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The #1 Reason Dealers Lose Customers (It's Not What You Think) · Auto Collabs

• 0:00
  Price is not the number one pattern or insight as a theme that's showing up on defection. Overall, it's third on the list, actually.
• 0:08
  If you look at the data that we're getting about the defections and why they're happening, we see price may be mentioned, but if you look at the detail, there's a lot of communication that's breaking down.
• 0:21
  [upbeat music] This is Auto Collabs. There are certain things you lean on your friends for, and today's a pretty unique one because, uh, our compatriot, Michael Cirillo,
• 0:39
  caught the black plague [laughs] over the weekend. [laughs] And every day texts us no le- no less than 17 apologies on why he wishes he could be in the saddle but can't get out of bed.
• 0:51
  And today he showed up for this, and I said, "Michael, Kyle and I can handle this." We got this. We'll be fine. And you said, and you said what? I said, "I gotta do this." [laughs] I just have to do this.
• 1:01
  I just have to be active. I have to be- At first I wasn't-... in front of a mic. I get it. At first I wasn't sure where you were going, 'cause you brought up me and crutch in the same sentence, and, like- [laughs]...
• 1:10
  height, relativity. I, I'm like, could literally s- sit under your arm as a crutch. [laughs] That's unbelievable. I feel like I'm, I'm in a different boat.
• 1:21
  My, my wife and I were talking, you know, I was at, uh, at, at time of recording, I was at, uh, the Pash Group's DMSC earlier this week, and I'm, I'm a pretty good...
• 1:30
  Like, I wear my, my Apple Watch to sleep, and I'm a pretty... I'm lock tight for, like, 96, 97%. Two days in a row under 50, y'all. [laughs] It's like, just got crushed. Here's what I decided.
• 1:41
  I, I did the, I did that whole like, "Hey, let me track," you know. Y- hear all these things, like, track everything. No, relax. You don't have to track everything. It'll drive you crazy.
• 1:48
  So I did the WHOOP bracelet, W-H-O-O-P. I did that for, like, a year and a half. Yeah. And every morning, it'd be like, "How did I sleep?"
• 1:55
  And then I realized how the watch told me or how the device told me I was doing- Impacted your whole day... really did impact how I felt.
• 2:02
  And so I stopped wearing it, and I was like, "I don't know how I did last night, but I feel fine," and I realized I felt better not knowing. Dang it. Doesn't that make sense, though? It does make sense.
• 2:12
  It does make sense. Th- there is- [laughs]... there is some data worth knowing, and I think that's what we're gonna be talking about today, right? That's it. Oh, he's back- Got him... with the segue. Look at that segue.
• 2:24
  No- MC can retire now. Look at that. Wow. He hit a segue. He's like, he's like, "Okay, now I don't have to finish. I, I've proved to myself that I still got it."
• 2:30
  We're, uh, with Tom Cotterell, Advanced, uh, Analytics Director with Urban Science, talking about something that is near and dear to our hearts, why customers don't come back. What, what is the- Ooh...
• 2:42
  defection information? What is the data around that, and what can we do as an industry to improve that? So we hope you enjoy this conversation with us and Tom. All right, Tom.
• 2:55
  Thanks for joining us here today on Auto Collabs. Excited to have you. Thanks, Kyle. Glad to be here. Good. Good. All right, so I gotta dig in, because we, we do these...
• 3:05
  You know, we like to get to know you a little bit. We do our own research. We've never met before, but the first question I have is this: You said that early on in your career you were working with Chrysler- Mm-hmm...
• 3:20
  and that at that point, you were already working with AI. Did I read that correctly? Well, not the kind of AI that the popular term has come to mean. Uh- Okay...
• 3:33
  back in the day, AI was machine learning, predictive analytics, um, neural nets. You know, that was, that was commonly what was referred to as AI.
• 3:45
  Now we have these interactive chats that have agents and kind of simulate a whole bunch of human reasoning, and, uh, that's not exactly what I was doing 20 years ago, but- Yeah, but- But the principle...
• 3:57
  but it lay, it laid the foundation. I'd love to... I, I, I wanna dig into, like... 'Cause that's foundational to, uh, especially even just maybe the way that you think.
• 4:06
  Can you just double-click on, like, what was the, some of the foundations that you were helping lay and a part of laying at a large organization that maybe you're starting to see, like, okay, I see how that, you know, teams that were laying these foundations were preparing the way for maybe what we're seeing now?
• 4:27
  Yeah.
• 4:27
  Um, even, you know, 20 years ago, marketing-related AI was key, so optimizing who you're communicating to, the nature of that communication, the offers that you're giving, uh, you know, predicting who's going to buy what, w- all of that.
• 4:43
  So that was key, you know, back then, and it's evolved a lot since then.
• 4:48
  And it goes from everything from the marketing side to the after sales and warranty, where, you know, Chrysler, as all manufacturers are, what is the time of life of, of their vehicles? How long will they last?
• 5:00
  What components will fail? So it was, it was very much predictive in nature. What's going to fail? How do you react? Uh, what's the cost [laughs] going to be? Those kinds of questions. Hmm. Yeah, I think...
• 5:16
  W- actually, you know what's so interesting? So much of AI today is, is much more centered around what do I do right now? Mm. How do I interact with the moment, right? Mm-hmm. Mm-hmm. Mm-hmm.
• 5:30
  And again, yeah, uh, basically what, what kind of machine learning was, and there were decision trees and things like that, but what you're saying is, like, actually it was, it was much more used to go, "Okay, I've got this rear-facing view.
• 5:43
  I have this context. How do I project into the future- Mm... what m- may or may not be needed?" And I think m- maybe we've gotten, because of our obsession with, like, I want it now- Mm-hmm...
• 5:55
  everything's like, "Build me this now. Answer me this question now. Change this version of what we're doing now."
• 6:02
  And I almost wonder if there's something that we need to get back to, which is-How do we use some of the, the newer models to not just go, "What do I react, what do I do now?"
• 6:14
  But, what, what might I need to be looking at? What might I need to be looking forward in? And, and, and it's just questions that I personally have not asked about it, so, uh- That's pretty profound-... interesting...
• 6:26
  because as you mention it, I'm reflecting. Back then, I was concerned about very scalable problems.
• 6:33
  Thousands or millions of vehicles, millions of customers, and you framed it up to say now AI is all about me individually from a personal level.
• 6:44
  And obviously the kinds of things with generative AI and LLMs, it's very personal for lots of people. Mm.
• 6:52
  And so, yeah, I, I hadn't quite thought about it like that until you mentioned it, but, uh, I, I like that and I agree with that. That's been the transition is now these AI technologies, and new ones,
• 7:04
  affect the individual in ways that 20 years ago we weren't even thinking of. Hmm. Well, if you think about the, the difference in the personal world that we live in now versus what it was 20 years ago or more,
• 7:19
  we aren't even close back then to the level of personalization that we have now. Mm-hmm. Like, the...
• 7:25
  It was impossible to think that way because there was actually no channel to deploy anything in personalization that way. Like, n- very, very, very little.
• 7:34
  And so I guess it makes sense that the, the more personal of a decision you can solve, it's like you start there, and then you back up from there, and the big results take care of themselves.
• 7:45
  You almost can't solve it the other way around now because everyone is so personalized in their interaction, in their decision-making process. Which really does, um, kind of bring us to
• 7:55
  a topic that you talk about so much, and Urban Science does so much to help, and that's, like, this idea of, like, defection data. Mm-hmm. And, I mean, what's more personal than that? Like, why didn't you do that?
• 8:05
  What did you think about that one thing? Um,
• 8:09
  so I, I guess it makes sense, like, we're, as we're, like, framing this big conversation up, as we go down the line, it makes sense that defection data, it makes sense that hyper-personal use of AI to try to encourage or figure that out makes so much sense.
• 8:24
  So asking why a customer leaves definitely gets us where we wanna go in the macro. So, um- Mm-hmm... you know, dealers, I think, always... Well, well, let me ask you this question.
• 8:36
  Why do you think dealers would say they lose the majority of their, their potential customers? Oh, I, I think, uh, that's a good question. What do you think they think? Yes. I think, uh, price.
• 8:48
  I think price would be probably very close to the top of the list. Um- Sure... so I- Would you say... Kyle, do you think that's true? Would you say price?
• 8:58
  Oh, I think dealers would say most of the time, like- That's what I mean. Yeah... every dealer that I talk to is like, "Well, my... The person down the street is advertising it at..." You know, that's like the...
• 9:07
  That's a very quick gut level reaction, right? schedule. Yeah. Yeah.
• 9:12
  Okay, so- But, but we find that that's not actually the case, and that's really, really fascinating as we're drilling into this defection data, that, uh, price is not the number one pattern or insight, uh, as a theme that's showing up on defection.
• 9:28
  So that's one of the things I'm really excited to talk about. What, what is- Well- W- w- what are some of the top things, then? Well, so price isn't- Well-... number one. Do you know where it is on the list?
• 9:38
  Uh, well, overall, it's third on the list, actually. Okay. Third? I thought it would be number one. Third, yeah.
• 9:44
  So actually, the, the number one, and this is a very human challenge, not only we talk about [laughs] personalization of AI, but th- there's nothing more personal than this, and that's communication, right?
• 9:57
  That is, yeah. So if you, if you look at the data that we're getting a- about the, the defections and why they're happening,
• 10:05
  we see price may be mentioned, but if you look at the detail, uh, there's a lot of communication that's breaking down. Hmm. They're not responding. They're not addressing my question.
• 10:18
  They're not re- addressing it fast enough. They're over-communicating and not saying anything.
• 10:22
  I mean, all, all of these are, are, in a nutshell, uh, communication issues, that as people, we can, we can address those, right? Price is sometimes- [laughs]...
• 10:32
  harder to directly, uh, address because profit and cost and all of that usually are financially baked in, but, but communication everybody can strive to do better on. Okay. I didn't think I w-...
• 10:45
  Like, this, watch this, this segue. I didn't even think we were going here, but I think this is, for you especially, because of what I now know about you as a musician and as a space nerd.
• 10:58
  We were talking a little bit about before. That's right. Oh, I missed the space nerd part- Watch, watch... of the conversation. This is, this is...
• 11:05
  I think people need ways to logically connect, 'cause we can all say communication. We gotta get better at communication, right?
• 11:13
  And I'm gonna give, I'm gonna give, like, two starter things here, and then I want you to, like, think through and extrapolate them for us- Am I gonna need a flow chart for this?...
• 11:22
  and then maybe connect them back to how we can come back to, to retail auto. All right. So, uh, I don't know if you've ever played in a band, uh, with a musical director. Have you, Tom? Yes. An MD? Uh- All right...
• 11:35
  high school, I was part of the orchestra. Okay. So you got an MD, whether that be a, a, you know, a, a band that's playing like we all know, or you've got a musical director in an, in an orchestra.
• 11:46
  And, and, and there's something interesting about the way an MD communicates with the entirety of the band, right?
• 11:56
  The type of communication that has to go to the drummer verse the entire d- verse the entire band verse the, the, the guitarist.
• 12:05
  Like, how the MD codifies their communication in real time-To make sure that that person knows exactly what is up and what's to happen next, without over-communicating, to allow the freedom to move within it.
• 12:20
  Go to, go to space. I'm watching them take off last... Did you watch them take off? I did. Ugh. First of all, amazing.
• 12:28
  But you go to that and you think about the words, the phrases, the timeliness, the exactness of when it's expected for a call and response to happen.
• 12:38
  These are both places where you've, y- you kinda have some, like, unique experience that you, you tap into a lot, that the type, style, timeliness of communication is, is, uh, like, either life or death or bad sound or good sound, right?
• 12:57
  Mm. And I think if we could tie, like, these uniquely human things, these things that we see in the rest of the world to, okay, what does that mean to communicate better- [laughs]...
• 13:09
  in a dealership, which is, like, a very broad saying- [laughs]... but we could bring that to some real stuff, right? Yeah. Um, so
• 13:18
  at the end of the day, there's a, there's a person communicating, and then there's the audience receiving that communication.
• 13:25
  And, um, while I'm not a, a, uh, psychologist or [laughs] whatever, social sciences would probably be best to analyze this, um, it's, at the end of the day, I think you have to want to communicate, you have to know your audience, and you have to speak in a way that's relevant to them, addressing what they want to hear or what they need to hear, you know?
• 13:48
  That you have to make that connection. And, you know, when you, when you read the survey data, you know, yeah, you're getting one perspective of it. You are getting, you know, just the customer's perspective.
• 13:59
  But you, you generally do see that there can be problems cropping up where, um, just w- just the, the want to, I guess you could say.
• 14:10
  Like, d- is, is this, is the person at the dealership, are they always wanting to be communi- communicate in a way that's relevant, that's timely, that's impactful?
• 14:22
  And I, I don't think I've got a silver bullet for anybody that's might be watching this- [laughs]... there's probably people that could, uh, elaborate better. And I'm a data guy. I'm an AI, uh, machine learning dude.
• 14:32
  I'm, I'm not a, uh, communications expert per se, but, you know, I think the one thing that we're trying to do is we're trying to highlight when there are problems. Yeah. You know?
• 14:41
  And just kind of, you know, knowing, [laughs] knowing is half the battle, as they say, right? Uh, to use an old [laughs] cliche, but- GI Joe said that, yeah. [laughs] GI Joe.
• 14:50
  I'm also dating mys- twice, I've dated you, Kenzo. Yeah. I, I watched those. I, [laughs] I watched those two. [laughs] So it's our generation.
• 14:56
  Uh, but I think, I think we're, we're identifying when those are happening, and we're quantifying, uh, how frequently and the scope and, and all of that. So I think that's the first step.
• 15:06
  And I think, Kyle, getting into even more specific things will be coming down the road from us as Urban Science, but we're probably not, uh, completely there quite yet, but we're getting there.
• 15:17
  All right, I have two, two questions. Number one, uh, first, what was number two? Communication- Oh... number one. Well, number two, we- we've categorized it generally as, as staff, okay? Okay.
• 15:28
  So there's basically kind of a staff, and it c- this can be positive or negative. And, and I don't wanna just be negative here, 'cause I know you guys are all about the positive side, too, right?
• 15:37
  There's two sides to every- We like to tell the truth, though. We gotta say the hard things. Yeah, you gotta say the hard things. But for sure.
• 15:43
  But, so staff shows up, and so that's generally, uh, you know, salesperson or the finance manager or something like that is, is that.
• 15:52
  And then that can also be sometimes, uh, a specific amazing person, you know, that they really like, too. Mm-hmm. So it's not necessarily all bad.
• 16:00
  But it, but kind of a staff issue with someone specifically on the staff, either positive or negative, is kind of the second most common theme, followed by price is number three. Okay. You kn- you know what's funny?
• 16:11
  This makes sense. Uh, d- Paul, do y- uh, before, you have a second question. Do you know- I do... what the th- the three Ms of conflict in a sale are? The reason why they didn't buy?
• 16:24
  Like, back, back in old training days, do you g- uh, w- well- Old school? Ra- Tom, you, yeah, you d- you never sold. Okay. I'm old.
• 16:30
  So there's always, we would call it the man, the machine, or the money, or the person, product, or price, right? Mm. And then a lot of trainers, myself included, added time, right, as the- Mm-hmm...
• 16:42
  as the fourth conflict. And we would kind of bucket under the man or the person, uh, you know, communication, but I think we need to, we need to figure out, like, the four Cs. I don't know, you know. [laughs] Right.
• 16:56
  It's like... [laughs] It's too broad of a swatch. Uh- It's like, that, that's like going to marriage counseling. "What's the problem?" They're both gonna be like, "Communication," right? [laughs] It's like- Every time.
• 17:04
  Yeah... "Oh, good. We figured out the problem. Well, again, pay me my, you know, 100 bucks and go on your way and have a happy marriage," right?
• 17:10
  [laughs] You know, that- But what, but what it's telling me is, like, nothing's really changed. You know? Like, we still- No... can't figure out the three- Listen, people-... big things...
• 17:17
  people don't change, first of all. Right.
• 17:20
  It's gonna be the same problem, solving it with a different, with a different, um, approach, I guess, which leads me back to my second question, which, can you tell me a little bit about the, the surveys, how they're h- how they're conducted, how you get this information from people?
• 17:34
  Because I'm curious- That's a great-... 'cause-... great question... some people do them. I haven't done one, but go for it. Great question.
• 17:40
  So, so, uh, at Urban Science, we, uh, have access to daily sales information, and through the collaboration, uh, with our, the OEMs, um, the manufacturers, um, we can identify when a shopper, uh, in s- a lead in a CRM, uh, comes into market and buys, buys a vehicle.
• 18:03
  So we can flag that event and send that within 24 or 48 hours back to a, a dealership-And that can provide all kinds of beneficial information.
• 18:15
  It can make them more efficient about not wasting time chasing down sold leads.
• 18:19
  But I think more importantly, fundamentally to improving the whole experience, is now you've got a, a, a, a self, um, you know, a, a self-healing or, or a feedback loop, I should say, on, you know, w- training.
• 18:35
  How, how could I improve that? What, what went wrong? You know, how can I learn from that as a salesperson or as a sales manager?
• 18:42
  And I think that can promote a whole f- a set of faster training and education, and improve the whole process by this very fast feedback loop.
• 18:52
  Um, and so that's, that's in, in essence what the defection analysis is that we have at Urban Science.
• 18:57
  And now the survey going out, when, when dealers, you know, turn this on, when we work with dealers to turn this on, then that customer can receive a survey, uh, asking for more information about the whole process, and we ask about, uh, 10 questions in that process.
• 19:14
  Well, and, and how many- Is that written-... salespeople are out there just spinning their wheels on half their leads, right?
• 19:18
  That have already bought somewhere, and they're, they're getting more frustrated, which means they're less effective with the people that are actually responding to them. Right. Less likely. Yes. That's a great point.
• 19:27
  Yes. Yeah. Yes. No, uh, the, um, through the Harris Poll, which is another survey company, it's, uh, different than the defection survey, but they're a professional survey company.
• 19:36
  We see overwhelming feedback about dealers want to know more about w- why are customers defecting, uh, when, you know, when have they defected.
• 19:44
  Um, and we're talking 70, 80% of dealers, uh, want more information about the whole defection. And so we think this is, uh, just very timely and very useful for the whole, uh, auto industry. Well, there's a few things.
• 19:58
  Uh, if I remember correctly, are you gonna d- debut some of this info at ASOTU Con this year? Yes. We're, we're going to release the results of the Harris Poll for the first time.
• 20:07
  That will get presented at, uh, ASOTU Con.
• 20:11
  And then this, the, the, the insights that I'm sharing with you here, I will make that a full part of the presentation, and I will go into a lot of detail about all the patterns and insights that we're finding. So, um-
• 20:25
  That's great. And again- We, we love stuff like that... these are things that any, any dealer, any dealership can just, like, jump right on. That's, that's what is so- Well- It doesn't matter...
• 20:35
  here's how it- This is universal- But it-... you know? It all connects now. Like, back to the beginning of the conversation, how do we leverage AI to get more personal and help people with personal decisions?
• 20:45
  Back before we had these AI tools, so just go 24 months ago when they weren't common use, they weren't readily available, you had to, like, take a report like this, you had to analyze data, you had to try to, like, smoosh it all into your brain and then get some kind of output that would actually help you change something.
• 21:01
  But now, with the ability to analyze and implement, you could really quickly digest this information into an action plan where you could make some measurable change relatively quickly. There you go.
• 21:12
  So I think even the availability to act on these reports, like, they've been available, and you all have been doing this work.
• 21:18
  But now the ability to just, like, take this information and implement some action plan, even to make progress on one or two areas, is, like, right at our fingertips. Yes.
• 21:27
  And, uh, you know, on the, on the, one of the good uses of AI, one of the ones I, I really like talking about, is we're using AI sentiment analysis to pore through all those surveys.
• 21:37
  Like, AI's really good at language decomposition and extracting sentiment out of texts and language- Mm... type data. It's really good at that.
• 21:45
  Yeah, you used to have to be, like, an FBI negotiator to figure that out, right?
• 21:48
  [laughs] So, so we, we can c- we can process a lot of that and actually make recommendations even at the individual level, like we were talking about earlier.
• 21:58
  Um, and so that's r- really a fun use of AI that we're, we're putting in, uh, to do that kind of analysis to, to analyze the single cases and also in the aggregate. All right, so we're gonna end here.
• 22:10
  If there's one thing dealers should do or you've seen the successful deal- dealers do with some of this data, what is that one thing they do with it?
• 22:19
  Well, one of the r- really, um, cool things that I, I'll be talking about at the conference is that one fascinating finding, and I didn't expect this going in, I kinda stumbled on this, was that when somebody's name is mentioned in a survey, so, you know, Kyle did this, or Paul did this, or Tom did this,
• 22:41
  it's overwhelmingly positive sentiment. Yo. Oh. Wow. It, the, and it's really, it's, and we're talking the, the sentiment is, is about 77% of the time positive sentiment.
• 22:55
  So they mentioned Kyle, but they still didn't buy the car. They still didn't, they still didn't buy the car, but, but, uh, four out of five times- But they remembered someone... they r- "Boy, so and so was great.
• 23:05
  I bought, I bought something closer to home. Wasn't, you know, didn't have all the features that I wanted." I mean, maybe they went with a different brand altogether. [laughs] Right. But they made a connection.
• 23:16
  They had a good experience. Um, and they'll even, uh, about 60% of the time they say they'll recommend that dealer to their friends or family.
• 23:28
  And so we think a very actionable thing is for the dealership to, you know, um, reach back out to that person, say, "Sorry," you know, uh, "but would you consider us for service?
• 23:41
  Um, if you have another vehicle that you'll be interested in, in buying in the future, I hope you come back and talk to me," right? 'Cause- There you go...
• 23:47
  the, the, the average household has 2.4 cars, like they have, you know, 2.4 kids or whatever. Yep. [laughs] So- Right. [laughs]... so there's m- multiple cars in the garage.
• 23:57
  And, um, you know, that, that is really, I think it's really impactful. It's really a, a story about the, the power of people to make an impression, in that nobody bats 1000, right? I think, uh, you know- Yeah...
• 24:11
  Michael Jordan missed half of the shots he ever took. Right. [laughs] You know? Yeah. Uh, Babe Ruth I think was, like, hit, like, 340 or something his career, right? [laughs] I mean- [laughs] Yes...
• 24:21
  nobody bats 1000, right? A lot of strikeouts. Yeah. [laughs] Lots of strikeouts.But, uh, you know, it, it, this can be a feedback loop to say, "You're going in the right direction.
• 24:30
  You're, you're having an impact," and that's better than, you know, "So-and-so didn't listen to me." [laughs] Right? Man. Yeah.
• 24:37
  Tom, I, I'm s- I, we love this conversation 'cause it always boils back down to the human, and what is the person thinking, how can we serve the human better. Uh, thank you so much for sharing this with us.
• 24:47
  We can't wait to see the rest of this data because w- when it comes to stuff like this, we're kinda geeks about it. So thank you again.
• 24:53
  Thank you for all the hard work you're doing with Urban Science to help dealers, like, understand what's going on, move to serve customers better, and, uh, thanks for joining us here today. Thanks, Paul. Thanks, Kyle.
• 25:04
  [upbeat music] Okay, legit, Year of the Human is undefeated at this point. Yes, it is. Yes, it is.
• 25:12
  The, the one stat that rings in my head is that when someone mentions someone at the store by name, the overwhelming result is positive feedback. Mentions. Right. Yeah.
• 25:26
  Even the peop- [laughs] How do you, how do you figure out a way to make sure that your people are known by your customers?
• 25:34
  I can only imagine, like, Russ Flipswips, when people finished doing business with him, they knew his name. "Yeah, I did, I bought a car from Russ." Right? And- Even, even before he was, like, the internet sensation...
• 25:46
  abso- And that's on the lead-up, that's on what happens in the showroom, that's on the post-sales engagement.
• 25:52
  And I think we have to figure out an ecosystem where people remember the names of the people that they do business with.
• 26:00
  And so how you set the appointment, how you engage them before, what that engagement, how, how often you say their name in the showroom.
• 26:08
  Uh, all of those things will translate to a positive customer experience by default, whether you mess up little things along the way or not. [imitates explosion] Hit it. Oh, my goodness.
• 26:18
  [laughs] Okay, but, like, think of the real implication from online to offline, right? 'Cause, like, th- this is all about offering a better experience, a more personalized experience, and the gravity of that.
• 26:31
  You look at, like, how many businesses, dealers in particular, kinda force people, strong-arm people into leaving reviews while in the store, but we actually know that when a review is left from some other IP address outside of the store, it actually carries bigger gravity for, like- Yep...
• 26:47
  a Google business profile and stuff. And this is, like, a direct tie-in to that because, like, quantity of interaction may go down, but, like, quality of interactions go up- Yes... which, which, like, tips the scales.
• 27:00
  And it's just something that is not as easily replicated, which therein lies the, the competitive advantage. Oh, my gosh.
• 27:08
  So wh- what we're saying is the quality of the humans and the quality of the human cultivation is actually the underlying driver for all the tech and data you could ever want or have.
• 27:16
  I don't think we need to talk about anybody else. As always, thank you so much for joining us here on Auto Collabs. On behalf of Michael, who is back, Kyle, and myself, thank you.
• 27:29
  [upbeat music] Welcome to Auto Collabs. [laughs] Welcome to Auto Collabs. [laughs] Are we recording yet? Are we rolling yet? Not yet.