, you have all that variability, unpredictability. So it's a factory with no inputs or controls, right? Like it, it just takes what it gets and then you have, you know, a, a suboptimal system. And then on the flip side, you know, from a surgery center, they, you know, assuming there's no, it's not an emergency or, you know, it, there's no complication, it, it runs like clockwork. Why is that? It's because they know what you're going going in for, right? Like you, you don't go to get an ACL surgery and then while you're there be like, yeah, well, while you're talking to me, doctor, yeah, take the appendix out too. Right? Like, right. You're not upsold. You're like, I'm here for my ACL and that's it. So you can plan for it. Now that when you think about the service, in my mind, like you'd love to run it like a surgery center, but then you, you, you have too much variability and predictability. And so that's where Evenflo comes in is trying to, you know, trying to help overcome those challenges. The ER to make it like a surgery center as best you can to control those inputs. And part of those inputs of like, is like there is gonna be walk-ins, there is gonna be no-shows, people are gonna be late, people are gonna be early, but as best you can try to like understand those inputs to optimize the output.

You know, it's funny, it's funny you said that because I used to use a dentist office as an analogy, right? So you make an appointment for a dentist office and they, you know, they bring you in, you have a 1-hour teeth cleaning, x-ray, whatever the case is. And then if they find anything wrong, they reschedule you. They don't, they generally don't do things the same day. Right. So next Tuesday at 1 o'clock, you're going to come in and get your teeth filled or you're going to get a root canal or whatever the case may be. Right.

Yeah.

You know, if you have extreme pain, they might block out a little bit of period of time. Like one dentist I went to from like 12 to 1 was blocked out for emergencies. But, you know, I've always felt like, geez, I wish I could do that. You know, bring your car in, do an oil change, do the multipoint, and then sit there and rack you up and say, okay, next Tuesday, Steve, when you come in, your radio will be in. I'll put your radio in. And oh, by the way, you need your 15K service, we did your oil change, your tire rotation today, but we're going to do another 2 hours worth of work, right? But we all are afraid of doing that because the customer is going to go somewhere else and shop us, right?

Yeah. Or just never come back, right? They come back, never come back.

Right. But it always amazed me, you know, you go into the dentist, you get, you get your teeth looked at and they'll bring you back 4 times. I'm going to get the fillings done on the right side. I'm gonna get the fillings done on the left side. I'm gonna do the root canal once. I'm going to come back the second time. They have no problem.

And people go, okay.

You know, the ortho, the ortho guy will x-ray you once and then bring you back for something else.

So yeah, we can say, Steve, I was going to say, listen, I don't make the— I like to kid around. I think having your regular service department and scheduling is very, very important. What I was really leaning in towards was express maintenance, right? I think you should be able to drive in at any time, anywhere from open to close to dark 30, and the dealership should be prepared for you. To be able to get an express maintenance at any given time. You know, one of the conversations we had, I know when we met at NADA, is like when I was running a big store in Southern California, the one I was like, I'd like to talk about is we did have, you know, 8 groups, right? We had 8 lateral support groups. We had, you know, 50 technicians and we were going 20, you know, we were going 7 AM to 6 AM to midnight. And then we had an overnight shift and so on and so on. And I had a group of people, but we did this manually, like literally every group had their own appointment taker, and then we would have everybody spread out. We had these spreadsheets that were 8 miles long. And what I found fascinating about what you did was really you automated that or you turned that into the AI. So my— I used to call them a service administrative manager. I had SAMs. And so we had ASM, assistant manager, assistant service managers taking appointments, and we had SAMs— ASM are the advisors, and we had SAMs taking appointments. Right. And their job was to schedule and load you know, for their one little, you know, their one, one group. And what I thought was super cool was when you were talking about how you really, your software automates that, right? Or your software looks for those holes. Forgive me if I'm saying it incorrectly, but it really does helps plug those holes so that the department continues to stay full, you know, at the same time. So I think that's a great idea. I, I do love to be in the, uh, the, uh, protagonist sometimes, but I am a total fan of, of taking appointments. I just want to make sure that if you have an express department, you better be able to, you better, I better be able to walk in as well.

So anyway, now that I've explained myself, uh, you had to clear the air on that one. That's good.

I was going like, wow, I'll get my ass kicked.

Go ahead.

I'm sorry.

You know what? I'll, I'll layer on one last healthcare analogy. Cause I, this one I feel is really the most impactful thing as an outsider looking in. And I'll put the preference, uh, like I've worked in healthcare and I still have ties to it. So I'm speaking with like some knowledge here. So I think about that, not to pick on them, but I will, like primary care doctors. The way the economic model works is they got to turn you in 8 minutes. And when they went, walk into your door, they got to get out in 8 minutes. And so they're giving you the healthcare version of an oil change rotate, because that's all the time they got. Now they know they should give you the healthcare version of a coolant service or, you know, alignment. They just don't have the time for it. And one of the craziest stats I've ever heard, so I'll just throw it in here randomly, but they, they, uh, they monitored primary care doctors and they, they found that the average doctor interrupts their patients every 23 seconds because they don't have time. So if you start saying like, well, my Aunt Martha had this thing, they like, they yank you out and they're like, okay, what's your hypertension? Right? So, so they, you know, they have expertise, but they don't have the time. Then, then I think about like, you know, your teams, right, and service centers. And as an outsider, the word upsell was always weird to me. I was like, what is upsell about? Right? Because at the end of the day, you know, the team's expertise is recommending services and repairs to help you with safety or maintaining the lifetime operation of your vehicle or extending it, right? Or managing repair costs, like pay $200 now to avoid a $1,000 bill, like in 12 months, right? Those are all things in the customer's interest, right? Like in the same way, like, you know, I didn't think my doctor upsold me on a statin. He was just saying, Dave, your cholesterol is too high. If you want to live, you know, extend your life, you should probably get on a statin. I was like, okay, you know, thanks, doctor. That's good advice there. I know there's a lot of different reasons why services aren't pulled through in the drive, but I know one is for sure time, right? If the team is backed up, they don't have the time to do the full inspection or talk to the customer, right? I go back to Dave, your example of Philly Honda where like every RO was 0.9 because you were just jacked up, right? I'm sure there just wasn't time to talk to the customers. And so the economic model is such that like You guys, if you build in the time, you can do it. You're not primary care doctors who just give you the proverbial oil change, rotate, you know? So I really think about that a lot when I talk to like dealers we work with about time, you know, we're trying to create time for them.

That's cool.

So how do you do that? Like, how would you like, you know, I'm a, you know, whatever. Give me an example of a store that you're working with on how you were able to, you know, through appointments, really just be able to drive traffic because I know that's what everybody's looking for. We obviously have a capacity issue and we want to, you know, we sell time every day, right? We sell our time and we want to oversell our time or we want to, you know, be within, you know, say 10, 15% of what we have our capacity and we want to make sure we maximize that or we, you know, a technician's time if he doesn't sell it, it's lost, right? So I think that's what everybody's biggest fear is. If they don't book enough appointments, then they got all this leftover, right? If I book too many appointments and then the customer waits, right? So how do you go about How do you go about maximizing the, the technician time in the store? And I think that's probably what our audience cares about most is, is how do we make sure that our technician's time is, is sold?

Yeah.

And I, I think it, it's, um, going back to two pieces that both of you guys said, uh, it's like, first of all, Steve, like our goal is to try to automate the algorithm in the service manager's brain. So we're not coming in there and say like, hey, I know better. This is the way it should be. It's like, How do you want to run the shop? If you could schedule 100% of the appointments, you know, in all the different use case scenarios, what would you do? Right? And we're trying to extract that and then power it with software, math, you know, automation. And so that's like key. And then, you know, Dave, what you talked about were aspects of the variability, uncertainty. So we better, if there's a lot of walk-ins, we better bake that into like the plan, right? Like, we can't just say, like, we're going to schedule, you know, up to 100% and assume zero walk-ins, right? Or, um, as you said, upsell. Like, um, if we know that, like, the average Ford Works, you know, is 0.7 hours, but, you know, on average, you know, the ROs might be 1.3 hours or something, or it could vary by, you know, 10,000 miles it's like 0.7, right? 90,000 miles it's 1.6, right? So we should baking that into the scheduling in itself. And then, so that's part of our prediction that we're layering in there. And then the other thing that we're doing is also tuning it to the team's capability. So we're adjusting the clock time based on their efficiency. So if, you know, if it's 1.6, but like you got some, you know, really skilled guys, you know, you can beat the clock well, right? Or man, my express is just filled with a bunch of, you know, apprentices or guys straight out of school. So we better account for that, right? So all this is factoring it into sort of the core part of how do we bring the flow in. And then the other aspect that's really key is like how you flow it in based on the specialization of the job. It's like kind of like, well, in an emergency room, how do you treat a stroke patient versus like stitches, right? You're not using the same people to do that, right? And the level of resources and where you want to bring that in. So, you know, if there's certain recalls, like it doesn't make sense to bring in at certain peak times or you're on parts allocation or there are huge job and it would chew up, you know, a technician all day long. So you only want one of those a day or else you, the whole shop's, you know, jammed up with it. It's like that type of, um, you know, again, it's like what the service manager's vision of how they ideally would want to do it and all the nuances and the variability that you see every day and trying to execute against it. Hmm.

Well, yeah, go ahead. I was gonna say, I remember thinking about like back in the day, right? You know, we didn't want to bring in, we did tire recalls at night, right? We did the Firestone tire recall at night and I had a whole crew of people right? We would rotate our groups throughout the evenings. We'd have 2 or 3 groups working every night doing tires because we don't want to clog up our shop with that, right? And then I guess just recently everybody had the, the steering wheel— I mean, the, the airbag recall, right? And the Takata recall. So we didn't want to just clog up our shop with that, you know, just do, just do the airbag recall all day long. And so if I'm hearing what you're saying is your software can kind of, can take those different types of repairs and plot those out and help the dealer maximize efficiency that way. Is that, is that what I'm hearing?

Yeah, it's like an optimization for, you know, each type of requests or like specialized skill set, right? So the traditional way is like, think of a main shop express, right? But you could carve that up into any sort of way. And so like another example, just to carry on, is like there's, um, some OEM recall work, right? Like you, you pay, they get paid like 0.3 for it and there's no way humanly to do it under an hour, you know? So these are like nuisance one-line ROs. Well, what they, especially if they're showing up at, in the drive at 8:00 AM, but if they show up at 2 or 3 o'clock when there's off peak and everyone's got time, then that becomes an opportunity, right? Then you see you know, CP work that just starts getting added in. But it's all about like, you know, optimizing the business or the factory, but also the byproduct is a better customer experience too, right? Like, you know, if you're the customer with the nuisance RO, you feel it. Like everyone wants you out of there. And everything—

Really? You guys can tell?

Yeah. Yeah.

Is that like me driving my Wagoneer into the dealership and saying I have an electrical problem and they run up, they all run and hide? Is that what you're talking about?

Yeah.

Yeah, it's the exact same thing. Yes.

So I think that's what's been key for us is like, you know, the, the underlying, what I talked about is like the prediction on top of what do we think it is and then translating it, you know, to clock time-based efficiency. You know, that's the underlying framework, but carving it up by like, you know, request, specialized requests or where you want it in specific resources, that's where we get a lot of the, you know, the magnitude of the benefit from.

One of the things that, you know, discovering with AI, and I know, you know, it's a broad term and I know there's different types out there, but it seems to me that there's a lot of folks out there that just say, okay, I'm gonna get this AI technology, I'm just gonna plug it in and we're gonna start it and we're gonna forget it. And, you know, to me that's always just, it's ridiculous to think that way. I look at this AI like it's a, a child or a new employee and you got to teach it, train it, coach it.

Right.

And so I got to assume that that's kind of what you have to do when you're starting out in a store and you're getting, you know, like you said, getting the service manager's feel for how things work. And every brand's different, right?

Yeah. Yeah.

You know, you were talking about recalls before and, you know, there's, there's connecting rod recalls on, on, you know, Hondas and Acuras, you know, and it's like, you know, you got to drop the pan, you got to measure the bearings, right? And then you got to order the parts. Another car's dead on the lift. You're right. You don't want to do that at 8 o'clock in the morning. You want to do that at 3 o'clock in the afternoon, right?

Yeah.

So when the guy leaves, he's got a car the next morning, he could come and put the bearings in. So how do you, how do you work around that? Is that just all input and vary? How do you, how do the variables work with you?

Yeah. So first, before I address it, I would just like reiterate exactly how you think about AI. Exactly. I think of them as a digital employee. And so in that context, like, would you ever hire an employee and then say, I'll check back with you in 12 months to see how it's going, right? Like we do that every day.

Okay.

That's what we're doing here.

It's called the Earn as You Learn program.

That's the core business, man. That's how it works.

Well, that creates training opportunities then, right?

You are correct though.

Yeah.

No, it's true.

You gotta work with AI. You know, the AI is only gonna know like what you train it on, right? And so it's a feedback system. So that's just broader. Then the way our product is, I always think like, you know, we're 50% software, 50% people, meaning that we have to earn the trust of the, you know, service manager, fixed ops director, whoever that is, right? Like that we're their tool or enabler of their vision. And that's a people thing, right? And so part of that is we launch and configure and deploy, but then we're like the copilot or, you know, the caddy along the way. And so we're tuning and refining, but even as it's learning though, like your business is so dynamic. right? Like if 2 techs quit, whatever we thought it was, it's now different, right? Or I just got 3 people straight outta school. Well, that's different. Or we just got hit with a major recall. Now that's different. So even if we think we got it right, like, you know, we've gotta stay alongside and evolve as, you know, the operation evolves for every, you know, service manager.

You made a comment about prediction, right? And I, I, that kind of stuck in my head. I wrote it down is, uh, uh, Like when you say prediction, you're like, and I think I heard it correct. I think I heard you say, well, if the 2026 comes in, you know, with 10,000 miles on it, we're going to say X, Y, and Z amount of time. But if the 20, you know, or the 2017 comes in with 100,000 miles, we might allocate a different amount of time. Is that what you're saying?

Yeah. I also preface it of, uh, I'm 100% wrong on every prediction. Like we've never got one right. But it's, uh, because like, I don't know if you're going to decline all your services or I don't know if this engine's got corrosion in it. Like, how would, how would we ever know that? Right. Anyone who claimed that, like, I think they're just full of it. Right. But, um, the way I— it doesn't necessarily matter because we don't have bias in our miles. So if I'm like half hour too high on carb B and half hour too low on carb A, right, like it's still blending itself out and we're getting sort of the flow in, you know, through the day. Okay. But like, yeah, I hold no crystal ball to know that like, hey, Steve, your 63,000-mile vehicle is going to have a coolant service.

And David—

Right. But you're using an algorithm. I guess what you're saying is you're using an algorithm or you're trying to predict as best as you can. And you're saying, if I, again, if I understand correctly, because I'm not from— I haven't used the software, but you're saying that based upon X, Y, and Z, based on the history and based on the your particular store, we can predict X, right? Which is going to be a lot better than just saying 5/10, because that's what most people will do is they'll just block out a half hour for every appointment or an hour for every appointment. Say, that's it.

Right.

And we hope that we, we hope that we fill the shop up by scheduling 25 appointments today or 40 appointments or whatever.

Yeah.

Yeah.

And I think like the law of averages plays out well. Like I've heard you say this before, Dave, like, well, we got 100 hours. It's 2 hours in RO. And if you look out at 90 days or like a half a year, like that's probably spot on. But on any given day, right, that could just shift wildly based on like, we got 5 more DIAGs than what we thought. And all the models, Steve, just to, um, like elaborate on that, we're tuning it to each dealer's data. So it's not like some theoretical, like the factory says, like it should be this. We just look at the actual behavior of the dealership and that, and that could be like These advisors and technicians are amazing at inspections and working with customers to pull through services, or they're horrible at it, right? But like we build it on where they're at to say like we should plan for it, but then it's evolving. Like if, if it's a learning system where they're getting better at pulling through more flag time, then we would, you know, update the models accordingly.

Well, I think, you know, so you're taking, you're taking a baseline Or like you might, like the way I look at, like you said, 100 hours in a, in a 2 hours in a row. And I know I could do, and yeah, there was some days when you had too many cars with low mileage and you had other days when you struck out and you couldn't upsell anything. But you know, I've noticed the difference between a lot of my stores when I look at ASR percentages, you know, the amount of recommendations we make per car. Obviously it's driven by mileage in a lot of cases, not necessarily, but it could be brand too. And then you look at the closing ratio from the advisor. You know, and it varies. I have some stores where they can't crack 30% and they got other stores that are at 50, 55%, right?

Yeah.

So, you know, you obviously, when you look at that, again, AI computers and could be a lot better. When I dispatched, or I mean, when I made appointments for my own store and I looked at those things, you know, I could look at 'em, I could breeze through it. I could, I, I'd know kind of what day I was gonna have. But as the day went on, you know, I'm nowhere near as smart as AI or as smart as a computer is to figure this stuff out. And, you know, I think you're just taking it to the next level, which I think is great. Because I do think, you know, there's a lot of variables to what we do. And, and, you know, that computer being able to figure this stuff out with some, with some, you know, good data feed from that store is going to make— because you can't just make one policy that goes across, in my case, 34 stores. It just doesn't work.

Yeah, yeah, right.

And I mean, we didn't, we didn't bring Dave on to like as a sponsorship and say, hey, you got to buy Dave's stuff. We really were interested in— the conversation was fun the other day about about appointments or non-appointments and kind of like we were talking about. But I am curious. I mean, I know that when we talked, I love the idea and the concept or whatever you want to call it of what you're doing and how you are plugging those gaps, right? I mean, everybody is out there struggling because like you said, I can make an appointment every half hour and if somebody wasn't paying attention and all I did was make oil changes or all I made was this, I'm going to end up with half my shop filled or Or if I plugged in all of these appointments and they all turned out to be check engine lights, as Dave would say on a Cherokee, then I'm going to have, I'm going to be way overbooked and not be able to get my, be able to get anything done and end up with a bunch of unhappy customers. So I love the idea. I love the idea. I love the concept. What are, what are we're like some success stories or, or share with us some success stories that you guys have had, or, you know, I don't, you don't have to name a particular dealership, you know, you don't have to do that, but what are some things that you see and how were you able to help some stores? you know, with this tool?

Yeah, I, so I'll preface it as like, again, most of the great ideas I've heard, like I'm writing down notes, right? Like the service manager is telling me like, hey, this is something that I would like to do. Can you help enable it? And then we execute it. And then we go to the next dealership and I say, hey, I got a great idea. But you know, like I'm not like creating these great ideas. I'm just taking—

that's what I do. That's what I do.

And you know, what we've seen though is I'll tell you is like we, we've increased dealerships like, you know, revenue by 20, 30%. We've broke service gross records, monthly records in the first month, you know, then in the next month we broke that again. The third month we broke it again. So we've got, you know, many case studies where we're breaking like, you know, all-time high records for them on service gross. A lot of it at the end of the day, it comes down to like time pacing, but the vision of the, you know, what the service manager would be. So it's like, like I said, with the recalls, like these are nuisance recalls, one-line ROs, and now we push them to the afternoon and we're getting $150 of CP on top of them on top. You're right. Like, right. And then we got better flow in the morning. Or it's things about, you know, load balancing between if you got a big shop of tech teams. And so, you know, we're not doing maybe the real-time dispatching, but if we have Team A and Team B, we're making sure like, hey, they're showing up at the day at like equal balance, you know, like that. Dave, you don't have 10 diag and Steve, you got, you know, 10 brake fluid services. And then, you know, like, how are we going to make this equal? Right. So we're balancing it from that standpoint.

Right.

Yeah.

What was that video game back in the '80s we used to play like Tetris or something like that? Wasn't it?

You just trying to— I, I use that example. Like, honestly, like what we're trying to do is actually we look at the technician, you know, capacity when they roll on and off the clock. Right. And we're trying to pack it in like a perfect Tetris board. And if we get that right, then it's, you got time. We, we feel like we enable the operation to work with the customer to pull through all the recommended services. Right. Doesn't mean you're gonna execute, but we at least set it you know, possible for it.

Awesome. That's awesome.

Well, there's, there's, it's amazing how many times in, in, in the, you know, in a day you'd get to one of these days where everything went smooth. You made the most money you made, you made the most hours, you know, it was just one of those days where just everything clicked just perfect, you know? And you always go back and look like, how did we do that? Like what, what, you know, and, and it's, it's the work mix, it's the flow. But you know, the one thing I wrote down and circled is, is I assume the AI factors in like no-shows, right? No-show percentages. You know, I was looking at that the other day and I was talking to one of my service managers and like their percentage was like 12.5%. And I'm looking at it, I'm like, is that a good number or a bad number? Is that normal? Like, what do you, what do you think about, like, how do you figure, how do you factor that into your whole equation too with the no-shows?

Yeah. So, um, to me, like no-shows, walk-ins are like forecasts, right? That we have to look at. Like we, we can't ever predict that on any given day, but we can look at by day of week, by like service book or shop, right? And by time of day, like there's patterns, right? And then we're factoring those forecasts into, you know, planning. So, you know, like a Monday morning main shop, like, we're going to have a few tow-ins or whatever, right? Like it just— you can look at it. So that's, that's planned for it, right? We're going to have more walk-ins, you know, on Monday and Friday, less on Tuesday. And obviously they drop off, you know, throughout the day. So that's planned for us. So it's not like one size fits all. We're basing it on the data and we're monitoring and then, you know, adjusting, you know, accordingly with it. It's not, you know, I would say like we're not perfect. There's nothing like I'd say every calculation, there's an error into it. But like what we're trying to to do is be more precise than where you are today on like a law of averages. And, and that gets you, you know, closer to where you need to be, you know. And Dave, you brought up like, maybe I'll say this anecdotally, like our service managers, what they usually say is when you get, you know, attuned, right, is like, hey, the days are easier and I'm setting records. And I remember, you know, one service manager, like we had just launched with them I was just asking him like, how'd it feel today? You know, because I'm looking for that qualitative feedback. He's like, you know, it felt pretty easy and smooth. And, you know, if, you know, as early in launch, so like maybe if we got 300 hours today, like I'll take that as a win. You know, that's still like a decent day. So I was like, hey, check out how many hours did you get? And he's like, well, I haven't closed up everything, so let's take it with a grain of salt. And he's like, I'm like, whoa, we just did $430,000. He's like, it didn't feel like $430,000. We had only done $430,000 like once in the last 12 months. It didn't feel that way. So that's awesome.

You know, that would be a great place to kind of tie this all together because we are going a little long, but I love it, man. It's like so many people be like $300,000. I'll take $300,000 every day.

Right.

Right.

And, uh, that's, that's a, that's a pretty good size. Uh, that's, that's the stuff that warms my heart, man. I love these big stores, 3, 4, 500, 600 hours a day. You love that. You love that stuff. Kind of wrap this up, Dave. Tell us a little bit about you, about where people could find you, maybe find out about Evenflo. If somebody wanted to get ahold of you, tell them about, tell them where to find you. And then I'm going to turn it over to Dave Rogers to give us a GM tip of the day.

Okay, cool. Yeah, I think the easiest way to get ahold of us is just through the website, Evenflo, E-V-E-N-F-L-O. Evenflow.ai and just put a contact us message. You can email me, it's davea@evenflow.ai. People reach out all the time and I'd love to talk to you if you're interested.

Put it in the notes below too. So when it goes live, there'll be, we'll put the link into the, we'll put the link in the show notes for everybody. They can click on it from here too. That'd be awesome. Cool.

No, I appreciate that. Yeah, yeah, yeah, man.

It's great. It's great. We had fun talking to you. And I know when we met at the convention and And I just look back to my days running that big store and having every, you know, we had 8 people for, you know, for 8 teams trying to do everything, which it sounds like that's something that you can pretty much do, you know, with one digital employee. That would be freaking amazing, you know, with some coaching, right? And then you gotta keep that guy coaching. So I, I loved it, you know, uh, and I never seen the software, but the concept, what you're saying, it definitely warms my heart. And if a guy can go, uh, do a couple hundred hours more in a day, I mean, hey, it's worth giving it a shot. What do you think, Dave? You wanna wrap us up?

Yeah, no, I think it's a, I'm, I'm, I'm definitely just, my head's spinning cuz I'm like, all right, I gotta see how this thing works. No, I mean, you know, I do. GM tip of the day, you know, I, I, I try to relate things to sales for all the GMs out there. You know, you get 100 leads, you know, you're looking for how many appointments. It's a numbers game, right? That's kind of what they look at up in the internet with their, their CRM or just their cold calling and stuff. And I think you have to look at service the same way. You have, you know, 10 technicians. We know the numbers. We gotta figure out how we're gonna get more people in there and we can't leave it to chance. If you have a way, now they have AI emailing customers and trying to book appointments with internet leads and obviously phone calls. And I, and I think they have to look at it the same way. You're, you're trying to stack this, stack the shop, stack the sales department to maximize your success. And I think this is the, you know, you have to look at it that way. It's not about anything else other than just putting bodies in front of bodies. That's really what you have to do in the right way.

Yeah.

In the right way. Awesome. Well, Dave Anderson, thank you so much. Dave Anderson from evenflow.ai. Dave Rogers, uh, thank you for bringing your, uh, big guns with you today. And let's get out of here before everybody— it's— we're way over, man. It's like the longest episode we ever did. So hopefully everybody out there, everybody out there got a little bit of a little extra today from, uh, from Dave Anderson. And, uh, let's keep in touch. Let's let it— we'll keep us, keep us in touch. Let's see how, uh, how people are doing with, uh, with your software. So Appreciate it, Dave and Dave.

Yeah. Thanks guys.

Thanks. Have a good one, man.

Yep. You too.

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