Okay. I want to show you now. So going back to the tool you use today for daily management, how many cars have been priced in seven days? I'm going to show you how we're going to do it better, different, in a variable way that makes a lot of efficiency happen and is much more sensible. So every day, instead of looking at how many cars you don't reprice in seven days, what we're going to see here is your alignment. Five Precious Metal bucket. So first of all, we're going to notice that we are 88% of our vehicles priced in the recommendation range. Pretty good. Pretty good. If it was 100%, I'd be yelling at you like, hey, you're you know, you're not doing your job, you're just letting the tool do its job. But 88% is is a pretty good acceptance range. But here's where it gets really powerful. Platinum. Platinum, when we didn't accept the recommendation range, we went, I think one under in two over. Gold, we went three over, no under. Silver, we're all within the range and and on bronze, we're one under and three above. This is ten cars in total. If you only looked at ten cars today, you could do to possibly revisit changing their price. If, if if you did that, you could have pretty darn good peace of mind that the rest of your inventory is in good shape. Think about that. Think about how much more efficient that is in your time. Think about how much smarter that is to not have to look through a whole bunch of cars, many of which should have been repriced three, four or five days ago, many of which don't need any price today. It's just smarter. It's more efficient. It's not the blunt instrument of how many cars in seven days. But I'm going to show you where this gets really interesting, this alignment tool, if you're responsible for any store, but particularly managing over an enterprise, if you manage over multiple rooftops, I think you would all agree with me that stores have personalities just like people. Right. And the personality of a store is is is reflected by the collection of their behaviors. Well, you want to know something? Your pricing manager, the person who price is your cars has a certain personality that is reflected in the way they price their vehicles. And what you're going to see for the very first time is the ability to surface opportunities and risks based on the varying personalities of the individuals you have at your rooftops right now doing this job. And I can tell you they'll all be different. So let me show you how this works out with an example of dealership ABCD. Here is your alignment. You're sitting on the back of your yacht floating in the Italian Riviera, and you look on your on your phone and you look at your rooftop A. Here's the alignment. 51%. So the first thing that you know here now is that 49% of the time your pricing manager probably looked at data science roughly half the time and said, No, no, I don't think so. I know better. You know, maybe they do. But that's the first thing you know. They're, you know, roughly 50% acceptance. But more importantly, let's look at the distribution what their personality reflects, what is the personality reflecting in their store? What's the mindset of the person who is pricing cars at the store? What's their mindset? Well, you can see what their mindset is. It's heavily oriented towards gross. Heavily oriented to gross. Eight over on platinum, six over on gold, and I think seven over on silver and nine over on bronze. Six, five seven, nine. Yeah. They're they're heavily indexed on gross And listen, gross is fine but floating on your yacht in the Mediterranean, what you can now see potentially that you would not otherwise have been able to observe at this store is maybe there is an opportunity for some more volume. Maybe there are some trade offs to be made between average gross and total volume, probably an insight that would not have been observable using the method in the software and the methodology we have today. Let's look at your rooftop B. Here's a different personality. What's going on here? You see what's going on here. This is the opposite. This is the store that's indexing heavily on on volume, perhaps at the expense of gross. And volume's great. But once again, immediately, at a glance, at a glance from a distance, you can observe the personality, the mind set. It's like looking into the mind of the person who you would trust to price your vehicles, to have an idea as to what their mindset looks like and how they're behaving and potentially what sort of opportunities or risks that surface. You wouldn't have this level of insight. Now, here's the interesting one. Rooftop C. I'll tell you, this is the most common one. This is the most common one. I would bet that if you are using the ProfitTime software right now, not using the methodology, the strategy of Variable Management, but just paying for the software. I bet this is what you look like. Now, what's going on here? What's happening here? Total mistrust. Well, okay, so we can see you're saying mistrust, because the overall acceptance here, I think, is 46%. Is that right? Well, okay, but why? Oh, well, it slow down. Let me just make the first observation, okay? This is 46% acceptance, 54% of the time your manager is saying no to data science. Now, he probably or she is probably right on some of those. But 54% of the time he or she thinks the data science is wrong. It's hard to question that. But okay. Now, now to the next level. You say similar number below and above. And that might be true. I didn't add those up. But let's look at the types of cars that they're going below on versus the types of cars they're going above on. That's where the real insight on savings, right. Where are they going above when they reject or refuse to accept your recommendation? Where are they going? What types of your vehicles are they going higher? Yeah. And why and why are they doing that? And you do know the answer. You do know the answer. Cost. You're right. They don't want to face the music, and that's understandable. I understand that. Who wants to, you know, who wants to face the music? None of us do. It's human nature not to want to. It just happens not to be good business. So here's the problem they have that reflects their behavior with platinum and gold. While they're sitting with a significant part of their inventory that's priced not to move and you're phoning them from the Mediterranean saying, why aren't we selling cars? You know where they go to sell cars? No. They go where it's easy. They go cheap price their platinum and gold cards. Because they own them right, they could cheap sell them and still reflect some decent profits, not the profits they should bring. But this is what happens, and it's completely consistent with the psychology that we all understand exists in these dealerships. Now, for the first time, you can see it at a glance and you can see it through the lens of objectivity of data, such that you can have meaningful conversations with a guy or gal who isn't saying, well, this that I mean, look, here's here's your pattern, here's your behavior, here's your personality. Help me understand why that is working. I mean, this is really powerful. Insights that are surfaced about behaviors that actually matter to the outcomes of your businesses. And then rooftop D. Is how it should look. This is what it should look like when it's done right. Yeah. You know, 85%. I think it's completely legit that your pricing manager, 15, 20, maybe even 25% of the time says no, I think there are reasons, legitimate reasons why we shouldn't accept that recommendation. But but when they don't accept it, they're not demonstrating a behavior that is overweighted to one side or another in terms of volume or gross. This looks like what it should look like when it's done right.