 Hey, Tom Stewart here. Our guest today is Aja Holiday. This is Smart Business Moves. It's Wednesday again. Woohoo! It's Wednesday, halfway through the week. No kidding. I'm going to be off for the rest of the week, so it's Friday for some reason. Oh, that doesn't even count then. How are you, Aja? Yeah. What are we going to be talking about today? I'm talking a bit about what I'll be talking about at convention in a couple of weeks, but mostly about data-driven decisions and how to use what you've done in the past to get you where you are and to change things to take where you are to a better future, to a more profitable, successful future. Talking a lot about how to use data in a profitable way, and not just looking at numbers to look at numbers, which is something I have fallen into. Or not to look at numbers at all. Or not to look at numbers at all. Yeah. To have actions in place that you don't even need to use the numbers. It's like, how are things going? Good. It's like, yeah. And? How good are they going? Please measure that. Sales are up. Really? And, you know, it's hard for us, though, to speak in numbers. You know, it's, you know, the natural go-to move is to, I guess they would call it being subjective. Things are better, things are worse, things are good, things are bad, things are okay. It's really easy to fall into the idea of, like, using how you're feeling. You're, you know, what you're intuitively feeling about a situation to decide how that situation is actually going. Whether you feel good or bad about it and not really knowing, I guess, if it's actually going good or bad. You know, we're used to talking about our feelings more than our... We can't really measure our feelings, but we can measure business. Yeah. So, if you're going to be, you know, give me the number, and then if you want to editorialize on it, the number is blah, blah, blah, blah, blah, and that sucks. Okay. Or that's awesome. Okay. But, you know, throw the number in here. Speaking of numbers, you know what today was, Aujo? You know what happened today? No, it's my brother's birthday. Okay. Well, that's cool. You had a birthday party yesterday, didn't you? Yep. So, you know, the Federal Reserve, which is kind of the government entity that basically decides interest rates, you know, ended their meeting today and they announced another 75 basis points, which is like three quarters of a percent increase in their lending rate, which means all lending rates are going to go up and they're trying to slow the economy down to fight inflation. So, what does that mean to us? If any of us are borrowing money with a variable rate loan, your loan just went up again today. And they're telling us they're going to be raising it another 75 points, I guess, in six weeks and maybe another 50 points, six weeks after that, then they don't know. There's somebody who's buying a house in the next three weeks. Yeah, that's some stuff to definitely take into account on all sorts of choices that we're making right now. And, you know, for the last 30 plus years anyway, the smart money was when you're like getting a mortgage or whatever, get a variable rate mortgage, because interest rates are low, they're going to stay low and you'll pay less per month than what you have in the fixed rate. I think that's flipped now, though. Oh, yeah. But you're going to pay, you're going to pay, you're going to be more for that fixed rate mortgage than what you would in the past. You know, what you can hope for, and I've done this for a while and been through it, go ahead and if you have to pay more interest now, great. Right. Interest rates will eventually come down in a few years and when they do, you refinance it all. Exactly. I think somebody said to me, you know, date the rate, marry the house. Somebody said to me, I don't know if that's a common thing, but that has been stuck in my head. Never heard that, but yeah. Yeah, date the rate, marry the house. So I'm working on finding some rate dating right now. And if the house continues to appreciate and value when you refinance, you can like take money out of it and do other things with it. If you want to, there's all kinds of options there. Yeah. I'm looking forward to it. I mean, I'm terrified because this is a very different world than what I, you know, I bought my first house two years ago and it's very different in those two years. Yeah. It can be a, it can be a little stressful, but so, you know, it'll, it'll all work out and I guess I've found to believe that it's never as bad as you're afraid it's going to be. Sometimes, you know, it's, it's not as great as you hope it would be either, but you know, it's certainly within a range of, of acceptable. I can agree with that. I can get on board with that. So you've been developing your, your, your, your presentation for the ArcC ISSA convention, which is right around the corner, isn't it? Maybe three weeks? Two weeks, two and a half weeks. I mean, if it's your Friday, it's two weeks out. Okay. Well, fair enough. I'll have two weeks to get ready. I guess. And I do have some work to do. What's your, what's your presentation on? So yeah, working on data driven decisions, really, really trying to focus on the idea that, you know, you can really use the facts of where you've been to help with where you're going, I guess. And one of the things that I really liked that I've been kind of like really mulling over is the power of predictability and kind of the power of, you know, we talk about consistency, especially in cleaning services and what that does to us on our technician side, our employment side, on our client side, like the more consistency that we have, the better things are. And predictability leads into so much of consistency. Like when you can predict, you know, what you are doing, what you're currently doing and what that's going to do for your future and how that's actually going to, you know, by the choices that you're making today, what those results are going to be in the future. It makes this type of work a lot more enjoyable, a lot more. There's a couple of words you threw out there. One is consistency, you know, in a convention, I'm going to be giving a presentation on consistency. So help me out. I need to know what is consistency? You know, in cleaning service terms, you really talk about consistency of same technicians going to the same house at the same time, on the same day of the week, doing the same level of cleaning each time, the same quality of cleaning each time. And it can keep kind of diving down further into how much consistency you actually want. Or I mean, whether you want it or not, people crave it. So for me, consistency can be so many different things. You say people, who are you talking about? What people? Some companies, more than anything, a lot of companies, you know, either they value consistency so highly and they're like, all right, we said the same team to the same house every single time. And other people have thrown in the towel on that because we went through that major great resignation where nobody kept a job for longer than a couple of weeks. And, you know, it was almost impossible to have the same technician all the time. You throw in the towel and you're like, nope, we don't promise that anymore. We don't care about it anymore. But really, even if you're not promising it, if you're able to do it, you're able to get so much more out of your technicians and out of your clients. And that consistency helps so much. There's a whole spectrum there on one end of it. Yes, I guarantee you, you're going to be getting the same person every time we clean your home from now till death do us part, which would basically be over promising because, you know, and on the other end of that spectrum would be, no, you're going to get a person, a different person. Somebody will be there. If you're lucky, if you're lucky, somebody will show up. Yeah. So, you know, how do you, you know, how far, how far can you go with that? How do you, how do you, I mean, I'm kind of taking you off. I don't know if this is where you wanted to go with her. But I'm curious. I'm giving a presentation. I need to know this. Yeah, we'll help each other out. Yeah. No, I mean, so I, I, I see people have both of these mindsets either, you know, we're super consistent. And that's what we promised. And that's what we're working towards. And others that are like, we just don't do it. We don't care. We don't do that. And what I think people don't realize is how much consistency matters. And whether you're promising it or not, everybody else craves it. Your technicians crave it, your clients crave it. And the more that you're able to make that happen, the better your, you know, bottom line is at the end of the year. The less money you're spending on training, the less money you're spending on churn of clients and technicians and, you know, and the less time you're spending scheduling because everything's already set and ready to go. I mean, the returns on consistency are, I don't even know how to measure them. Honestly, I haven't, you know, how to measure the return on consistency is, is a whole project on its own. But there are massive returns on having good consistency. If you think about it on the, what's the, what's the opposite of consistency? I don't like birds. But, you know, I would argue variability. I would argue you can say change even and there's a whole body of, there's a whole body of work out there. There's a lot of people who make a living helping people deal with cope with change. So change and variability is stressful for people. I mean, there's the joke of like change is hard, but really change is hard. Okay. So it certainly, certainly makes sense that if you know what to expect because you've been there done that and that's important to your technicians. That's important to your clients. Everybody's happier. Yeah, I feel better just thinking about it. I'm looking forward to that, my presentation now. There you go. You're welcome. I mean, I'm like, I've always had a passion about consistency and it was really funny how it started to come up as I was, you know, writing down my thoughts for the data driven decisions is that that consistency kept popping up in a slightly different way. The consistency of action and as far as working towards goals and I may essentially talk about our core KPIs and a lot of people have different KPIs that they're tracking and stuff like that. And we know what good things can do and what bad things can do like if there's too much of the good and not enough or too much bad and not enough good that you're working your way in the wrong directions and you want to go. That's the level of consistency that comes from consistency to get predictability and that little marrying of those two concepts was really interesting. That if you're able to really get everybody on board internally that now we're not talking about technicians and clients necessarily now we're talking about what does this look like. What else can consistency do for you internally and how does that help your predictability of your projection. And I, you know, somebody, I don't remember who said it or where I wherever where even got this but the idea of working towards like using data as starting with observation working towards calculation and then getting action off of that and that becoming like your cycle of prediction prediction of your success and of your growth. And we've got observation down you know people have built spreadsheets and spreadsheets and spreadsheets off of their cleaning services and off of their results and where they're at. And then some people get stuck there. And then what I've always done is I've gone into the heavy, heavy level calculation of, okay, well, what does this mean if we go here and how does this change if we do a little bit of this and just like really working on calculating, like, what, what impact. What what numbers impact each other go together. And how changes on any of those could help change the bottom line. And then final thing being action. Okay, now what do we do with this. Now, now that we know what can happen. What are the things that we need to do to make that happen. And by doing that consistently, you're really able to really have some really big changes on on your business. If anybody watching wants to hit us up and chat with a question we'd be glad to help you with that. That being said, we get questions all the time, you know, Tom what's your favorite made central report. And, you know, I love all my kids the same right, you know, I don't really have a favorite. But it does make me wonder it's more of a like an observation exercise where not even to the point of trying to reach some conclusion from the data and then taking some action on it it's getting ready if you're playing, you know, house cleaning jeopardy. You know, I know my number, you know, yeah. So, do you have an example in mind of how this progression from observation to calculation to action might might look at a cleaning business. Yeah. So, really, I think the some of the easier ones to kind of, or the ones that maybe not as easy, but you can look at like technician turnover customer attrition, and I don't even know just like total home, something like that and the homes that you're cleaning, just to gain a couple of our KPIs, but you can look at those through the past and you can find trends, right, that's just observation, where depending on where you're located, you might see, you know, if you have a heavy summer life that your technicians turnover and your attrition, both the jump at that point, they both go high, because people start leaving and running around over the summer and they work together in tandem at least and then they kind of come back up during cool year and winter and what not depending on where you're at. By knowing that that's going to happen, you're able to start helping calculate how you out, outweigh that, what do you need to outweigh that how many people do you need to hire additionally to outweigh that turnover or that attrition, or how many extra sales you need to get to outweigh that turnover or that attrition, and then the next year when that starts coming up you now have, now you have calculations of how many need to do, and now you've got the action in place of alright, in April, we need to bumper sales up really high and we need to bumper hiring up really high using whatever methods work in your area and outweigh both that jump in attrition and that jump in technician turnover during that time so that when you come back in the fall, you're starting much higher than you have in the previous years, because that just steps you into a cycle, it breaks the cycle. You threw a term out earlier, you said predictable outcomes. So the example you're giving sounds like you're using data to predict what's going to be happening in the future. Yeah. Think about that. If we if we could like predict the future, how powerful that is. Oh yeah, it's a lot of things that can be predicted. We know how, how numbers, you know, once you have a level of data that you can use of where you're at, you can easily show where you're going to be in a year if you stay at that same place. And that's, I mean, it's nice to be in an industry that it is. It is a little bit easier to do that. But yeah, we can, we can do a lot more than we think if we're not just stuck in the day to day putting out fires if you're actually able to take the time to observe and calculate and do an action that will change what you're doing. So the example you gave, you know, probably any of us who have run our business for for a while for for a couple years, a few years have noticed trends. Maybe they're, you know, there's hunches can't really, you know, at that point can't put a number on it, but you know, it seems like But the observation is a good place to start. If you think that you see a trend, maybe that's where you want to start, you know, pulling some data together and do some calculations to actually verify your, your assumption. Yeah, so I always talk about the difference between long term data driven decisions and short term. So short term helps with your long term but short term really comes from questions like, why is this happening. Is there a link between this and this, how long has this been happening like being able to go back and seeing when it started and help it trying to figure out if you can find out what is the problem. Like those are always the questions that seem to start that sort of short term data driven data driven decisions. You're asking those questions, you go and find the numbers, and then you're able to start making changes on those to see what's going to do the most. And then that turns into long term data driven decisions because now that you were able to do some short term testing on what works best in your area in your business with your culture. Depending on what you're working on. Now you're able to say, okay, by doing this, I know that we're going to see an increase or decrease in whatever way you're trying to see it good or bad, a positive effect in this direction. And if I'm able to change it this much, you know, where would that put me. So when you say short term, is that kind of like an experiment just to verify your assumption. Pretty much yeah, like a monthly to quarterly nothing longer than quarterly typically of testing pretty much, because we have things that can change our, you know, our results. We all know different things that we can do we can throw more money at our ad words we can post more indeed ads for technicians we can, you know, higher do sales people we can do different things that are going to change those numbers some of those things are going to be better than others for your business. Not the same things aren't always going to work for everybody. Some are some are very much like a, you know, 100% for everybody but those are very rare. Don't think there's many of those things that are 100% effective for everybody in the same way. So you've got to determine effectiveness of your actions before you dig deep into them I think. You know, hey, I'm down with numbers. I like numbers, you know, I've been known to spend some time playing around with spreadsheets and going through that drill. Sometimes I wonder though, you know, can you drill too deep on the numbers. And, you know, where do you get to a point of diminishing returns how, how much data do you need and how precise do you need to be if if another degree of precision are you going to be making a different decision taking different action. And I don't could you speak to that a little bit. That's a really good point. I do think that there is, you know, the time to value is very important on when you're dealing with numbers and data because you can go too deep and go too far. The biggest thing I think about when I'm, you know, creating a stupid big spreadsheet to figure out what is happening is what can I do the most consistently the most often consistency again. So what can I, what can I grab that will be the same, it'll be coming from the same source using the same information every single time with as little effort as possible. What's the easiest way for me to get that information and the easiest and quickest really because you don't want to spend forever getting the minutiae of everything when it's going to be almost the exact same number of what you can get from a high level summary of the same information. So, you know, if I'm finding myself, I might dig deep for a second to determine reliability, but I will go out as far as possible from that reliability definition to whatever to like as far as possible from that. And still feel accurate or still feel, still feel on par with it. I don't know if I have a precision percentage precision amount that I use but you know I might dig in a little bit further take a small sample size and make sure that that's good and once I know that that feels good to me then I'm off to the races with the original number. So does it makes sense to be thinking about the action that you're going to be taking based on the data and what impact does that action have on your overall business. You know, once you have enough data to say okay, well I have enough data to know what I, you know, needs to be done. And if I spend more time getting more data more precise data, is that really going to change my action and if the answer to that is no well, I don't need to build another spreadsheet. And if the impact or the cost of that if the if the if the action means you're going to be spending a ton of money or the action means it's going to make you a bunch of money. Then I think I've got enough data to know what I need to go but you know it's the stakes are high so let me go ahead and maybe dig a little bit deeper. Does that make sense. Yeah, I mean, there's definitely I mean it is it's a return on, you know, the diminishing returns on how how far you want to go. And for a lot of things it's not I mean even if the potential outcome is really high. I don't think it. It still may not be the, you know, super beneficial to go super deep on it either. It, I'm trying to think of like an example of I'll give you I'll give you one maybe. One of the things that that that we we measure and put a lot of emphasis on and made central is customer turn customer turnover. What is your monthly loss rate for your recurring customers. 5% I guess is a nice round number that is probably average. I was kind of looking at some of your some of your presentation that you did for our last live event and I think for main central users it's a fair amount lower than 5%. But I think 5% is the industry average. So if you do the math on that, you know, you can say well gee 5% a month that means I'm getting 20 months worth of cleaning out of that customer. And if I'm getting 20 months of cleaning and say on the average are getting two cleanings a month and you know, obviously I'm pulling these numbers out of the air. I'm you know, but I just want to do that more precisely but that's roughly 40 cleanings on the average I get for every new recurring customer and say if every customer pays on the average $150 per cleaning was that $6,000. Say my cost to get sold is 50%, then that's $3,000 in gross profit. I'm going to make off of the average recurring customer over the lifetime of that customer. So how much money am I willing to spend on ad words to get a new recurring customer if I can get $3,000 on average. Would it make sense for me to spend $2,000 and ad words for recurring customer. But the numbers would say well I'm still going to be netting a thousand. So I'm going to spend a ton you know. The problem is it coming in the problem is going out. Explain that what are some of that what are some of the the other the problems with that logic. Why would I not want to spend $2,000 to make three. $2,000 and you turn around and give me $3,000 I'm going to keep giving you $2,000 as fast as I can right. Yes. Okay. Yes. So it's I think it would also be a measurement of what would make the biggest difference for your business as a whole that you know if you're already putting out. You know $2,000 does doubling it really still feel you know you're not starting at zero typically at that point. You're already probably doing something at on your ad words so is it $2,000 additional or is it $2,000 total. That that plays a part in it and then how much would it change the lifetime value of that customer if the attrition level was lower if your customer turn was lower. That if you were able to pull the turn down lower instead of just doing more money at getting more people. You know, with that lifetime value then get you that extra $1,000. Without, you know, having to put more money out there in a more predictable way. So, you know, one of the things averages are important numbers we make a lot of decisions in our business based on on averages and for good reason you need to know that but there's a lot of detail that gets lost in the averages. If you look at that 5% churn, what you're going to find is there's a lot of customers that quit your service within the first couple of months, a lot of them. And there's an occasional unicorn that'll do business with you for 20 years. So, the average might be 20 months, but if that's because you've just got a couple that stay with you forever. Okay, for the ones that you spent a lot of money on that you lose real quick, you're not going to get your return on them. And for the ones that are going to be with you for 20 years to get you back to that 20 month average, you're going to be waiting 20 years to get your $3,000 that you were anticipating to get for you. Yeah, exactly. That's two thousand and out of words. The things that turn around very quickly. So, you know, that's an example where if the stakes are high and you're spending a lot of money, you might want to. The averages can lead you to make bad decisions sometimes. I think it's really good to pay attention to, you know, like to constantly be looking at your, not constantly, but regularly be looking at those numbers for your most recent period of time. You know, I think we can usually assume that that customer that's been with us for 20 years is probably not going to leave us anytime soon. So don't really need to worry about taking them into account on most things because they're bought. They're so bought nothing that you're very few things that you're going to make decisions on are going to change their business with you. As long as I see their doctor occasionally and they're fairly healthy, you're good. We've got a few of those, dang it. Yeah, you're typically good on that front and not very few things that you're doing are going to affect their business. So really focusing on what matters and that's part of that intuition thing. Intuition I think also can be skewed by averages too. If you're taking an average on something and you're taking that average over a long period of time and that's what you're seeing all the time. Your intuition is probably going to follow that. You know, what you believe is happening is just going to follow that average. And so it's really important to pay attention to what your, what data you're taking, what timeframe you're taking. If there's any blips along that timeframe as well. So sometimes even over the time, over a time period when you're talking about putting in a lot of investing into something, you might want to just do more than just averages. Looking at how something has changed over time as well. If you are going up, going down on that particular front, so average lifetime value of a customer, are you going up or is it going down? Because yeah, if you're going to throw $2,000 of your ad words, but the lifetime value of your customer is actually trending downwards, and your return is actually smaller than what you thought it was. So sometimes looking at that too, or it could be bigger if your lifetime value is actually trending up. Yeah. And looking at it in two different time frames, like if your three month turnover rate is higher than your 12 month turnover rate, even if it's fairly low, you know you're going in the wrong direction and it's beginning higher. Yeah, and I'm a big fan of year over year comparisons. And some people haven't been taking that information for that long, but year over year comparisons are huge in predicting where you're going to be at this time next year. So tell me about that. How does that work? I, so I've pulled, I started, when I was with All Star, I started pulling all the things in 2018, I think was the first iteration of that. And to get, you know, by all the things, for those of us who might not know what that world is, what are all, and it sounds like a bag, an everything bagel. It is. It's an everything bagel for cleaning service leaders. So all the things at the time was, you know, getting weekly data points for sales, quality, technicians, revenue, cancellations, I think that's everything payroll. Pretty much all of the major pieces of the business taking a, you know, weekly aggregate of all of those things to see how things were going over time. And we started doing this in 2018. And this pretty much exists and made central now is the set by week is almost the exact same thing. It is extremely close and it's almost that one more thing on there and that's coming. So that is that's very close. And it just gives you a really good quick reference. And it's that upper level data point, right? We were taking the summary upper level data point of all of this information and being able to see how they relate to each other. What is their commonalities that any of this stuff happened at the same time? Throwing them all up on different crafts of over a period of time and getting to see that. All right, for the three years that we've been doing this, tracking all of these numbers on a weekly basis. What, what's the same and what's different? And, you know, being able to see revenue trends around summertime, that's where that example came from. Being able to see technician turnover trends also around summertime. Let's see here, you're able to, you're able to make decisions on the next year. So once we found those patterns or saw those patterns, then, you know, the next year we were able to start trying to outweigh them. Didn't work out perfectly, but it helped. You can do it with, you can do it with almost anything, anything that you're tracking on a weekly basis or even just on a regular basis. You can kind of see where you're at year over year and being able to take advantage of those. Oh, when you're looking at like year over year and hopefully you're seeing growth year over year. Is it a percentage that you're looking at? Or is it a like a dollar amount that you're you're looking at? I like for that case for those year over year changes and like trying to find those patterns. I don't even look at dollars or percents. I'm looking at visual trends of, you know, if I'm looking at 12 months for the last two years, two 12 month periods or even two, it would be favorable for three years, three 12 month periods. I'm just trying to see if there is similarities in the trend lines across those, assuming that there's growth. So assuming that as the years get further more present, they're bigger, but do they still have trends? Are they still going up and down in the same way across the year that they were three years ago, even just at a larger volume? And if you're at a larger volume, sometimes those changes can mean a lot more. So when you say trend line, that makes me think of a graph. So are you just looking at like a spreadsheet with columns and numbers or are you creating graphs out of this so you can see the data in a different way? I am so visual, I'm definitely creating graphs. And I think that's actually a really good point on data driven decisions that is that you do need to put it into a format that you relate to. Very few people are going to be able to scroll down a column of numbers that look vaguely similar across 169 weeks. Is that three years, something like that close to that? 172? It could be like 156, I believe. 50 times 3 is 150 and 3 times 2 is 6. Maybe it's a leap year, maybe it's a leap year, you know, I don't know. Alright, cool. So 157. Similar numbers, right? They're not going to have major drastic changes. Maybe the top and the bottom will be different, but just scrolling across them. You're not, a lot of people aren't going to be able to see those changes. Some can, I'm not one of them. I definitely need to graph it. I charted something, some way to put it into a visual method of me being able to see how is that changing. So what you're, you know, explaining here, it seems like that once we start getting into the analysis part, the calculation part, it sounds like that. Spreadsheets play a role in this. This isn't as simple as just looking at a report out of Made Central or QuickBooks or anything else. I mean, you're taking the data and you're putting it in a spreadsheet and you're playing with it. Yes. For most people, yes. I think that that also becomes more of a necessity, the larger people get as well. It might be a little easier to see it on a smaller scale to see some of those changes without putting all of it into a spreadsheet and playing with it. I don't know. And I know, like I try not to put too much focus on that. The biggest thing that's why I like graphs is that those most people can understand graphs or at least see the ups and downs in a good or way or a bad way. So, yeah, you know, plugging that information into a spreadsheet, looking at it in various ways. And if not finding outside sources that can help with that. Yeah, I wanted to go there. So not everybody knows how to use Google Sheets or Excel. Certainly at the level that you do, Aja. So do I, does one have to be, you know, a top gun of fifth degree black belt and spreadsheets in order to do this? I mean, is there a basic skill level that one needs? Should I even bother, you know, if I'm, if I own a cleaning business, should, and I don't know how to, you know, do spreadsheets. Should I even bother to learn or should I just hire somebody? I mean, you know, I know, I know there's a lot of people listening to this. It's like, you know, I can spell Excel. That's about it. So what am I supposed to do? If you don't put your time into it, find outside help for that. I definitely, again, time to value the time of learning how to even get to that base level. Because, yeah, I do think that there's a super base level of Excel work that you would be able to use to take something like the staff by week report. For the last six months, plug it into a spreadsheet and make some graphs off of it and be able to see some of those trends. That is a step above basic. It's a little bit more than a sum formula, but mostly, mostly self-explanatory and especially G Suite Sheets. The Google Sheets for graphs and stuff like that is actually a lot more self-explanatory than Excel, but shouldn't be too hard. But if you can spell Excel and that's about all you feel like doing or all you're really wanting to be capable of doing right now, time to value. Your time is way more valuable spent on the things that you're good at. Do not spend time trying to figure out how to use Excel to make these things happen or be able to look at these things or, you know, play with these things. Find another way to do it. Either somebody within your business that already exists or hiring without people that can literally just manipulate information for you. So they don't have to tell you what it means. You can probably, once you see it, you can probably know what it means. But yeah, there's a lot of different ways to do that. Time to value is huge. Don't spend time doing things that you're not valuable in. So do you, if you're not valuable in it, I mean, imagine you can find people on Fiverr. I mean, there's probably a lot of people out there that are really good with spreadsheets that would love to crunch your numbers for you. Oh, yeah, for sure. There's, I think, what's the other one? Upward. Upward, yep. GOO is the one that's been around forever, maybe not as popular, but it's got a lot of people there too. Yeah, I think I've, I've looked into a lot of those when I was looking for extra help for me for just kind of getting the base level of things done before I wanted to do the big extra stuff. Like, I don't want to add all of these, these formulas here where somebody else I can do that for me so that I can do the fun things that I want to do. But yeah, definitely, there's lots of other opportunities for that. Because there is a level of every business is different and you're going to have to look at your numbers knowing your business a little bit more than somebody else looking at it who doesn't know your business. I love your model, though, it's like observation, calculation, action, it starts with the observation. So, well, what, you know, what, what spreadsheets do I need? What do I need to build? Well, that depends upon what you're observing, right? Yeah, what are you, what are you seeing? What is it going as expected or what numbers are higher or lower than you'd like them to be? It all starts there. Why is this happening? Now, there's some, you know, nuts and bolts kind of blocking and tackling parts of running a house cleaning business that, you know, if you're using the right, you know, the right software to manage, you're not really having to crunch a lot of spreadsheets for a lot of this. You notice your payroll to revenue is creeping up. There's, you know, you're not charging a month as much as you need to or you're not managing your labor as well as you need to. And there's kind of a progression, a flow chart, if you will, in terms of how to address either one of those. You very well might not need to hire somebody from Upwork to build a spray sheet for you. You just need to do the hard work of managing your business. And also understanding how different things relate to each other. You know, really understanding that your payroll to revenue, you know, we used to be able to tell when we were training and how many people were you're trading like to the number off of that payroll to revenue number. That took some time of just watching it and understanding it and knowing that we are that was important to our bottom line or what we are working on. And knowing when not to worry about something too. That that comes into play. That's an observation thing is like, okay, well the numbers bigger. Okay, before I spend a lot of time trying to figure it out how many people did I did I train that month. Or that week. I'll give you another one, comparing things like revenue month to month that, you know, well how many days were in those months and sometimes the number will go down and it really felt like it you were growing and it was like, well, you know, I had, you know, three less work days this month and I did last month. Yeah, August is so great every year. Why is that? And those things matter. September always feels like a rough one because it comes off of to two big months. And suddenly September feels like shrunk, but it's just because of the number of work days. Or if you're looking at your P&L like looking at a quick box and it's like my gosh my payroll really blew up this month as well. You had five pay days that month compared to four pay days and most months and that'll mess everything up to critical thinking on those things are really important. You can't you can't stop at just, Oh, this is bigger or smaller than it was. A lot of the why is this happening. What's the link between this and this. So, in Chicago, and two and a half weeks, I guess, you're going to be doing a deeper dive in this discussion. Yeah. And if you're going to convention, awesome, we'll see you there. If not, and you're interested in going, I want to know more about it. I'm dropping a link in chat. You can go to the RxC website is a show and it will take it to this page and you can register if you want to see the topics we got our friend Liz who. Oh, Liz isn't here today. No, she's trapped. Just just miss Liz. Matt is going to be taking us through discussion here seven steps the cleaning business can follow about to get results. Here's Aja and this is on Monday. This is where we're going to be doing the data driven decisions and this is going to be recentation is going to be fun you definitely don't want to miss that. And there's cool stuff going on Tuesday and Wednesday as well. Take a hard look at this we hope to see you there so we've got just a couple minutes left. Is there any final thoughts you think that we should wrap this up with. I mean, I just want to say I'm super excited for convention because it's my first time going. Yeah, I've never gone before so I'm like, I'm super excited. I think I showed up for a day and somebody let me their past just to kind of watch walk around the trade floor for a little bit but that's the most that I've ever seen of it so. Where was that do you remember. I don't. Where's it been recently. In Las Vegas the last couple of years. Oh, then maybe it wasn't even that maybe it was a different. No, I don't even think it was that I haven't been to Vegas for an industry event before. It was in Dallas a few years ago. That would have been it. Okay, I think that one was it that I was in. We went to or was it an awesome. No, it had been Dallas. That was a while ago. Occasionally it's in Chicago. I don't think it's been in Chicago for the last few years but it's in Chicago this year. I think it was the Texas one say I think that was when. And that would have been a while ago. That would have been 2017. That's my gas. That's my gas. I think I saw the show floor for about 30 minutes and that was my extensive convention so pretty excited about it. I am excited to get a little bit more of the whole thing to see some people I haven't seen in a while and no first big industry event in the last couple of years. I'm pretty pumped. Sweet. Yeah, it's going to be fun and it will be here before you know it. You're telling me I've got I've got a lot happening right before then. Yep. Never a dull moment. Not a single one, but yeah, no feeling good. Very excited. So we'll go ahead done and call this a wrap for today. As you know, as always, you're awesome. You're my favorite spreadsheet expert. I mean, you are you are absolutely the subject matter expert when it comes to this topic. So it's my passion. Let me tell you. We'll see you a couple weeks in Chicago. Smart business moves here every Wednesday, five o'clock Eastern. So don't see you before we'll see you again this time next week. Again, I just thank you everybody take care. See you soon. Bye bye.