What you see here is what you would see in the software. And I'm showing you software now, and I know it's going to start to look like I'm selling you software. Promise you, that's not what I'm doing here. But it's important to see how important to see how this works. Okay, what we now do or have the ability to do is put what we call a recommended range, price range, recommended price range. Two really important words, recommended and range. Recommendation on every vehicle, every day. In this example, it's a 2020 Honda CRV. It happens to be a platinum car. It could be a bronze, gold, silver, whatever. But the recommended price range on this vehicle today is between 36 and 36,500. Now, do you need to price your vehicle within that range? No, it's a recommended range. You can price it below that range. You could price it above that range. Are there ever reasons why you should legitimately reject the range recommendation? Absolutely. There are legitimate reasons why and we could talk about some examples of what those are, but for now, suffice it to say that there are recommended ranges for every vehicle every day. What would happen if you accepted the majority of the ranges? What would happen? Well, what would happen if you accepted the majority of the ranges consistently is you will get those outcomes that I showed you that will optimize the return on investment of these vehicles. Roughly speaking, not every car, but on average your platinum cars will sell in 50, 40, 30, 20. If you accept the majority of the range consistently. Now, how does that happen? I mean, where are these ranges coming from? Are we plucking them out of thin air? Do we have some guru in a cubicle in some room someplace? No. Let me tell you what we now can do. Listen carefully to this. We can predict with with a high degree of accuracy the probability of any vehicle selling at any price in the next seven days. Say that again. We have developed a probabilistic algorithm that can predict the probability, frighteningly accurately, of any vehicle selling at any price in the next seven days. This algorithm considers everything we know about the vehicle, which is not everything. It's one of the reasons why we need a star person on the other end of the tool, but it considers everything we know about that vehicle. It considers everything we know about your market. And importantly, it knows everything that we can possibly know about your store's dynamics. Because what we now clearly can see and show objectively is that for one reason or another, sometimes known, and sometimes unknown reasons. Some stores have the ability to sell some cars much faster than others, even at a higher price. And this reflects every store's ability or lack of ability, as the case may be, to sell certain vehicles at certain prices so we can predict the probability of any vehicle selling at any price in the next seven days. Now, we don't want every car to sell in the next seven days. Definitely don't want that because we'd be leaving a lot of money on the table. But these price recommendations are calibrated based on the vehicle's investment score and metal bucket. They're calibrated to drive the outcomes of 50, 40, 30, 20, which again are the ones that will optimize the return on investment. So when it gives you a recommended price range on a platinum vehicle, it's going to be based on a probability on day one that's kind of low on day two, still low, on day three, maybe a little bit higher, on day four, day five. So as the days click off the calendar, because we're moving towards that 50, that pricing algorithm is going to give you the recommended range that increases the probability. On a bronze car, on day one, the recommended price range is designed to have that vehicle not sell in seven days. That would be too fast. We're going to adjust it to sell it in 20 days. That's a major difference. That's technology. That's data science working. So, you know, and I totally respect what you did at the Morgan Group, you know, with day supply. But still, once you heed on the day supply, you know, should it be priced here in the range or the competitive set or there? Different people would disagree. Well, today we actually have guidance based on science. And listen, I know this is hard. I know it's really hard for us in the industry to let our judgment, our instinct, our experience be challenged by data science. And I'm not here to tell you that you should accept all these recommendations. Definitely not here to tell you that. There are definitely things that our probabilistic algorithm does not consider, that does not know. We're always trying to make it better, but there are definitely things. So we still need somebody on the other end of the tool, you know, vetting it. But even then, I know it's hard for us as an industry culturally to, you know, subjugate maybe some of our instincts to the science. It's another reason why this is really hard. Please. I would say yes, it does that but probably not by somebody typing into the algorithm for your store, that your a Cadillac store, it will probably know your Cadillac store by looking at your history. You know. Correct. And let me tell you something, the longer we've done business with you, the smarter this pricing algorithm gets in terms of learning the dynamics and characteristics of your store. Fascinating. It just keeps getting smarter and smarter. Where we actually struggled a little bit is when it's a brand new dealership who we've not even done Velocity or Provision business with. In those situations, these pricing algorithms we actually ask people to ignore for like 45 or 60 days because we do need to get history, you know, an understanding of your particular dealership's dynamics. So I'm not sure if I repeat that question, but basically I think you got the essence of it. It was how much does this consider the brand of your dealership? Good. Good questions. Thank you. So with these recommended price ranges, every day, we're guiding you, guiding you to those outcomes. Of 50, 40, 30, 20 by being a little bit more patient with some at higher prices for longer, being less patient with others, with more urgency to get other cars moved out. I mean, at some level I think you got to agree. It just makes sense. Not all cars are the same. Just taking that Velocity blunt instrument approach and saying, every car out as fast as possible on a lot of cars we're leaving a lot of money on the table.