DataBytes 14 - April 2021

April 14, 2021

Jason Baumgartner and Chris Moriarity from Suds Creative are back with the latest edition of DataBytes! In addition to the usual analysis of recent car wash volumes, they share data on whether men or women are more likely to buy a plan, who spends more and who has the longest tenure.

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Jason Baumgartner, Suds Creative President: Hello, there. Welcome to DataBytes, April 2021. I’m Jason Baumgartner, President and Co-Founder of Suds Creative. And with me as always, Professor Moriarity, Chris, how are you?

Chris Moriarity, Suds Creative VP of Consumer Strategy: I’m doing well. Coming to you remote this time from not so sunny California, but data stops for no one.

Jason: Data stops for no one. All right, let’s take a look at the agenda. First, we’re going to do something that we’re calling a Car Wash Gender Reveal. Chris has got some really interesting information that we’ve gathered from a handful of clients and brands. And we’re going to talk to you about what gender looks like at the car wash, and what kind of information we can derive from that. After that, we’ll talk about our usual. We’ll go into some trends, national trends. We’ll talk about some states that are performing well, struggling in some more interesting cases, and Chris and I will walk you through those slides. So, let’s jump to it. Chris, I’ll hand it over to you.

– Yeah, and when you think about it from just a 30,000-foot view, really how important is it to know the gender of the people that are coming in? Does it make a big difference? Well, to us obviously, it’s going to, because when it comes to the tonality, the words you use, the messaging you use, we want to be as accurate as possible to attract more and more of your patrons and be as authentic as possible. But the problem was simple. It’s that we have a hard enough time collecting email addresses, let alone having people fill out surveys and gather demographic information. As simple as gender sounds, it was so difficult to get. So what we wound up doing was we actually took the names of the individuals, and ran it through a little baby algorithm that bounces it against this great big database, and you get a probability. So it says, okay, the name Tim has a 99% probability that it’s a guy. And if it’s Chris, well gosh, now it depends. So it’s sophisticated enough to the point where we started figuring out that we could get gender assigned to not only members but prospects if they have names, and the more we dug into it, the more we found. And all of a sudden, all of this actionable intelligence just started presenting itself. And I started running around the office of course and found Jason right away. And I was like, look at these things, look at these things. And it’s such an exciting time. But if you think about it in a different context, like Jason, you’re a basketball fan, right?

Jason: Mm-hmm.

Chris: Unquestionably, did you watch any of the tournament?

Jason: I sure did.

Chris: As most did. Now, imagine if you were about to go into this tournament but you had no idea who you were going to be playing against and you didn’t find out until the game was about to start, and they said, and by the way, the game is going to last eight seconds and you have to win or lose. I imagine most coaches would not enjoy that scenario.

Jason: It’s not preferable.

Chris: One would say, but that’s basically what happens is this car pulls up, we’ve got a split second to make a determination. If we’re going to go left or right. When they open up their email inbox, if they’re going to read it, not read it. And every month when they have to decide if they’re going to recharge that membership or not, there’s just all of these choices. And what we’ve found and what we’re going to get into is gender plays a significant role at every site. But also what’s interesting about this is the differences that they play. And so if we want to kind of go through these slides a little bit here. So, this is kind of just a glimpse at what we were talking about in terms of how we get that data. There are a lot of privacy concerns. There are some technologies out there that can use cameras and assign gender. And we’ve had some folks that were concerned that that’s what we were doing. And that is in fact not what we’re doing. We’re just using a probability-based naming algorithm.

Jason: And we’re getting the name from, you can get the name from a credit card transaction itself?

– Exactly. So anywhere that name hits the database that we can get access to, we can run this process. And now that we’ve got it refined, it’s another arrow in the quiver that’ll be able to help just about you know, anybody anywhere.

Jason: So, let’s take a look at some of the numbers.

Chris: Yeah. So, try to make this as simple as possible. And one of the things that when we did have access to the data, what we’ve been saying for a few years now is it’s about a 60, 40 split in terms of looking at members, how many members are males versus females. And as we looked at each and more and more sites and more and more brands, this has held remarkably consistent. It can get a little wider in certain circumstances, but by and large, this indeed tends to be the exact ratio. So then the next questions you ask, like okay, what are the other advantages? So where we have LTV here, that’s lifetime value. So this would simply be, what is the average recharge value of whichever package they’re on, multiplied by how many months they tend to be a member. So of course, we’re looking to see if there is a difference in loyalty. And for the first site here for brand number one, there was really not a whole lot of difference in lifetime value. In fact, it was only off by a few cents, but what made this more interesting is that when you looked closer the gentlemen did tend to enroll at a high rate. They did tend to pick higher-priced packages but on the other side of the world, the female members tended to stay longer. So their tenure, in this case, was actually 10% longer, a full month recharge longer. So it offset. So their lifetime value was kind of flat. And so at first we were, we were like, oh man like you find all these differences but ultimately they wound up in this same spot. We’re like, oh well, maybe it’s not as meaningful as we’d hoped. So then if we go to brand two, this is where we started to see things stack up a little bit more consistently. So now all of a sudden, higher enrollment, higher lifetime value, longer memberships, higher-priced packages and the females were distinctly churning out at a higher rate. So, to give you a little bit of a scope since it might not have gotten picked up off the first slide there, we’re looking at 40,000 members, sort of in this sample size. So we didn’t want to dabble because of that volatility. We’re looking for big sweeping trends and insights here. And the more we looked, the more dramatic the insights got. So if you go to brand number three, this is where it was an extension of brand three here, where again, everything right there, 62, 38 lifetime value, tenure. It’s almost mirroring what we saw in the first one. Now, when we think about this breakdown you have to not just look at the distributions of the membership but look at the dollars attached to it because that lifetime value is certainly telling, but then you have to multiply that times how many male and female members there are within that package. And if you go to this next slide, this about knocked me off my chair, where for one of these brands, the male population, on their top package, represented 49% of their overall recharge revenue. I’m going to say that again. 50% of all of the revenue being generated by recharges is coming from one package and exclusively the males. Earth shattering.

Jason: It’s crazy.

Chris: Because all of a sudden, the implications of this are… When we put this, when we put this up on the screen, Jason, what was your first reaction?

Jason: Well, I think you’d have to back up to, you know, a year and a half ago or two years ago when we put out that first survey of a hundred thousand members and it validated what we had thought, was that, well we asked the question, is it really predominantly female members? Because that’s what we were told when we came into the industry, and we didn’t have a great answer. Nothing other than anecdotal information from other operators. But we were literally at the point where we were targeting, you know, television commercials towards moms with kids because that’s what everybody wanted to target. And we did that survey, it came back 60, 40 male and 62, 38, I believe. And it was at that point, you know, we announced that at Data2Dollars, where we were like, okay, well, we’ve got to rethink this whole thing. And you’ve done some other things with cluster analysis and, you know, showing the value of a male unlimited member. So this, to me, this just further validated this. And now we have a huge subset that we can, we can target people, not just broadly and say that it’s predominantly male, but we can say, you know, we do have one brand, you know, somewhere in the Midwest that was even male and female, and it didn’t really matter in terms of the distribution because lifetime value was equal. So that to me was kind of a wet blanket. And I said, well, let’s keep looking. Let’s keep looking. And when you came back with this, you know, we still have more work to do here but it’s very, very interesting.

Chris: Oh, unquestionably. And that site specifically. Because I was with, I was exactly where you were, where it’s like, oh man, so basically you can go left or you can go right. Ultimately, you’re going to hit the same finish line. But when I started thinking about like, okay, if this is my information, what else is this telling me? This sort of second, third level of facts. Well, in that specific example, we saw that the female component had this extended tenure, this propensity towards loyalty. So if that’s a group’s natural trajectory, well now we can parse out that group and treat them differently. And all of a sudden you start hitting them with messaging that’s all around, community, loyalty, thanking them. Because if they’re already 10% higher, how much more of a nudge would it take to make them 15%, 20% and just capitalizing off of the direction that they seem to be going while trying to hedge against the volatility of the males. And all of a sudden you can kind of foster this element and environment of continual testing and refinement. And now you’ve got some benchmarks to work against. And that’s where I think this is going to be really interesting.

Jason: And so stay tuned. So, come see us at Data2Dollars down in Nashville in August. We’ll be talking about some things that you can do tactically. Message differential, the way that the pay station screens potentially could flow, how you communicate with people post-sale, those types of things. We’re excited to dig more into this data and show you what you should be doing with it.

Chris: Oh, amen. And just imagine the world in which, gosh, do I need to offer this person a discount or would they have signed up anyway? Or the opposite side, if I had just offered that person a slightly bigger discount, would they have gone for it? I don’t want to give that to everybody but this is where that nuance becomes so meaningful but we’re going to get into it in a big way.

Jason: Really interesting stuff. And, thanks for bringing that up to us, Chris. All right. Let’s take a look at the week-over-week data. In the orange here, what we’ve done is we’ve actually separated out 2020 because it, right towards the middle of March, obviously with COVID last year, this is where things started to nose dive. 2019 is a better indicator of what is more typical. And you can see it’s following a very similar curve. In the orange, you’ll see, 2021, 2019 in yellow. I want to pay attention specifically to weeks, 10, 11 and 12 before I leave this screen. Up 7.7% year over year, well, 2021 compared to 2019. So, still doing, performing really well but it did take a little bit of a dip here in weeks, 10, 11 and 12. And we’ll talk about that. You’ll see this kind of as a recurrent theme. Some weather events and some things that happened precipitation-wise when we get into the state level. But Chris, what jumps out at you when you look at these, week 14 data?

Chris: Well, the thing that I’ve always loved about this segment, is what we’ve tried to reinforce is, things move together. States move together. Patterns emerge the closer that you look. And when everything starts to slide predictably, the whole game is to try to find corresponding events to interpret and ultimately predict how far down is it going to go or how far up could it potentially go? And as you kind of get into this next part, everything’s got an explanation, but we can’t control some of the things that influence the sites, but we can control our reaction to those events. And after our last DataBytes episode, we got a wonderful response from a whole lot of folks saying, “How do I put some of these trends more into play? More into my strategy and have some triggers to pull out to these events that I can’t control.” So as you get into this next segment, I’ll kind of pepper in some of those examples, because it’s becoming more and more important to do so.

Jason: Absolutely. So, we’ll take a look at some of the states that have been performing really, really well. California, Florida has performed phenomenally compared to 2019. Just look at that difference. California took a dip in week 10 but since then it’s been performing really well, and really from, from week five, four or five on, has looked really, really strong, and we’ll get into the reason, one of the reasons why in just a second. But a lot of these, California and Oregon, share similar type of weather patterns. And we’ll take a look at the precipitation map, and you can see, look at, just look at Oregon. I mean, it doesn’t even follow at all. It’s like it’s a completely different state. Super dry. Super dry from January through the end of March in the state of Oregon. So definitely more wash opportunities. And it’s interesting as we look at a state like Minnesota, how sometimes the opposite can be affected. Warmer temperatures, more precipitation, at warmer temperatures instead of snow, can really affect a state’s weekly averages. Anything on the state level, Chris, here that you see that it jumps off the page at you? Besides Florida looking at, just look at this, crazy.

Chris: Florida’s always been that little weirdo that just nothing seems to be able to stop. Like they just keep on trudging forward and trudging higher. And that, and that’s fantastic. But when you think about Oregon, and as we kind of talk to more and more sites that are there. I think part of this too, is you have to look at the sort of emerging marketplace where we’re seeing a lot more investment into newer, modern, high-tech tunnels that I think again, the rising tide floats all boats. As people start seeing more wash activity, that leads to more wash activity. And so you’re seeing it spin up, where there’s always been car washes down there, but they’ve been obviously slower than Florida or California or other states to really see some of the investment the other states have seen. And I’m almost wondering how it’s just becoming more and more commonplace within those local economies, which would explain why they’re hitting those little dips and those new lows, like they’re just setting this new low-side threshold that, hopefully for all those owners, that’ll continue to go up and up and up.

Jason: Good points. When you look at this, this is precipitation for the month of March, and you’ll notice here, record driest would be in portions of the state that are dark brown, and the record wettest would be these darker portions of green, but significant amount of precipitation. In fact, March in Nebraska was the second wettest month that they’ve ever had on record since 1895. So, a significant amount of precipitation in Nebraska. But then you look at the states that were just, we were just looking at, North Carolina, Florida, California and Oregon, look at how dry Oregon was in the month of March, and the same thing with California. And then the white or these lighter areas are just near-average precipitation. But it is interesting how these storms can be in pockets and then kind of move. And there’s another one we’ll show you that’s year to date. So January through the month of March, that kind of further illustrates these things. And if I go back, if I can to just look at this slide. And we talked a little bit about that slingshot type effect. It’s really interesting how you see, you know, the low periods are offset in 2019 by high periods. Low periods in 2019 are offset by high periods in 2021. Low periods in 2021 offset by high periods in 2019. Is that normal? Or does that just, it just seems crazy to me that there would be that kind of consistency in the reciprocity.

Chris: There’s things that you can do to where again, this is where whether it’s, there’s different types of forecasting models that take into account. So even with those forecast lines that we’re looking at, it’s still averaging up. So when you back out, like if you imagine this as an issue of scale, where the closer we get to these numbers, the higher the peaks and valleys are going to look. If we were to back this way out the smoother it’s going to appear.

Jason: Right.

Chris: So, well we have to make sure that we don’t lose sight of with some of these sites that don’t have really robust weekly counts, is that, that volatility can seem extreme when really that deviation is maybe one or 2%. So, when we look at that anticipation, if you use something just like a simple, rolling average, am I going to be right more than I’m wrong, year over year? If I plan my years out with this cyclical nature per your state, will I be safe? And this is what we’re proving year over year is that you can predict your year a little bit better. You can stage specific events. And as it relates to those inverses where those things again are kind of inverting, this is where we talk about your sundown principles. Where all of a sudden there’s that freak snow storm. There’s that weird event that throws a wrench into your whole plans. Well, we’ve got to have a plan in our back pocket because it’s not impossible those things would happen. And we want to still take advantage of the time period, knowing it’s when people behaviorally are going to wash more. So, it’s nothing to fear. It’s just something to spend a little more time on and figure out kind of, what some of those contingencies might be.

Jason: Right. If anything, it makes you feel better when you have a dip that you can expect a slingshot type of effect is going to come your way because everything does level out. It’s like flipping a coin. You get four heads. It’s likely that, you know, things will even out when you get to 25 or 30 in statistical significance.

Chris: Exactly.

Jason: All right, let’s look at some of the states that have been struggling. So, so Washington, this was one, Washington and Minnesota too were very interesting to me. Washington is kind of in that similar pattern you would think with Oregon, California, but it really just has not seen the type of spike that it did in 2019. And it’s remained relatively flat, and looking at Minnesota has just really taken a dip. And as we were talking, as we were prepping for this, it’s almost like, you know, these are early spring months or early spring weeks or right before spring. And so you have the, it’s either going to be snow or rain. You’re going to have precipitation in that area. It’ll be snow or rain. And so this is obviously more indicative of rain, and we spend a good amount of time looking at the Noah database. And there’s a study that’s done, environmental study that is looking at temperature monitoring basically. And so the state of Minnesota did have 6.3 to 6.4 degree higher than normal March in terms of temperature. And so that could account, just a couple of degrees one way or the other means rain and not a lot of people showing up to your location or snow and you’re going to have a lot of people’s showing up to your location

Chris: With Washington, a lot of those states, what’s that do to the pollen? Does it delay it? Does it push it up? Because obviously that’s a huge impact, less so in Minnesota, But certain states, I mean, even though pollen’s yellow, it might as well be green because that’s how, that’s how it shows up at your business, I’ll tell you that.

Jason: That’s a great point. Yeah. That’s a great point about pollen and certainly could impact even with some of the wetter… Let me see if I have a slide here, go back. Even if in some of these areas here that are likely to get more pollen, that could be a significant impact. Absolutely. That’s a great point. All right, so let’s talk about Colorado. This is one I keep pulling up because it’s just, it just has never, it just hasn’t bounced back to what we had typically seen. I’ll flip to one slide real quick. Just to show you the amount of precipitation that is much above average from January through March. So not just the month of March but really since January, February and March, all the way through March, a significant uptick in the amount of precipitation that Colorado would typically get. The way that I look at this, Chris, and jump in here and tell me if I’m wrong, but we had, after the holidays, we had kind of an uptake in COVID. Colorado was one of those states that shut down a little bit. So they went back to restricting some things, this could account for this dip, but this, this is again, week 10, 11 and 12. It’s likely precipitation, but what jumps out at you when you look at Colorado?

Chris: Well, and this is something too, where, even in the last time that we chatted, every time they kind of try to poke their head above water, something pushes ’em back down. So now the question will be, if you get pushed down enough, I always use the grasshopper analogy to where if you grew up in the Midwest, if you ever caught a grasshopper and put it in a jar, put the lid on, it will go ping, ping, ping, ping, ping, ping ping for awhile. And it will stop. And at that point you can take the lid off and that grasshopper will literally stand there until it dies, uplifting story for everyone. So my question is right now, where we saw that rebound, after they got hammered with that last kind of late-season storm, is it truly suppressing the market or had there been so many events where it’s retraining the market? We’re no longer, I’m no longer in a pattern. I know there’s no longer something missing from what I usually do in my errands. Now, I don’t believe that’s happened. I believe that every time they poke their head in they kind of get knocked back down. But once they get past this rainy portion, we should see them rebound at least beyond the 2019 averages from everything else that we’re seeing. From the state level of economy that should indeed happen. And especially there’s, there’s still pockets of excellent things happening in Colorado. So I’m hopeful that once this weather lifts, they’re going to be where they want to be or at least closer to it.

Jason: I think we touched on this a little bit last time, but certainly we’ve seen in these, I would call Denver kind of a sprawling MSA, right? So it’s a very, very big to where you could live on the North end of Denver and you could commute potentially to the South end of Denver and your drive time could be 30, 40 minutes. But what happens when you take away commuter traffic and how that changes peoples ant trails and the way that they behave. Certainly LA, you know, the Bay area, you know, some of these sprawling MSA’s like Colorado have been hit pretty hard. So it’ll be interesting to keep an eye on not only what happens from a weather standpoint but what happens from a work standpoint. If people continue to go remote or if, or if they start bringing back people over time slowly and see if that has an impact on some of these sites that may have been set up to deal more with commuter traffic around, you know, regional shopping centers, that type of thing as opposed to local traffic. And we’re certainly seeing a difference on our end when we open new sites.

Chris: Well, and even for the last couple of days. I’ve been up almost as far as Santa Barbara down you know, Long Beach, like all over this LA area. And what Jason’s describing is something that, I keep asking the locals, like, is this how it’s been? Is this the change? Because, you’re seeing a lot more even traffic. Even right now, you can’t see this, but I’m in this sprawling shopping center. It’s the middle of the day. It’s certainly not lunchtime. And I’m seeing so much activity on the sites, that you just wouldn’t anticipate. The same thing was true yesterday. So, as we may not have the bottlenecks of years prior on those Fridays and Saturdays. How much of that has been now just pushed out against these other days. So wherever you are, you know, think about what those changes are because now the target’s moved. So we need to aim a little bit differently and take advantage of those people that are now running errands whenever they want. And it’s, it’s interesting. I mean, there’s a key for every lock I believe. So just don’t take your eye off the ball and don’t assume that they’re all going to come back to those old patterns. The game is continually changing and evolving. It’s a very fluid industry. So it’s worth taking that second look.

Jason: Absolutely. And you’re getting traffic and the opportunity exists then to, to try to reach out to people that aren’t driving by you, that you could rely on just street traffic to come into your wash. And I think that that plays a big part. Those people still exist in those MSA’s. You’re going to have to reach out to them a little bit differently and not just rely on penetration from street traffic. All right. Well, that’s it for April DataBytes. Chris, any last words for the audience?

Chris: I would just say, stay tuned. Gender was a huge checkbox. And again, as simple as that sounds. And so now we’re, we’re heading down the road of age. That sounds a little bit strange but the combination of those two data points makes predictive ability exponentially more. Oh gosh. I mean, think about this. All right. We were talking about earlier, that 49% of all that revenue, think about just that very, very lucrative band of people. Ones would naturally assume that the 20 year olds in that category are operating very differently than the 50 year olds in that category. So once, the further and further we’re able to dive into this, the more effective we are, the more efficient budgets become, penetration becomes, And we are finally getting to where we can take all this data that these sites are sitting on and just truly make it purposeful. So just stay tuned. Because I say this almost every week, we’ve learned something new, and it’s the greatest thing ever. And it’s always true. So, stick around for the next biggest thing ever.

Jason: Perfect. Thank you everybody. Thank you, Chris. Been fun.

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