How AirGarage Uses Dynamic Pricing Based On Supply & Demand

You know, that you do dynamic pricing. So, you know, based off supply and demand, of course, for, you know, for some of the spaces in different cities, and even kind of like on a neighborhood level. So could you maybe share, you know, how you think about that? And, you know, maybe some of the kind of like interesting observations or learnings around dynamic pricing?

Yeah. So dynamic pricing is really interesting, and pricing in general for parking is really interesting. And the supply and demand problem of parking is actually very interesting in and of itself, where I think most people, when you think about parking, you think, okay, if you're in a downtown of a major city, yes, you're going to pay for parking. But if you're in like rural or suburban or, you know, small town America, you're not going to pay for parking because there's just not that many people, right? And what we found in our experience is that, you know, parking is this very localized supply-demand problem where as soon as you have any small radius where the demand for obviously people being in that area is greater than the supply, you now have a parking problem. And the best way to solve that parking problem is through the economics of charging for parking, right? And this is where actually a lot of cities have gone awry in the last 50 or 100 years is that they're making all of their parking free because they think that that's what people want. And then that induces demand for people to drive to that area. And then suddenly they don't have enough parking and then they build more parking. And then you wind up with cities that look like Houston in the 1980s, just all surface lots, right? And so our philosophy is you can build better cities if you first of all collect data about how parking is being used, when it's being used, why it's being used, And then by optimizing the pricing and charging the appropriate rate for parking such that you're not subsidizing parking implicitly by not charging for it. Right. And so there's a lot to think about with cities there. But on such a localized level, supply and demand can be imbalanced. Right. You would think of, you know, there was a city we managed a parking garage for, a municipality. It's a town of 18,000 people in Minnesota. You would think a town of 18,000 people doesn't have a parking problem and doesn't have any paid parking. But it turns out that when all 18,000 people want to be on the same 3 blocks, on Friday night to get all the restaurants and bars, now suddenly you do have a parking problem. And so you have to have paid parking. And so that's really interesting in that there's so many niches out there of parking demand and supply imbalance that you wouldn't think of necessarily. And so one of the things that we do uniquely that nobody else in the industry does is real-time experiment-driven, data-driven dynamic pricing. And that's done on a per-lot basis at different times, different days of the week, different seasons, all this sort of stuff. I'll explain how that works in a second, but the reason it doesn't exist in the industry to date is because most of the companies that are running parking lots are just these traditional management companies, right? They are old school property management businesses, not technology companies, and they buy all of their systems from third parties. So they buy a gate machine from a third party, they buy a payment machine from a third party, they buy an app from a different party, they buy their backend software from a third party, right? And they're all from different third parties. And even within different locations that they manage, they'll have different systems within each of those garages. And so you can imagine that this becomes a huge nightmare for integrations because you have a non-technology company integrating 5 different systems and trying to make them work together. And then they also aren't software engineering companies or technology companies. And so they can't actually change those systems. And so the unique thing that we've done is we have built all of our own systems in-house. So our payment system, our enforcement system, our license plate reading cameras, everything is our own technology. So it is all like sort of inherently integrated in a deep functional way that nobody else's is, which then enables things like, hey, we control the payment system and we can see all of the downstream metrics of that payment system. So when you're a driver pulling into an Air Garage facility, what we'll actually do is we basically internally call this like ABCD+ testing where, you know, you've heard of A/B testing, but it's, hey, we can segment drivers that are pulling into a facility into different buckets. So you will pull in at the exact same time as somebody else and be shown a slightly different rate structure. And then we can track how all of the people that are being shown the 2 different rate structures or X number of rate structures are responding to that. So are they parking or are they not parking, or are they leaving the facility? How long are they staying for? How much are they paying in total? And then, you know, what was the review that they left at the end of the session? And do they come back to the facility? And all these downstream metrics then inform what is the appropriate rate structure that you should have in this facility. And we've seen a lot of success with this where There's parking lots we've taken over that were, you know, well run before by a local company that, you know, was doing a fine job but didn't have any of this technology advantage. And we've increased their gross revenue by like 40% year over year, even though traffic was the same. And it's just because the lot was mispriced because they didn't understand the data behind their pricing. And most companies set their pricing by just sticking a finger in the wind and checking the rates in the local area, you know, once every 2 years or so, right? So it's really like all powered by this sort of full stack, hey, we own the entire technology stack. It's sort of like, maybe the best comparison for this is the Apple walled garden that everybody talks about, where if you're really in the Apple ecosystem, and you truly adopt it, then they have these really great experiences that you get to have because of that. It's the same thing here where we're sort of very strict about we only use our own technology, and we use the same technology in every garage, which then means that every garage we onboard, We have more resources to pour back into making that technology better, which then helps us make more money and helps the owner make more money, which then gets us more properties. So yeah, dynamic pricing is done in a very sophisticated way on an individual property basis by running these experiments to see how drivers react to the pricing that we're doing to then figure out what the optimal price is. And I want to clarify also, just in case this, you know, I know this is being recorded and somebody might come after me later, the optimal price does not always mean raising the price. Sometimes prices are too high and you need to lower them to get people to park in your lot. It can go both ways.