March 03, 2023
Are you leaving money on the table? If you haven’t optimized your car wash pricing, the answer is probably “Yes.” Even if you recently changed your pricing, there is always room for optimization.
In this one-hour webnar, DRB® and SUDS® explain why you may be leaving money on the table with your current car wash pricing and how PrecisionPricing® by SUDS can help you get pricing right.
Kayla Ivey, DRB Product Marketing Manager: As everyone's hopping on, we are going to be talking today about a topic called PrecisionPricing. So we're going to be discussing how strategic pricing can make you more money at your wash without having to add a single extra vehicle. So that's going to be through strategic pricing, pricing optimization.
And while you're joining, while we're kind of getting settled, there's a Q and A button at the bottom of your screen, so that's where you're going to put any questions as we go along. We will save some time at the end of the webinar to do a little bit of Q and A, probably five to 10 minutes or so. And then if we have other questions we don't get to, we'll follow up in an email. So anything you're wondering about as we go, put it in the chat, or sorry, put it in the Q and A. Even if we don't have time, we'll follow up with you afterward. And then if you have any technical difficulties or need any help with anything like that, you can put that in the Q and A as well, and we've got a team that can jump in and help you.
All right, I'm going to go ahead and dive in. We've got a lot of ground to cover with this topic today. So we'll do some intros before we get started. My name is Kayla Ivey. I am a product marketing manager for DRB. And I've been in the car wash industry for about six years now, a little bit over. And through that experience, I've been able to really work hand in hand with a lot of different car wash owner/operators of many different sizes, many different needs. And so through that, like I've been able to understand what, you know, truly drives revenue, what impacts revenue the most, what helps car wash owners, you know, hit their goals as quickly as possible. So that's some of the insight that I'm hoping to bring to the conversation today.
Kirk Fletcher, SUDS Director of Data Analytics: My name is Kirk Fletcher. I'm the director of data analytics at SUDS Creative. Prior to SUDS, I was working for 10 years in the finance industry doing investment portfolio analytics, and now I've been at SUDS for going on three years. I work on analyzing pricing for car wash locations, looking at the retail potential of different locations, consumer analytics, just the different parts of the data side of the car wash industry that we can use to help out car wash operators.
Kayla: Awesome. Thanks everyone for joining. So we're going to give you just a little overview of SUDS if you're not familiar. So SUDS is part of the DRB family of brands, and SUDS is really the marketing and analytics arm. So SUDS' main goal is helping car wash owners grow, hit revenue goals, you know, growth in whatever that means for them and what your particular goals are.
So we do a lot of that through understanding data and being able to guide car wash owners on, you know, what levers to pull to impact certain things. So some of the complex questions that we help answer that can drive revenue are things like, my business has plateaued, what can I do about it? How can I increase my membership capture? How am I doing compared to the rest of the industry? So this is a big one we found. You might have a good understanding of, you know, how you're doing and, you know, your analytics, but how do you know how that compares to, you know, another wash in the same region or how that compares to what the industry is experiencing as a whole. So that's a unique perspective that we can offer there. And then things like, why are my members churning? And really diving in and understanding, you know, the behaviors of customers and how we can impact those things and influence, you know, how long they stay with us. Where should I build my next site? So this is a whole topic in itself. We're actually, Kirk and I, are going to be doing another webinar on Thursday of next week all around site selection. So if you are planning to open a site, whether it's an acquisition or a new build, anytime in the next, you know, year or so, this is a great thing to understand. So what we've done there is really understood what pieces of data are truly driving the most impact when it comes to revenue and member count at a site solely based on the location itself. So I believe we put that in the chat. So feel free to jump in there and sign up for that one as well.
And then finally, why we're all here, are my prices optimized? So this is where we're going to be spending our entire conversation today. There's so much to talk about when it comes to pricing. We're going to start with a lesson on economics of pricing, why pricing is so important and what's different about it in the car wash space and the car wash industry. Also, one thing I wanted to point out here is, the fact that you're here is amazing because a lot of owners don't even really know to be asking the question, are my prices optimized? It's often something that we find, we do first thing when it comes to a new car wash working with SUDS, is we look at pricing and optimize that right off the bat so that you know, every other tactic, marketing, advertising that we do is driving the most impact possible. So "Are my prices optimized?" is a question that everyone should be asking, because as you'll see, there's a lot of money to be made just by asking that question.
Kirk: Okay, so to kick off a discussion on pricing, unfortunately, we do have to go back to economics because that's what explains kind of the relationships behind why pricing works or doesn't work, and ultimately what's going on behind the scenes. So we're going to go back to the demand curve, if you remember from Econ 101. And the demand curve is what's going to model the relationship between price and quantity. So it's important to understand this relationship, it's an inverse relationship because it's going to help dictate how much a business is going to charge their customers. So on the y-axis here, on the left side, we have price, and then on the bottom or the x-axis, we have quantity. Now, what this shows us is that as prices go up, people are willing to spend or purchase less, their willingness to purchase goes down. Alternatively, when the price goes down, all of a sudden you have lots more people. So at that lower, you have high demand but low profit margin, and higher, you have high margin and low demand. And so what this teaches us is that not all customers are the same.
So the challenge here then is figuring out what price should I charge? And what we're ultimately trying to figure out is, what price is going to maximize my revenue? So looking at the next slide we have, here where you have a higher price. And the rectangle here that's blue is going to be your revenue. So price times the quantity of people who will purchase at that price is going to be your revenue. Alternatively, if we were to charge a low price point, then that revenue cube is going to shift, and we're going to get a different structure of revenue and who's buying and how much are they spending. So in order to optimize pricing, what you ultimately have to find is that happy middle ground between that price and quantity relationship that is ultimately going to maximize how much revenue you're going to make given the price and quantity combination.
So once you found that, even though you're maximizing your revenue, given that single price, there are still revenue left on the table. If we look at the graph, there are triangles, one above the cube and one to the right. And those represent dollars that customers are willing to pay or alternatively, products or quantity that they're willing to buy. However, given that we are charging that one optimal price, we're giving up that potential revenue. So the challenge now is to figure out how can I capture some of that revenue? And the solution would be to charge multiple prices. So this would what would be called price differentiation, where you're trying to figure out how can I capture those who are willing to pay more while also gaining revenue or profit from those who are willing to pay less, but not making a war and not making people upset at us for charging multiple prices. Because people do tend to want to pay less for their products. And so what we have to do is we have to figure out a way to get people to identify whether they are willing to pay more or less and get them to pay that. They won't do it voluntarily. So what happens, one of the best ways to do this is to bundle products at different levels of service. Those people are willing to pay more. We'll see that higher price, it's higher value, they will have a reason to pay more. Alternatively, those people who are not willing to pay as much, they will see a low price that's in their ballpark. And then you have, depending on how many prices you have, you have different opportunities for people based on their willingness to spend. Now, you see here that using this structure, the revenue was able to go up. That being the case, you still have the challenge of finding the optimized combination of those price offerings, because one of the things you still see in this graph is there are still triangles of consumer surplus, as it's called, that is not being captured. And the reality is you cannot capture all of that because you would have to have an infinite number of prices and you would have to have people willing to charge all of them. So that's not going to happen. But what you do need to do is figure out how you can capture those to the best of your ability.
So that's one of the trends that we've seen in the car wash industry. Typically, we will see operators that have multiple price points. So what tends to happen though in the car wash industry is, obviously, there's not a demand curve that's sitting there waiting for you and your template that you get online, and you don't really have data for telling you what you should do. And it's not the case where you can get on Amazon and figure out what everyone else is charging for a car wash and make an adoption to that. So the default tends to be, I'm going to look at the customer or the competitors that are already there, and I am going to copy them or adapt their price offerings in some way, in order to have mine. Now it's a little ironic because you're moving into a market, convinced that you're going to beat out that competitor. You certainly wouldn't copy their title, copy their wash names, copy their location, but you're relying on them to tell you what prices you should charge in your business. But the reality is, there's not a whole lot else you can do, so it's a fine place to start. So there's a little bit of guesswork. Now the reality is they probably did the same thing when they moved into town. They copied someone else, and they probably copied someone else. So who's really making the decision of what those prices are and do they actually work, and are we wrong? The reality is, we're probably wrong, but we do feel safe being wrong as long as everyone else is wrong. And so that's why anytime we talk to people and they're considering making a shift, they get a little nervous because if they're doing something different from everybody else, that's when you get a little, yeah, you start to sweat a little bit.
So as we have worked with different clients, there are some things that we've seen along with this. It's kind of some trends in the industry. We'll call these some pricing fallacies, we'll share a few of them. And these are what both we hear as we work with customers, but we also see, as we dig into the data, of what often are some preconceived notions about how operators should price. So the first one we'll look at is, my competitor has a $60 wash option, so they're making more money than me. Now, this one plays off of FOMO called the Fear of Missing Out. We see what someone else is doing and we automatically assume, well, they must be selling that. What we can tell you, as we have worked with customers, as we've looked at their data, is often those big price points aren't selling much. So yes, they're on there and they make that operator feel good, but they tend to not be making them money. So just because you see something on someone else's menu does not mean that they're selling it. They may be, in some markets they may be, but the trend that we tend to see is they're not.
Another fallacy would be, my competitor has a $60 wash option, so everyone will think that they are a higher quality car wash. Now, there are some industries where this is the case, where brand plays a big role. Cars, for example, the auto industry. Tesla. You see a Tesla, you think quality, right? Even if it's not the higher end Tesla car, it's one of the lower end ones, which are still pretty pricey, you still see that Tesla brand and you think, oh, quality, prestige. But the reality is, the car wash industry isn't quite like that. Anytime you're making a purchase, you kind of make a decision on how much research, how much you need to know before you make that purchase. If I'm sitting down at a restaurant, I'm going to do a little bit of research into figuring out what I want to buy, but not as much as if I'm going to buy a car. With a car wash, the reality is, people aren't paying quite as much attention as you might think. And this is an important lesson, both here as well as just in lots of areas of the business is, you are not your customer, okay? Your customer does not focus on the same things that you do. They don't see the same things that you do and they don't think about the same things that you do. So you know what all your prices are, and you know what your competitor's prices are. The reality is your customers probably do not. But don't take my word for it. Ask a customer. If you have a customer out in your lot, go to them and give them a free wash, some free coupon, make conversations, say, hey, how do you think we are price relative to our customers? Do you think we're fair? Do you think we're overpriced? What did you purchase today? Love this car, what have you. But do some research on your own and get a feel for that. How much do they pay attention? The reality is, most don't. They come into the car wash, they look at the menu and they make a decision. So the point here is don't try to win by having the highest priced wash. Win with customer service.
Now, next fallacy. My equipment costs have increased, therefore I need to increase my prices. Maybe. That depends though. It depends on what your data is showing for your prices, because your customers don't care if your prices, if your equipment prices increase. They're not making their decision based on that. So the last thing you'd want is for your expenses to go up and/or your demand to go down, because then you'd be squeezed on both ends. So again, don't make a decision based on that. Make a decision based on what your data says about your customers.
Next fallacy, I should never lower my prices, only increase them. Sometimes, again, in general, you do want to see prices go up, inflation does go up. People are used to the idea that prices go up over time. That being said, it depends. It depends on where you're at and how customers are reacting to your product offering. So figure out what the data shows first. Sometimes you do need to go down in order to capture more revenue, going back to that demand curve.
Next fallacy, my competitor is raising their prices so I need to raise mine. Again, same idea here. What does the data show? Just because they're raising prices doesn't mean they're going to make more money. How are your customers reacting in your market to your product offering? Next, my competitor is a big brand so their prices must be right. So news flash for you. Big brands ask for help too. So we work with big brands and some of them are quite surprising. And you got to think about it for a second. Lots of these brands are growing through acquisition, meaning, they're purchasing sites of different brands with different prices, different levels of quality, different markets. So they're kind of all over the place in many cases and it's hard to really centralize and standardize an offering. So it gets a little bit tricky on their end. So if you're looking at a big brand wash that's down the street, and you're assuming that those prices are right for your market, those may be the prices that are right for the market in the state over and they're just matching. So not always a great rule to rely on.
Next, maximizing ticket average is what's most important. Partially true, all else equal, we do want to increase, on average, what customers are spending at your wash. But, if you remember from the first demand curve we saw when the price was really high, but the quantity was really low, that was not the optimal revenue place to be in. So it's important that you understand that yes, your ticket averages are good, and they're a good barometer of your profitability, but you can't look at them in isolation, because volume, your memberships, your retention, all of those are also aspects that affect revenue, that are affected by price. So it's one piece in the larger puzzle.
And then, those are all the fallacies we have today. We'll have to do more next time. Kayla will talk a little bit more about pricing.
Kayla: Yes, so I want to get into a few examples, a few stories that we have here on, you know, how pricing has impacted car wash owners that we have worked with.
So how do you know if you are leaving money on the table? It's a really good question. And if you've never used a strategic pricing tool, the answer is most likely yes, because there's not really another great way to do everything that Kirk was talking about and really understand, you know, the most optimal prices that you can have and how they relate to one another. There's so much that goes into it, so likely there's, you know, money to be made just by using an analysis like this. And most owners that we work with are shocked at how much money they're potentially leaving on the table. So when we run these, we can actually, you know, provide a projection of what the new price points would add to their revenue numbers and their ticket averages. And it's often, you know, very surprising.
So one example of that I want to share is a family-owned operation we are working with in the Southeastern area. So we were doing ongoing marketing programs with this car wash. One critical point to me is that they were doing well. And I think that's a pattern you'll notice through a couple of these examples is, it doesn't necessarily, you know, need to be that you're underperforming or that, you know, you're struggling with revenue or anything like that. Like a lot of times these car washes that have the biggest impacts were already doing pretty well, but they didn't know that they could be doing so much better. So that's kind of one key point. So this car wash in the Southeast was adding sites regularly. They had about a dozen by the time we ran this. They were doing ongoing marketing, they were growing their memberships over time, like things were generally good. And another piece that actually was part of the trigger for even knowing that they had optimization opportunity on their pricing, was they had a pretty significant piece of their customer base choosing their top wash regularly on the retail side. So they had about 36% of customers picking the top wash. So that would seem like a good thing. And so that's kind of a little bit counterintuitive, but if you think about it, if, you know, almost 40% of people are willing to pay, let's say it was $20, there's going to be some people that are willing to pay more and even more than that. And again, back to what Kirk was saying, it's not about just raising the prices, they're all in relation to one another so it's a lot more complicated to maximize the revenue than it is just, you know, oh, some people are going to be willing to pay more, bump them up. So that's one piece I want to make clear there.
So again, they were doing well, but we ended up running a PrecisionPricing analysis on this wash just to understand, you know, was there optimization opportunity here since there was a pretty good chunk on this top wash? What we found was nearly $2 in retail ticket average increase opportunity. So with PrecisionPricing, we predicted that we could add almost $2 to their retail ticket average. So again, across a dozen sites, that's a very large revenue opportunity. Let's look at the predictions here. So these are actual numbers from this case study. So part of what PrecisionPricing does is it predicts how customers are going to behave, which wash they're going to choose with the new wash prices. So in the gray, you're looking at the prediction, in the blue, you're looking at what actually happened after the price change went into effect. So in the gray, for example, we predicted 31% would choose the best wash with the new pricing. And then what actually happened, 33% chose the best wash with the actual or with the new pricing. So what you can see here is how close those percentages are to one another. And what that really means is, when we give you a prediction in terms of how much we think we can add to ticket average through this PrecisionPricing tool, it's very, very accurate, because as you can see, the percentage breakdowns, the predictions of what we think customers are actually going to do with the new prices are so accurate.
So in this case, we ended up adding a $1.72 in ticket average on the retail side. And that combined with effects that also happened on the membership side, this was $3.5 million in annualized revenue. So again, this was a wash that thought everything was going well, like they weren't necessarily struggling, but they were leaving this much money on the table in annualized revenue. So these are really big gains to be had just by running an analysis like this.
Okay, so how does PrecisionPricing, we've kind of thrown that term around a lot. So basically what that, how it works, what it is, is it's a tool that's going to help optimize for revenue like Kirk was talking about. So it takes into account, you know, what your customers are currently choosing, what your prices are currently, and it's really going to optimize for the ticket average as well as maximizing how many people are purchasing. So both of those things together are going to come up with a combined pricing strategy that's going to maximize revenue on both the retail side and the membership side. So part of what PrecisionPricing leverages is more of like a mathematical analysis, taking into account statistics, economics, math, all of those things and, you know, running a model. And so that's kind of the math behind it. And then there's also an important component that really makes this different and makes it even more accurate is it also has a behavioral economics or a consumer behavior element added into the prediction. So what that means is we're also taking into account, you know, not just what the math is telling us is the right answer, but how are real human beings going to react to this pricing model. So we know, all of you know that human beings don't behave rationally when it comes to making purchases so it's really important to add in these elements. So it's not just what the math is telling us to do, but it's also, you know, factoring in real human beings and how they're going to respond. So think of things like how many wash packages there are? That's something that's going to influence behavior. What's the distance to jump to the next package? That's heavily going to influence behavior. What's the distance to go from a single wash to a membership? Things like that. And then we won't get too much into this in this conversation, but another thing we found that really influences consumer behavior as well is the design. So the, you know, the colors, the fonts, the layout, how, you know, the real estate of the sign, how big a certain wash package is, all of these things are going to help, even maximize your return even more. So again, that's a little bit separate from PrecisionPricing, but we recommend doing those things in tandem to really maximize how you can influence the consumer behavior piece. So with both of those things, kind of the quantitative with the modeling side, the qualitative with understanding how real humans are going to react to these prices, we combine both of those and that's what PrecisionPricing is. And so it's going to come up with a recommended pricing model with prices for your retail packages and your membership packages that are going to maximize revenue and it's going to be very, very accurate. And so part of the accuracy piece is, because at SUDS we do work with wash owners all the time that are going through price changes, we understand this is not necessarily an easy process. There can be a lot that goes into a price change from menu changes to training your team members to answering customer questions. It's not necessarily easy. So if you can go into a price change knowing how much revenue you're looking at adding, it makes this whole process a lot more worthwhile and less stressful.
Okay, one more example I'll share here. This was another car wash owner, car wash operation who was doing well overall. They had nine sites and they were kind of just, again, solid brand, great operation, doing the right things, but they had a feeling that there might be optimization opportunity. One indicator is, you know, 62% sitting on one wash package of their membership. So we'll talk about that in a minute as a sign that it could be time for a pricing optimization if you've got a larger percentage in any package. So again, they were doing generally well but interested to see if they could maximize, if they could optimize their revenue through pricing. And what ended up happening, so they had 17.09 on the retail side, 30.72 on the membership side. Here's kind of the after effects from the price change. So we ended up increasing their retail ticket average to 18.33, which was over 7% bump, and then 33.86 on the membership side, so over 10% increase there. This is great in terms of revenue. Let's look at the impacts of that. So this was a $2.3 million annualized revenue.
Just something to point out here like, these are, the first one was a 12-site operator, this is a nine site. So keep in mind it's going to vary dependent on the size of your operation, but all in all these are major gains to be had so definitely something to consider looking at, because this is, again, this is revenue without having to add a single extra vehicle. So you're not trying to necessarily go out and drive new traffic, this is revenue just from optimization.
Okay, so what's included in PrecisionPricing? It's basically we start with a breakdown of what's currently going on, and so that's going to be, you know, your current distributions, your current ticket averages. You may or may not be super familiar with that already, but it's going to feed into the model as well as provide a before and after. So that kind of gives us the baseline. Then we look at new pricing recommendations for retail and membership. So that's really what you're going for here. That's kind of the bread and butter of this analysis is you're going to get what is my optimized price points for retail and membership, how many packages is it, what are the exact prices? And then with that, we also predict how people are going to behave. So the distributions, those are those percentages we were looking at. And then, you know, with that prediction of how people are going to behave, how is that going to increase your ticket? So we predict, you know, a dollar amount in terms of ticket average increase on both retail and membership. And then finally, how is that going to impact your revenue?
Okay, last topic here before we save some time here for questions is, when is it time for a price change? So this might sound intriguing, but it's hard to know, you know, when's the right time to make a move on this and are there any indicators you can be looking at to know, yes, I am leaving money on the table? So one, I kind of hit on this a little bit earlier, but one thing to point out is when SUDS starts working with a car wash in essentially any capacity, we do pricing first. So no matter what time of year it is, you know, what they've currently got going on, seasonality, any of it, we do pricing. And really, the point of that is we want to get everything cleaned up and optimized, kind of in-house if you will, before we start marketing and advertising and really paying money to bring people in. So if you optimize what you've already got going on, then it's going to make so much more sense to, you know, pay money to go market and advertise and drive new traffic in 'cause you're making more per wash. So now's a great time.
And then, a few other things you can look at that are sort of indicators. If you haven't done a price change or really looked at your pricing analytically, in about two years, that's a good timeframe. So we generally advise car wash owners to look at pricing about every couple of years. It's not something that's ever necessarily going to be done. You're going to be, you know, prices obviously change all the time. It's going to be a little bit more fluid than that, and often, every couple years, there's going to be an additional optimization play as your consumer behavior, you know, shifts around and as things change. So even if you've done something like this or you felt comfortable about your pricing a couple years ago, it's a great thing to look at this on a more ongoing basis.
Another indicator to look for is, like I pointed out on that previous slide where there was a larger percentage of customers purchasing a certain wash package. So like I already mentioned, if that's happening, there's, you know, statistics will tell us that there's a percentage of those people that are going to be willing to pay kind of, you know, that next price up or whatever that is. But remember, it's more complex than just increasing prices across the board, because all of the prices relate to one another and it's really that relationship between the prices that's going to impact behavior, so keep that in mind. But that's something to look out for, is if you see a lot of people hanging out in a certain wash package, there's likely optimization room there, so it's a good time to look at a pricing strategy.
And then last indicator or last kind of timeframe that might make sense is, if you are considering increasing your prices. So like Kirk already mentioned, this is a very common thing that we hear, and it's kind of common across the board, people wanting to raise prices in order to either, you know, keep up with a competitor or to make more money, run a pricing strategy or a pricing analysis before going through with something like a price increase, because there's often a lot more revenue to be made through, you know, manipulating the prices and optimizing them rather than just increasing. So if you're already considering increasing prices and, you know, the signage changes and the things that go into a price change anyway, that's a perfect time to use an analysis like this.
Okay, so hopefully, we demystified the pricing conversation a little bit and gave some useful information on when a price analysis might be beneficial. So we're going to go ahead and get into some questions. Let's see. Okay, we've got one question here. Is this a standalone service that SUDS offers or is this just for customers already using SUDS? Yes. It is a standalone. So that's what's great. It's included in our ongoing marketing services as well. We include this, kind of what I was mentioning before, where you want to get your pricing nailed down right off the bat before you do ongoing marketing just so it's more effective and more cost effective. But we actually do offer PrecisionPricing as a standalone.
And I see people asking about the cost. The cost is going to vary depending on how many locations you have. It's going to depend on how many different regions you're in as well. If you've got, you know, four or five different price points in various regions, that's going to be a different situation than someone who has five sites, and they're all the exact same pricing. So we can connect with you more in detail about that in terms of what it would be for your situation.
Other questions? What is the timeline for this project? So timeline's going to be about, we say two to four weeks. So it's going to be about, again, kind of going back to what I said a moment ago about complexity, it's going to depend on how many price points you've got. Are we looking at doing regional price strategies or is it a little bit more of a simpler, you know, one pricing strategy across the board? So if it's a little bit more complicated, it might be closer to the four-week scenario, but anywhere in that timeframe, two to four weeks. And I feel like there was a related question, what's required of us on our end to do the analysis? So really, all we need from you on the wash owner side is login credentials to your point-of-sale. So we essentially connect that way to get your data and pull the data out of your POS. So that's really the only thing we need to get started. Is that correct, Kirk? Anything to add on that?
Kirk: Nope, that is correct.
Kayla: Cool. Let's see here. Kirk, this one's for you, I would say. What would you say is the success rate of PrecisionPricing? Does it ever not work?
Kirk: Ah, that's the most important question. Let me give you two answers to that first of all. And the question comes down to how would you measure success? So Kayla mentioned that we, as we look at the data, and we try to figure out how different changes to your pricing structure will affect the outcome. So we do predict that outcome. And so there is a projection that's involved in there. Now, for all of you who have tried to create projections in the car wash industry, you're going to empathize here because you know that's a very tough thing to do. So we will create projections, sometimes we're higher, sometimes we're lower, sometimes we're pretty close from a decimal standpoint to what we are predicting. And actually, from a data standpoint, we tend to be a little bit more excited when we're closer than when it's more, but that's just how we are. So yeah, in terms of nailing down and hitting that projection exactly, sometimes, usually no.
But on the other hand, what the real goal here is to increase the revenue. I mean that's really what we're trying to do is we're trying to figure out ways that we can find, given what we're finding in your data where there is still potential, there's money being left on the table. Now, in terms of that and in terms of making the customers more money, that one we've hit 100% of the time. I have yet to have a customer say that they regret making the changes that we recommended for them. Now, that is a little bit of a bold claim. But the reality is, it's because we're not guessing here. If you have a new site and we're giving you a pricing recommendation, there is a little bit of guessing there. We are doing it based on experience, but the reality is, in those cases where we don't have data to work with and it's not telling us anything, those are a little bit trickier. But in most cases where you have a site, it's been operating, and we're getting a little bit more understanding as far as how the customer is responding to the options you have available. In that case, we're able to look at that and we're able to make data-backed recommendations. So as a result, we aren't extremely aggressive for the most part. We'll look at it and we'll make recommendations. We may see a lot of potential, but we're not going to make a big jump. We'll recommend gradual changes over time because the reality is, while we are confident in the recommendations and our understanding of the ability to influence the market and the consumer behavior, it's still business and it's still money and it's still a lot of money. So we do tend to make recommendations that we are quite sure of. And really, what it comes down to this, we've been doing it for a while, we've been digging into this data for a while, and we've been seeing some consistencies, some inconsistencies with how customers behave. So in terms of the real question which is making more money, we've hit that a hundred percent of the time. Might not always be the case, but as of today, it is the case.
Kayla: Yep. Couple more here. I have a one-site operation, how cost-effective is using SUDS? Good question. So, I would say PrecisionPricing is probably one of the most, I don't know if cost-effective is the right word, but it's going to be the most impactful on your revenue. So average return on the retail side is about two bucks. Average return on the member side is about three. It does depend. That's a rough average, but you could kind of look at what that, you know, what that revenue number might be on your single wash. We also have a marketing program that's specific for single-site operators that's a little bit more low-cost solution. So there's definitely things that you can do for a single site.
Kirk, I think this would be a you question, is churn a big variable that you look at determining pricing? So basically, do we figure in churn at all when we're doing our pricing analyses or is that separate?
Kirk: We take it into account, because that's one of the things, not directly in terms of pricing. Now, with customers who are also, when we're incorporating different promotions than we would, specifically with recommendations, if we do see that you have really high churn, that could tell us that your prices are too high and we will be able to see that. In general, we're more looking at the decision-making behavior of those customers, as long as it aligns with something that is a pricing structure that is going to be more in line with having better retention. So you can certainly, you can certainly charge lower prices and get lots of people signing up if you're constantly running promotions or whatnot. But that's not something that we would recommend because we do see that there are times when some pricing structures do seem to have that unintended consequence. So indirectly, yes.
Kayla: And do you have any, off the top of your head, healthy churn rates? But I know it obviously depends on what promotion you're running, lots of different variables, but anything that you and your team look for as a red flag in terms of an unhealthy churn rate?
Kirk: Yeah, and you also have, you always have to investigate churn because there are different reasons for churn. But in general, anytime you're upwards of five to 7% and getting above there, you do have to start in asking questions. And when we do see that, that is where we have to take that into account when we're figuring out what a pricing, how to tweak the prices. So that's a variable that we do have to take into account.
Kayla: Yep, and churn is an indicator that it's not necessarily prices that are the issue. It could be, but it could be a number of other things as well.
Let's see. Do we book a consultation through DRB or SUDS? Great question. I will, actually, let me, I'll put this up now. So if anyone needs to drop off, you can grab that QR code or grab the contact information. So scan that QR code or visit DRB to book the consultation. You can do either, but it's just easier to go through DRB for that.
Kirk, another similar question to the last one. Does PrecisionPricing consider competitor pricing or is it really more specific to what's going on for that individual operator?
Kirk: It does. Now, we don't have your competitor's data first of all. And so that's why I alluded to that earlier with the different fallacies there. That being the case, it would not be in our best interest to not take that into account, because number one, that is what someone thinks is good pricing and that is what customers maybe used to within that area. But really, so we'll take that into account, but really what it will come down to is how are consumers reacting to your prices? Because the reality is you are not your competitor, you have different customer service, you have a different location, you have different branding, so it's not as simple as saying they're doing this, therefore that's what's right because you're not the same. So that's where we would look at, we would take it into account to figure out what else is going on in the market. But what we're really interested in is how are your customers responding to your offering and where do we think there is potential for improvement?
Kayla: Yeah, and I think jumping off of that, like because this is an optimization play and not a traffic driving play, traffic driving is a separate consideration, so if we're thinking of this as optimization, we're kind of assuming, you know, your customer has already made the decision to purchase from you. They're looking at your menu, they're looking at your pay station screen, and we're trying to influence what they are choosing amongst the offerings that you have. So that might be another way to think about. It's really more about what's going on with you and your own prices.
We have a lot of really good questions. We'll answer like a couple more and then let you guys get on with your day. But we will follow up afterward with an email.
Our site has been operating for two and a half years. Is that enough data? Kirk, I would say yes.
Kirk: That is enough data, yes. Yeah, we'll look at that and we'll figure out what are the trends there that are happening and where they're currently, how it's currently functioning there.
Kayla: Perfect. Let's see. Is PrecisionPricing recommendation contingent on specific advertising or signage campaigns or upgrades? I'll take that one. So the recommendation is not. I guess backing up, the prediction is not going to be contingent on that necessarily, but we always recommend that you launch your new prices with an updated menu. So since you're going to be updating menus anyway, it's a great time to do a strategic menu design as well. So if you want to maximize the return, you're going to get out of your price change. It's a great time to do your menu design and pay station screen design as well. We've found around like 12%, 13% increase in ticket average. Even if we take pricing out of the equation for a moment and literally just were to do a menu design using, you know, we have a whole team that specializes in strategic design and influencing behavior through the design elements, you can increase ticket average alone, with just design, like 12%. So if you combine the two, you're going to get even better returns. I think I answered most of that question. Specific advertising, no, it's going to be more about training your team on-site about how to answer any questions. Really, we don't get as much customer pushback on things like this as you might be concerned about. Kind of back to what Kirk was saying earlier, a lot of customers don't even know what prices they're paying. Maybe some of them on the bottom package do. So it's going to be more about training, you know, the messaging on how you're going to respond, how the team is going to respond. But for a price change, we generally don't advertise it at all. It's a lot more organic than that. So really, we would just do, you know, make sure you're doing your training and messaging, get all of that clear on-site and then try to do the strategic design element alongside.
All right, Kirk, any last ones you want to answer before we call it?
Kirk: One of the things, we got a question, and this is a common concern, how can I raise my prices without making my customers angry? And that's definitely a natural concern. We always get a little worried about those customers. What we tend to find, and I alluded to this a little earlier, is most customers don't spend that much time thinking about those prices. You do, and you tend to think that they do as much as you do, but again, you are not your customer. So what tends to happen is, we may get a couple of customers who get angry over something, maybe it's the prices, maybe it's something else. And we tend to over exaggerate customers getting angry, when in reality it's less than a handful sometimes. So it's important to recognize that because we do have that bias in ourselves to really emphasize the angry customers. Now, you're running a business, you are going to have some upset customers, be it pricing increases, be it something else, but the reality is, you're not going to lose them for that reason. And you may lose some, a couple, but it's going to be a much smaller impact than you would think. But again, do some research. Ask your neighbor, ask them how much they pay for a car wash, and ask them how much this car wash is versus your car wash and see what they say. It might be interesting to see, kind of get that insight from, again, a kind of a non-biased third party.
Let me see, I saw a couple others. One of the things that we get questions about quite a bit is how much customization should we have for our car washes? How much options should we give our customers? Now, that's one, we tend to think that people want more options. And by giving people more options they'll be happier. The reality is, in some cases, yeah, that's true, but you got to recognize someone who's coming to a car wash, they're not there to figure out what wash, how they're going to wash their car. They don't know what the triple pink foam conditioner does and whether they want it on their car. The reality is, when someone's sitting in the front of that menu, their mind is already at the thing that they're going to do after the car wash. They want to get in and out as quickly as possible. And Kayla talked a little bit about menu, and that plays a big impact there. The idea here is make it as easy as possible for them. Don't give them no options, don't give them one wash, but don't give them 10 washes to choose from. Give them some basics. Show the difference between them so that they are able to see the gradation of value from one to the other 'cause that's important, but the reality is, they want to get in and out as quickly as possible. People don't wake up in the morning and say, okay, today's the day to wash my car, that's what I'm doing. It's something that people do in between two other things. So help them make their decision for them.
Kayla: Yeah, and Kirk, real quick while you're on that, there was a question about, is the PrecisionPricing recommendation going to basically take into consideration, you know, how many washes, is it going to sometimes remove washes or add washes to the mix? And I think you kind of touched on that, so maybe you can take that one real quick.
Kirk: Definitely. And there's no one size fits all here because again, we're looking at the data, trying to figure out where customers are voting with their wallets effectively. And it's hard to predict one customer, but when you have lots of data, you can start to see patterns and see where there are some bottlenecks and whatnot. So it may be, in some cases, we recommend adding washes. In some cases, we recommend take them away, it just depends. So there's no magic number, there's no magic answer to that question that is a one size fits all.
Kayla: Perfect. Okay, I think we're about out of time. So I saw a couple questions on pricing. If you're interested in what the pricing would be for your site and your situation, scan that QR code and we can connect with you on what that would look like for you. And then also questions about access to the recording. We will be following up with an email answering the remaining questions as well as providing a recording of the webinar so you can review this later. And I think with that, we'll go ahead and let you get back to your day. This was really awesome engagement. Great questions. Thank you all so much for attending. Thanks for your time today and we will be in touch. We'll talk to you guys later.