Taking Control: Why Dealerships Must Own Their Data Now

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Welcome back to the Core Conversations podcast, where we're always tackling these big shifts in automotive retail, specifically around data and AI, how technology is transforming the industry, and today we're jumping in deep into one of the most urgent topics for dealers, and that's data ownership.
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I'm always joined by Mr Todd Smith, ceo founder at Core AI.
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He breaks this stuff down better than anybody in the industry, and he recently published a really awesome, thought-provoking piece on LinkedIn.
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If you haven't checked it out, you need to go.
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It's called the Death of Middleware, why Automotive Groups Need to Own their Own Data, not Rent it, and it's a wake-up call for the industry.
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I got to tell you so.
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Right now, there's way too many dealerships that are renting their own data.
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Imagine that, just conceptually, you're renting your own data from vendors.
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You're paying for access to insights that should be yours by default the rise of what's happening and how fast AI is moving.
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Well, this means that the old way of how you've been managing this stuff through middleware and third party control it doesn't work anymore and it shouldn't be that way.
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So if you don't own your own data, well, you don't control your future, and there's probably never been a more important time for you to do that, so we're going to explore what's changing, why it matters.
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The companion to this again is this LinkedIn article, so you're going to want to go get into that as we get into this episode.
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Todd, first number one, I already know you're doing fantastic, so I'm going to get you right into this, your article.
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You talk about the death of middleware.
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I want you for the audience, if you don't mind, clarify what middleware is for the dealership world, why it's becoming obsolete.
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Yeah, look, middleware is exactly that.
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It's a system that lives between systems, right, and a lot of times we're, you know, I will say, like, let's look at something like a CDP.
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That, to me, is basically a middleware.
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So I'm taking my data out of my DMS, I'm now plugging it, I'm moving some of my customer data into another system, I'm cleaning that up or sort of cleaning whatever.
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Now I'm attaching that to marketing.
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So you are continuing the fractionation of your data probably not a word, but I'm going to go with it.
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And the reality is, you don't want these middle systems living between your nucleus, your dealership data and the tools you're trying to do.
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And this is why, you know, we have this world where we have all this disconnected data in automotive and all of them are chewing up your data and you are having to pay to get access to that, and to me, it doesn't make a lot of sense.
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So I see a different world emerging, mostly driven by AI, but, more importantly, driven by the fact that data in general, you know, doesn't have a lot of value, but your dealerships proprietary data is incredibly valuable.
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So why?
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Why would you just want to give that up to every vendor that you want to do business with.
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It doesn't make a lot of sense.
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Giving them access to your data is one thing, but you want to control that, so I had written something else also in this I said the value of knowledge is going to go to zero because knowledge is going to be so commonly available for all of us.
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Like, think about this.
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When I was a kid dude, we went to the library and you had to know how to use the Dewey Decimal System.
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If I asked my kid what the Dewey Decimal System is.
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He would be like what are you talking about, dad?
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I just Google it.
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Though I will say this you'll appreciate this, sean.
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As a parent who has a 15 year old, I told my son, uh, he was doing research, uh, around uh, the holocaust and he had to have a first party perspective from a woman and a child who had lived through it.
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So he's trying to do the research in the school's data tool and he's he was there for like an hour and I'm watching them and I'm like do I help them?
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Do I not help them?
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And I'm like what are you doing?
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So he tells me hey, I need to have these first party accounts.
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So I was like huh, but he's like it has to be cited, so it's very important.
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You know that.
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It's like like real research.
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I was like, okay, so I open up her, I walk with him to have lunch.
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I open up perplexity and I'm like what are we trying to do?
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I was like type in what you're doing.
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It literally brings back everything in 15 seconds, cited, cited, everything.
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He's like.
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I was like, finally, I was like the Dewey Decimal System, like old man, has scored the most modern, using perplexity to gain an access.
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So I feel the knowledge curve, though, goes to zero, but I feel the value of proprietary knowledge or experience is going to be at an extreme premium.
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So, when you think about that, all the general knowledge around auto not easy to get your hands on not a lot of value.
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The true value will be your dealership or group's organizational knowledge.
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Bringing that all in together into a system that is accessible to all employees and becomes your AI brain and this is that point where I feel like you don't want a middleman in there Like this is the heart and soul of the future of your business, and the only way you're going to get there is by controlling your data, and not only just owning your data, then owning, ultimately, the connections that your data has to everything.
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Yeah, I think that's really important.
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I love that you highlight the fact that there's nuance.
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That's really really important from the dealer's data side, and the example you gave with your son also is really brilliant.
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I have just recently had a conversation with a company that has brought in somebody to consult something for them that's really, really important and they could get foundationally in 15 seconds, a whole kind of lay of the land of like this is the automotive industry, because they don't know anything about it.
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But what they can't get is the other pieces of context from that nuance that comes specifically from this client that they're.
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It's almost like paying to educate somebody.
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But all of these things I think are really important for dealers to think about how important their side is.
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And you highlight the fact that moving slowly and when dealers are paying for people to be in the middle like this, it oftentimes is accessing their own data in the first place.
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So that ownership piece, which you're a big champion of, I think is also one of the things that can't be emphasized enough.
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And there's also risks when you're renting and you mentioned this in your article where the risk between renting versus owning can get really consequential for dealers, and you've been vocal about those changes.
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Why, in your opinion, for at least for the podcast episode?
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Why do you believe that's such a big risk?
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Well, look, it's almost like it would be like a Formula One team You're the team, you're the driver, but you don't own your car Like, so you rent the car Like would that make sense.
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No, I wouldn't see a world where you rent the car Like you want to own the vehicle because that vehicle, there's tons of proprietary software in it, there's tons of information, and so dealership, to me, is the same thing.
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The dealership has a enormous amount of intellectual capital and assets that are vastly underutilized, and that, to me, is number one when you think about it.
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And look I'll tell you what drove me to this, which was actually really uh scary research.
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So we spend on average in any business so car dealer doesn't matter, it's going to be the same 13% of our time looking for information.
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Okay, so 13% of your time at work.
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You know what that equals?
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33 days a year, wait, gets worse.
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Wow, we spend 37% of our time in meetings.
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Okay, so that's 50% of our time is spent on non-revenue activities.
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The world we're living in, and to me by, again, if I can get all that knowledge in and now I can turn it can make it easily accessible organizationally, you change the game and you're not spending 13% of your time looking for stuff.
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You don't need the meetings.
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You just change your organizational trajectory by kind of rethinking how you're doing.
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And this is where it gets hard.
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Right, because, again, we're using frameworks that are very outdated.
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Today We've evolved past our current frames and we're still using them.
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Like, oh, gotta have a meeting about deciding what SaaS vendors we're gonna use and what marketing we're going to do this month.
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Right?
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How many dealerships right now it's 20th are having emergency meetings because their numbers are off.
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So what can we adjust in our marketing?
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Right now?
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We need to crush.
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Our RO count is down for the month.
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Our used car sales are bad and we're, we're we have too much overage inventory, like they're having these, I'm telling you, in stores all across the nation right now, as we're doing this podcast, at 1239 in the afternoon, I guarantee there are tons of meetings going on and to me, that only thing I hear is you are not data driven, because you are just.
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You know, you're making these interesting kind of like, and that's the whole point.
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I was like God, how do we get rid of that?
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How do we re-inspire, building something that look, and with AI, ai can analyze all your ads, can analyze all your competitors' ads.
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You can upload into it all your brand guidelines, governance right.
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What has worked, what hasn't worked, and now say, hey, we're off.
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What should we be doing?
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Like, you should be able to get answers quickly.
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Insights right.
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That's the power of this.
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How do you bring all your data together that you own and now start to extrapolate insights against that data?
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That's the world.
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And if you're a dealer and you're not any business, you're not focused on that.
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If a dealer and you're not any business, you're not focused on that.
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You are using the wrong framework in how you're running the business and, look, this is the hard part.
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You have to crash.
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I call it like crashing the sacred cows of hey, that's how we've done business.
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This is no.
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This is works and you're like I understand it works, but is it optimized?
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That's a great question.
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It's a really good question.
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It's a really hard question to ask and answer it is, and when you.
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I love that you use the examples well, not even examples when you give the facts of how much time is spent researching, how much time we spend in meetings.
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As depressing as that data is, it is exacerbated for dealers that are renting their data versus owning their data.
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Right the conversations they should be having from owning their data Listen when you rent your data, this is what happens.
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Someone comes in, extracts data from you okay, you've signed up with a SaaS tool, they go off and do whatever with that data, and now they give you back a report.
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Right, this was a success.
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Do you think any vendor is going to show bad data for the month?
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You really have.
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No, you've lost control and insight into your business and you don't even know it because, look, I get like a good partner is going to hopefully be candid with the information, hopefully, but why do you even want to put them in that seat?
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Why wouldn't you be the controlling?
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Like, yeah, I'm going to use your tool for the output, but you're going to give me back inputs and then in my system I can see the entire loop and I think that's what dealers have to get to, and technologically there's the capability to do that today, where you're not reliant on renting systems.
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I think that's where the light's going to come on in the dark rooms for a lot of dealers is when they realize, oh, I don't need them for this anymore, and I shouldn't have had to need them in the past, but now it's never been easier and faster for me to get what I want without doing what you're just describing.
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So we know that this data ownership is transformative for dealers in their entire operations.
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It also has benefits into the customer experience, which is something that gets talked about and should be talked about.
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A question for you would be what's a realistic upside for dealers that end up with control of their data, Something that just they would be like, oh okay.
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Upside.
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Let's think Better decision making, because right now, if you're not getting the accurate data back from vendors or like all the data back, you're getting a curated view, you're getting a biased view.
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How can you make decisions on incomplete data?
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So to me, first part is transparency in the data gives you better data-driven insights, not bias-driven insights.
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So that's the first thing I think is important.
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Two, by owning and controlling the data, you will always have reliance on partners to do applications for us.
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But if you swap a partner out, which happens for whatever reason new technology If you swap a partner out, which happens for whatever reason new technology, disagreement, price increase, whatever it is you as the business owner shouldn't have to be starting back at scratch.
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You should just be unplugging a component, plug a new component in, think more like Legos.
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And the problem is with dealers today and I view this like I talk to groups all the time and they're like I talked to a group monday.
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Those of them they're like we have a big data lake.
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I'm like.
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I'm like we have 70 million people or something in the data.
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I don't know some giant number.
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I'm like it's like way too many people.
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All I heard, like listening to that, was your data is crap.
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Your data is crap.
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It was like coming into me because I'm like no way Based on number of stores they have and I know about how many customers per store is realistic because we have groups on, so I get a good sense.
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I'm like none of that math was going for me.
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But, that being said, I look at that and I found all the data lakes we've seen.
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It's like remember your kids had?
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You are a big Lego guy, right, right, I know you're.
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You are a super lego dude, or why are you super lego dude?
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I think you built a star wars lego once, if I remember, right, so when?
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When I, when I think about lego's and you know like, you have the box with all the pieces in it and it's like a disaster box, right, and you're like dude, you're in there.
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Okay, that's a data lake.
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So, yeah, all that data goes in there, but it's not valuable because it's just like a mess.
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Like if you broke apart the uh, falcon, falcon, like cruiser, and then some other jedi set and you threw it on a box, do you really think you'd easily be able to reassemble them?
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Not a chance in this planet that you, you know it would take you.
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You would have to separate them all first and go like all these I'd rather play with broken glass right, okay, so you don't want a like a data lake is not going to solve a dealer's data problem like you want.
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Like a data lakehouse you want yeah, of course you can.
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You want to be able to take everything in, but you then have to put it into things like all the windows go in this box, the tires and wheels go in this box, the figurines go in this box, the blue ones that are long going here.
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You want to separate things and organize the data and unless you do that, having a data lake is an absolute.
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You know what it is it is a thin, thin micro blanket on a minus 20 degree night.
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You think it's covering your butt, but you're going to freeze to death anyway.
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You think it's covering your butt, but you're going to freeze to death.
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Anyway, that is how I look at.
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When I see data lakes, we just call them data swamps.
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We've never tread into one where we weren't like, oh God, wow, they throw everything in here, but none of it's tagged right, there's not a lot of use for it, and you'll spend more time trying to extrapolate that than having a lake house, which is more like a data warehouse where everything has a shelf and organization and tagged and much easier to manage from.
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Yeah, you're touching on something relative to like limitations or downsides with middleware, right?
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A lot of dealers still rely on these middleware solutions and so love to know maybe some of your additional thoughts on the kind of problems that middleware creates.
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I mean, I think there's some obvious ones, but I think dealers probably could stand to hear some of these things that maybe don't initially come to mind for them.
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Yeah, look the first thing I always say.
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Yeah, look the first thing.
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I always say it's like data schema right, like so.
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I call it ID underscore customer.
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You call it customer underscore ID problem.
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Next guy just calls it customer, like so.
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First off, you have a sea of vendors.
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Everyone's data is structured differently.
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Nightmare.
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How do you deal with that?
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So, this is why the dealer you want a organized schema.
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Let your vendors access it and return back to your schema, not all theirs, because managing 30 vendors and all their disparate schemas in middleware is impossible.
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So even with AI and like listen, we spend a crap ton of time, sean, even just normalizing the chaos that comes out of like even DMS systems, case in point.
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So, having fun, you know we rip through cars.
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So oh, here's all the cars.
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Then we'll see something like stocked in as ct, like ct short shorthand for chevrolet, truck or ho, honda, because the guy, that person who sat in that thing, who onboarded that car, new stock, right, they shorthanded it.
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The other person who does it doesn't shorthand it and like they're.
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So this is bad data in bad data out.
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So we've had to build.
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So this is bad data in bad data out.
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So we've had to build multiple ML programs looking for what we call anomalies in data just to clean up bad.
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Now you could have just thrown them away and you'd be throwing away cars because it doesn't fit in the box.
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So not only do you have boxes, that everything has to fit in, then you have to write programs.
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That says I think what you're trying to say, sean is that is a Chevrolet truck, or we'll see stock number in instead of a make, we'll see someone booth the VIN and this is all human error.
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And same with customer files.
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See tons of data issues there.
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You know, in some systems you'll have to have an email address, so it'll just be na at nacom Because it's trying to bypass like a screen.
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So you know where, where we put, where some of these systems have like restrictors in, uh, so you gotta normalize all of that, you know, and you have to calculate, like well, think about just super complex things where customer and this is why middleware will never work, because you have to do this deep data work um, to ultimately advantage ai like okay, um, family of four husband, wife, daughter drives.
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Now son is 15, so there's two cars in the fan, three cars now in the family, but the daughter brings the dad's car in or the dad brings the daughter's car in and like you have just a cross of how you have to understand that.
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Like no, no, no, this car is this person's.
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I understand this person, been it, but this person is related to this person because we see him in a household, like, so you have to have that complete understanding and that this is the world like we live in right now no-transcript, and many look.
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I will say zero.
00:24:54.269 --> 00:24:55.541
Zero.
00:24:55.541 --> 00:25:16.691
Groups that we have seen talked to are currently doing business with had or have, like, organized data Zero, so they're going to have very limited results with AI in the future unless they get their data right first.
00:25:18.741 --> 00:25:21.289
And they can't do that with the middleware.
00:25:21.289 --> 00:25:38.109
So the next thing I want to ask you is if a dealership actually it should be, when a dealership wants to move away from middleware and fully own their data, yep, what would you recommend or say is the first step?
00:25:39.279 --> 00:25:39.441
Look.
00:25:39.441 --> 00:25:42.390
The first step you've got to figure out what you're going to do.
00:25:42.390 --> 00:25:47.172
So you need to create an architecture of okay, we are going to go and look.
00:25:47.172 --> 00:25:50.400
So you need to create an architecture of okay, we are going to go and look.
00:25:50.400 --> 00:25:55.386
Groups have taken that first.
00:25:55.386 --> 00:25:56.859
I will say quick, step to say get it all in a data lake, right?
00:25:56.859 --> 00:25:59.884
Is that the best first step?
00:25:59.903 --> 00:26:18.305
I'll say it's a good first step Probably not the best, but it's a good one, probably like oh yeah, we got to own our data, we got to get it all in one spot, totally on board with that.
00:26:18.305 --> 00:26:20.089
So I think it's one understanding where all your data lives.
00:26:20.089 --> 00:26:33.593
So first goal is mapping your data infrastructure, so the systems you're using, the data that's in them, the data that's available to be pulled out of them, and understand the correlation of how these systems are interacting with each other.
00:26:33.593 --> 00:26:36.847
You will find very common Like an audit.
00:26:37.490 --> 00:26:38.353
Yeah, like an?
00:26:38.353 --> 00:26:52.313
I won't say yeah, okay, like an audit of all your current technology, how it connects and talks to all the other technology, including how data moves, what's available, what's not, all the way down to even inventory.
00:26:52.313 --> 00:27:06.488
You may find you're spending and you have fees being charged to you on services you canceled a year ago, but nobody ever turned off the data pipes in the back, because nobody sees them or redundant vendors Tons.
00:27:07.361 --> 00:27:11.506
We see this like, oh my gosh, we go through and they're like what?
00:27:11.506 --> 00:27:15.330
I was like, dude, you're spending six grand for this stuff.
00:27:15.330 --> 00:27:19.911
I've yet to find anyone organizationally use it.
00:27:19.911 --> 00:27:22.208
So we're like I feel like we enter groups.
00:27:22.208 --> 00:27:26.832
We're like rats chasing the data through a maze.
00:27:26.832 --> 00:27:29.568
We're looking and I know there's cheese at the end.
00:27:29.568 --> 00:27:35.413
So I'm like I know we're going to trace this and we'll say, oh, show me what system you use.
00:27:35.413 --> 00:27:36.925
I've used this system, this system.
00:27:36.925 --> 00:27:38.606
And then okay, then what happens?
00:27:38.606 --> 00:27:41.068
And then you're like, wow, then it just kind of stops here.
00:27:41.068 --> 00:27:43.000
Maybe it turns into a report or something.
00:27:43.000 --> 00:28:04.611
And I'm like, okay, but it's very interesting when you start peeling it back and I take this lesson and I've been rooted in this lesson because of interactions with groups and the conversation of like, just fix our data.
00:28:04.691 --> 00:28:06.413
And I was like that's not that easy.
00:28:06.413 --> 00:28:16.646
That's like Elon when he went to russia and just tried to buy a rocket, right, and the russians were like, oh you, little internet kid from america, we're not going to sell you a rocket.
00:28:16.646 --> 00:28:20.842
Or they're like we want 40 million us dollars.
00:28:20.842 --> 00:28:26.651
And uh, and they basically laughed elon out of the.
00:28:26.651 --> 00:28:32.769
They're like you're not NASA, you're not a government, because who else would want a rocket but a government right?
00:28:32.769 --> 00:28:34.626
And he's like no, no, I want a rocket.
00:28:35.681 --> 00:28:44.868
So not many people know the story, but he flew home that night with the two guys he had hired and the two guys in the back were like well, honestly, that was a shit show.
00:28:44.868 --> 00:28:47.905
On the plane, Elon sat on his computer.
00:28:47.905 --> 00:28:59.250
By the time he landed he had already figured out what it actually costs and the raw materials to build a rocket and who he needed to now go talk to to do it himself.
00:28:59.250 --> 00:29:06.932
It's crazy, but sometimes you have to peel things back to like what is actually going on.
00:29:06.932 --> 00:29:08.104
We use this system.
00:29:08.104 --> 00:29:09.288
Why do you use this system?
00:29:09.288 --> 00:29:13.664
We've always used the system, Todd, I understand that, but why do you use it?
00:29:13.664 --> 00:29:15.705
What is it actually doing for you?
00:29:15.705 --> 00:29:16.509
I don't know.
00:29:16.509 --> 00:29:20.209
I'm just I plug it in here, but I totally get that.
00:29:20.209 --> 00:29:22.342
You plug it in there, but where does it go?
00:29:22.342 --> 00:29:23.063
I don't know.
00:29:23.063 --> 00:29:30.414
Okay, Like I hit so many maze like walls that I'm like what is going on?
00:29:36.140 --> 00:29:37.546
Tons of redundancy and tons of data not moving between systems.
00:29:37.546 --> 00:29:52.144
So it's keyed in by different people, same data, so then it's different data because they keyed it in differently, Like they represented the same, say, data element differently, and you see this constantly.
00:29:52.144 --> 00:30:04.664
And these are the things where it's like okay, if you want your organization, your dealership group, to run efficiently, if you think about it, it's going to begin by good quality data.
00:30:04.664 --> 00:30:09.054
And let's be honest and look, you know a lot of dealers too.
00:30:09.054 --> 00:30:26.005
Like could we call if we sat on our core conversations right now, our podcast, and we call dealers and we ask them one question and they have to give us an honest answer Do you feel your data is clean?
00:30:26.005 --> 00:30:29.271
What do you think our outcome would be?
00:30:29.271 --> 00:30:35.628
Whether they'd be honest or what the truth would tell us.
00:30:36.342 --> 00:30:40.952
One dealer or group say yeah, we have really clean data.
00:30:40.952 --> 00:30:50.527
Okay, so beginning they're operating inefficient but dealerships are so profitable nobody cares.
00:30:50.527 --> 00:30:52.588
But they're going to care this year.
00:30:52.588 --> 00:30:53.288
Why?
00:30:53.288 --> 00:30:55.971
Because margins are getting whacked.
00:30:55.971 --> 00:30:57.813
We have this whole tariff thing.
00:30:57.813 --> 00:30:59.694
It's going to affect new cars.
00:30:59.694 --> 00:31:01.936
We already have used car problems.
00:31:01.936 --> 00:31:06.445
Service again is like this last line of defense.
00:31:06.445 --> 00:31:10.433
But service is a lot of times not efficiently operated.
00:31:10.433 --> 00:31:20.493
You have bleed out to third party services, right, so we're only entering an exponential time of more and more competitiveness.
00:31:20.493 --> 00:31:33.150
Like you probably need to get your data right first, and the other side is you're also today, if you think about it, if you know you have bad data and it does track, and I'm going to put this.
00:31:33.150 --> 00:31:34.988
So Cox did a study.
00:31:34.988 --> 00:31:40.209
Seventy some percent of dealers said they don't trust their own data.
00:31:43.118 --> 00:31:46.815
Think about that for a minute 70, I think 78%.
00:31:47.885 --> 00:31:50.521
Don't quote me on that, but it was in a research paper.
00:31:50.521 --> 00:31:54.401
They don't trust their own data, so now think about that.
00:31:54.401 --> 00:31:58.705
So you have data you don't trust.
00:31:58.705 --> 00:32:04.766
You are making million-dollar decisions against Monthly.
00:32:06.469 --> 00:32:07.029
Unbelievable.
00:32:12.494 --> 00:32:13.758
Well, okay, so now you're in an airplane.
00:32:13.778 --> 00:32:47.243
I don't believe my altitude gauge, I don't believe my horizon line, I don't believe my airspeed and we've just yeah, but yeah, I am totally uh iv, like you know, at that point and when you start looking at it and you're like man, like this has to change Because we're entering an accelerated time of competitiveness, it's only going to get worse and to me, efficiency is where it's all going to land.
00:32:47.243 --> 00:32:49.087
So how do you get efficient?
00:32:49.087 --> 00:32:57.038
Well, you better understand your data and your data better be accurate and your data better be able to stand on its own.
00:32:57.038 --> 00:33:04.037
So I think and again, it's hard, dirty work, but it's so valuable when you get the data right.
00:33:04.037 --> 00:33:11.624
And then you know we have groups that now they hook their BI, power BI or Tableau or whatever to real good, accurate data.
00:33:11.624 --> 00:33:18.949
That's invaluable in and of itself to get your data organized, enhanced more intelligence on it.
00:33:18.949 --> 00:33:28.836
You know you can I put the accoutrements on their data right, like added additional insights, then start pairing it like okay, do people even own those cars?
00:33:29.336 --> 00:33:36.516
So 40%, I would say, of the cars in most DMSs people don't even own them of the customer database.
00:33:36.516 --> 00:33:43.950
So that data is raw, extracted by a direct mail or agency who has then created.
00:33:43.950 --> 00:33:46.942
Now think of the money cost money to extract.
00:33:46.942 --> 00:33:49.356
Now I build creative.
00:33:49.356 --> 00:33:53.450
Cost money to do creative and now I send it to that audience.
00:33:53.450 --> 00:33:55.576
That's an inaccurate audience.
00:33:55.576 --> 00:34:12.340
To begin with, if you don't have the data right, everything downstream breaks or I won't say breaks operates at an insane level of inefficiency, just like software and dealerships.
00:34:12.340 --> 00:34:20.025
Because, think about it, you were at Reynolds when you got a new store keyed up and operational dude, they used the crap out of it.
00:34:20.025 --> 00:34:25.461
They're probably at 85, 90% of efficiency using all the systems.
00:34:25.461 --> 00:34:36.264
You go back in three months they're at 50% Because turnover new guys train poorly, tribal knowledge is leaving the building.
00:34:36.264 --> 00:34:39.744
You go back in a year it's 30%.
00:34:42.461 --> 00:34:42.722
Yep.
00:34:43.724 --> 00:34:44.987
So, Absolutely.
00:34:45.434 --> 00:34:46.255
Because of this.
00:34:46.255 --> 00:35:06.369
This is why I also think, think not only do you need to clean your data, you need to capture the intelligence of your organization, and if you fail to do this in this coming wave of AI applications, you won't be left in the dust.
00:35:06.369 --> 00:35:08.141
You'll never get out of the gate.
00:35:08.141 --> 00:35:09.083
Like.
00:35:09.083 --> 00:35:23.576
There are going to be stores and groups that go ai first and they will win, and they'll win at a rate that I don't think, like I always.
00:35:23.576 --> 00:35:24.777
I think we talked about this before.
00:35:24.777 --> 00:35:26.797
I look at it like a flow of a river.
00:35:26.797 --> 00:35:37.121
I say that if you enter the race and you start learning, and now your competitor enters the race three months from now, they start learning.
00:35:37.121 --> 00:35:38.862
They don't catch you.
00:35:38.862 --> 00:35:42.523
Now in SaaS, you could catch up to people.
00:35:42.523 --> 00:35:49.045
In AI, you don't catch up to people because AI is not a SaaS tool, it's not a product.
00:35:49.045 --> 00:36:01.969
It is a living, breathing species that learns, adjusts and improves through time and experience, like.
00:36:01.989 --> 00:36:02.769
Look at Google Car.
00:36:02.769 --> 00:36:03.949
Google Car.
00:36:03.949 --> 00:36:07.692
From the beginning, waymo was not very good.
00:36:07.692 --> 00:36:20.835
Look at it now you go to Scottsdale, dude, be hopping like hopping Waymo, pretty much you know, drives without flaw, without issues, but it didn't start there.
00:36:20.835 --> 00:36:27.277
And think of all the data input and all the simulated roads, it road Like.
00:36:27.498 --> 00:36:29.695
I put a thing that I thought was interesting.
00:36:29.695 --> 00:36:31.559
Think about auto.
00:36:31.559 --> 00:36:33.503
So a dealer.
00:36:33.503 --> 00:36:35.045
I will tell you this future.
00:36:35.045 --> 00:36:37.789
I see as clear as day.
00:36:37.789 --> 00:36:40.818
Dealer organizes their data.
00:36:40.818 --> 00:36:45.224
Everything's clean and operational Data.
00:36:45.224 --> 00:36:48.489
All the organizational's knowledge is uploaded.
00:36:48.489 --> 00:37:09.114
So everything from like brand and tone, key value props, usage and best practices, unique selling props, do's and don't, guidelines for governance, faqs, sample Q&A content, templates and examples for the brand for everything, lexicon, terminology, summary, like.
00:37:09.114 --> 00:37:11.885
All this is loaded into the brain right.
00:37:11.885 --> 00:37:20.034
Once that happens, you basically say create synthetic audiences based on my audiences.
00:37:20.034 --> 00:37:28.588
Now test the ads against in real time and before you ever run an ad, you'll know how it'll perform.
00:37:28.588 --> 00:37:33.079
By cohort You'll be like the eco-conscious person.
00:37:33.079 --> 00:37:38.548
This ad will resonate the wealthy person on a passive income.
00:37:38.548 --> 00:37:39.896
This will resonate.
00:37:39.896 --> 00:37:44.467
That is coming like right now using synthetic digital twins.
00:37:44.467 --> 00:37:50.679
So you won't spend the money to market until you know it's pretty much been validated.
00:37:54.628 --> 00:38:07.983
Yeah, so once again, you've just absolutely dropped knowledge bomb after knowledge bomb on data ownership, kind of what's here how the future is.
00:38:07.983 --> 00:38:24.635
I love when you share so many of these examples, that for a dealer that is listening to you or reading the content that you put in, especially the LinkedIn, when you share things like, hey, if you don't own this stuff, you have no control over what's happening in the future.
00:38:24.635 --> 00:38:35.197
If you don't get this in line and prioritized today, everyone that does it before you will truly have a competitive advantage.
00:38:35.197 --> 00:38:56.168
Like you're, you're cutting through some things that have been um, cliche, uh, almost platitudes in our industry for so long, that aren't necessarily true, with statements that are absolutely true, backed up by evidence of what's really happening.
00:38:56.168 --> 00:39:09.407
And it challenges a lot of the people that talk about data lakes and CDPs and all this other kind of happy horse shit that they use really for their own monetary gain.
00:39:09.407 --> 00:39:24.632
Right, because it shifts the industry thinking that you're cutting through things that the dealers really need to know, regardless of how they decide to act on the facts and on the information.
00:39:24.632 --> 00:39:26.240
They need to know it.
00:39:26.240 --> 00:39:30.701
And I love that you come from that perspective of educate, educate, educate.
00:39:30.701 --> 00:39:41.927
But yet, as I say all the time you got to start somewhere and there's a difference between knowing and doing, but you at least need people to know.
00:39:41.927 --> 00:39:48.726
And so, as we close out this, once again, another freaking, great, great conversation.
00:39:48.726 --> 00:39:49.849
Super good episode.
00:39:49.849 --> 00:39:55.525
I want to encourage the audience Make sure you go back and check out this full article.
00:39:55.525 --> 00:39:59.706
We couldn't even get into all of it just on talking about some of this on the episode.
00:39:59.706 --> 00:40:04.186
The article that Todd put out on this is even more informative.
00:40:04.186 --> 00:40:11.001
It's a great companion to this conversation, or the document to the conversation, conversation, to the document that I highly recommend.
00:40:11.443 --> 00:40:16.003
If your dealership wants to stay competitive maybe you're not that competitive right now.
00:40:16.003 --> 00:40:18.599
If you want to get competitive, you want to cut costs.
00:40:18.599 --> 00:40:21.788
We talk about future proofing your operation.
00:40:21.788 --> 00:40:24.057
Good grief.
00:40:24.057 --> 00:40:27.927
You need to get on this horse right now and start kicking up some dust.
00:40:27.927 --> 00:40:32.623
You've got to take control, first and foremost, with your data.
00:40:32.623 --> 00:40:33.706
You've got to own it.
00:40:33.706 --> 00:40:35.378
You've got to get involved with that.
00:40:35.478 --> 00:40:40.400
Todd doesn't say this stuff to hear the sound of his own voice, so I'll say it for him.
00:40:40.400 --> 00:40:43.028
The best place to connect with Todd is on LinkedIn.
00:40:43.028 --> 00:40:50.135
He is absolutely abundant with and easy with his time, so if you want to DM him there.
00:40:50.135 --> 00:40:57.744
He talks about a lot of things that right now, dealers are not necessarily comfortable dropping comments into the posts that he puts out there.
00:40:57.744 --> 00:41:05.384
So feel very, very confident that you can actually send him a direct message on LinkedIn and he will actually respond to you.
00:41:05.384 --> 00:41:10.565
If you want to have a private conversation with him, that's the best place to connect with him.
00:41:10.565 --> 00:41:19.338
If you also want to connect with him through email because you're really, really old, well then you can do that at Todd, and he doesn't spell it like any crazy person.
00:41:19.338 --> 00:41:33.126
It's T-O-D-D at CoreAIcom and that's Q-O-R-E-A-Icom Todd at CoreAIcom, or just hit him up on LinkedIn.
00:41:33.394 --> 00:41:34.940
That's a wrap for the episode today.
00:41:34.940 --> 00:41:38.505
Folks, thank you, as always, for listening and or watching.
00:41:38.505 --> 00:41:41.222
If you found the conversation valuable, subscribe.
00:41:41.222 --> 00:41:44.981
If you're out on YouTube any other places, share it with people that need to know about this.
00:41:44.981 --> 00:41:46.159
They need to hear this message.
00:41:46.159 --> 00:41:49.161
It's the reason why Todd makes this kind of content.
00:41:49.161 --> 00:41:58.420
We will, of course, be back with more insights and how you can stay ahead in automotive retail, specifically around owning your data and using AI to transform your future.
00:41:58.420 --> 00:42:02.427
Until then, go free your data, dealers.
00:42:02.427 --> 00:42:04.998
Go, let it be free.
00:42:04.998 --> 00:42:09.577
Thanks again for an awesome episode, todd thank you really appreciate it.
00:42:10.139 --> 00:42:10.842
Always great to be here.