Nate Kaemingk is Chief Forecaster for BetterForecasting.com, where he has built a financial forecasting AI for mid-market finance teams. Previously he has been a Fractional CFO for two Montana based companies. Nate started his career as a mechanical engineer using inferential statistics to model chemical reactions in Diesel engines! Later, during his MBA, he applied an inferential statistics background to forecasting and scenario analysis. Nate has had the opportunity to apply these methods in roles with multiple Fortune 500 companies, including Cummins, and subsequently as CFO and Chief Forecaster at Better Forecasting.
Episode links: www.BetterForecasting.com
Connect with Nate (LinkedIn) https://www.linkedin.com/in/nathankaemingk/
On this episode:
- Probabilistic Forecasting, small (not big data) and inferential statistics
- Rolling Forecasts vs Annual Budgets explained
- Importance of not skipping out on the fact you need to update your plan
- Why are we resistant to changing the budget?
- The advantages of a rolling forecast
- Using Leading Market Indicators
- DSO (Day Sales Outstanding) as a powerful forecasting example
- How long range forecasting reveals structural issues in a business
- Building a driver-based model – and why it’s hard
- Renting an RV and touring the country for 3 years
- Favorite Excel function
Full transcript below
Glenn Hopper:
Welcome to FP&A Today, I’m your host, Glenn Hopper. Today we welcome Nate Kaemingk to the show. For two decades, Nate has been immersed in some of the most complex ie nerdiest math and statistics out there. Starting his career as a mechanical engineer, he honed niche skills in math and statistics and these skills. He later discovered thanks to some insights from his MBA cohort of commercial bankers could be a powerful asset in forecasting and financial modeling. Nate’s journey took him to product demand and financial forecasting at Cummins Inc.
Which is a Fortune 500 giant for those who don’t know it, um, where he applied these specialized techniques to deliver next level insights. After serving in various roles, including as CFO, Nate saw a gap in the FP&A in forecasting tools on the market tools that couldn’t keep pace with the sophistication that he knew was possible. In response, Nate founded better forecasting, which launched its beta AI powered forecasting product in April. And with a recent major update, they’re poised to transform how FP&A professionals spend their time automating the heavy lifting in financial modeling and creating new opportunities for strategic insight. Nate, welcome to the show.
Nate Kaemingk:
Thanks for having me.
Glenn Hopper:
Yeah, man. So, uh, <laugh>, what are these, these nerdy niche math concepts and why do they, uh, why do they matter so much for forecasting?
Nate Kaemingk:
Oh, man. Starting with the heavy hitters here. Yeah,
Glenn Hopper:
Let’s go right in.
Nate Kaemingk:
I’m gonna, so it’s probabilistic forecasting, which is different than deterministic, and we can get into that if we need to. It’s small data, not big data. Uh, ’cause in financial forecasting, we don’t have big data. We have, you know, if I have five years of financial statements, I have five Octobers. That’s five data points and inferential statistics. Why I say inferential statistics is because there’s a big difference between descriptive statistics and inferential statistics. Those are some of the key concepts and some things that I happen to have learned as an engineer. And then when I went to start doing forecasting, it was like, oh wait, um, I guess finance people don’t get degrees in advanced statistics, so there’s maybe a need that I can fulfill here. So, yeah,
Glenn Hopper:
A little off topic, ’cause this is just bit interesting to me because I travel around and as we were talking before the show, talking about, uh, implementing AI in finance and finance and accounting got passed by sales and marketing in, um, sort of machine learning and use of AI early on. But to your point, sales, I mean, if you, if you have an e-commerce site and you have all this data, they actually do have big data where we don’t have it in sales and marketing. So I think a lot of, you know, we, we know these concepts and we hear ’em out there and we see what sales and marketing are doing with these big data sets, but we don’t have, um, big data, but we still want to benefit from Yeah. Uh, being able to use machine learning and, you know, make data driven decisions. I guess what you’re saying is we just, we have to have a different approach. We can’t Yeah. Try to use the same map.
Nate Kaemingk:
Totally. And I, and I’ve seen, like, so I, I don’t know that many finance people are gonna get into this, but a place to look for technology is R libraries and Python libraries that are available like through, through just, you know, packages you can install. So a good example would be Facebook’s Prophet. And I’ve actually seen some CFOs and FP&A teams that use that, and they’ll learn Python and they’ll use this library. But that’s a, a really interesting example because Prophet itself, um, has some major flaws that aren’t apparent until they cause bad problems. And so it’s, and part of the reason for that is because it was built on technology that’s used for big data. And, you know, the definition for big data for purposes of our discussion is anything with a thousand data points or more. So there’s some, some statistical convenient assumptions you can make when you have above a thousand data points that you can’t make when you have less than 30 data points.
And so the issue becomes like, Hey, it’s easy, it kind of works, so we’re gonna apply it here and it’ll get you a good starting point. But it’s, it’s, there’s a couple of major, um, caveats or pitfalls that can end up causing some issues with your actual forecast down the road. And so the reason I, I talk about those libraries and what’s available is it’s, I don’t, I look at a lot of them, and most of them are doing the same thing. They’re applying big data concepts and they’re trying to make it shoehorned into our little shoebox. And it does okay. But when we’re talking about, we’ll, we’ll talk about this, I, it sounds like we can get into this if we need to, three to 5% miss on a revenue forecast is a huge deal to our cash. And so the, the difference between a big data machine learning versus a area specific small data that we’re applying could be that three to 5% difference. And that’s a big deal.
Glenn Hopper:
So I do wanna get into all this, but I guess maybe we need to, um, level set a little bit more than what I did in my introduction. I’m sort of having to put on the brakes a little bit to go straight into this, but I want, I do wanna pause and say there’s like a human element in a background in <laugh>. So, so I guess, you know, and, and, and I touched on it in the intro, but tell me a little bit about your background and how you ended up coming to, to do better forecasting. Yeah,
Nate Kaemingk:
Yeah. Well, and I think you, you put your finger on it. So I actually started as a mechanical engineer. Um, and I learned, so at the time, this was 20 years ago, so two decades ago, um, I was trying to solve a, uh, chemistry, physics, mechanical engineering problem in, in terms of like emissions for diesel engines. There was no papers written, there was no like first principles research on the area that I was doing. So I had to go and, and I couldn’t like find a science paper on, this is the chemistry. I had to use inferential statistics to help me figure out what’s going on. Now what was interesting is it was $10,000 per data point to collect a data point. Now, this was 20 years ago, so $10,000 was a lot more money at that time, <laugh>. Um, you know, we were, I think at that time we were, it was, you know, huge portion of my annual salary every time I said, oh, yeah, I need one more data point.
Um, so because of that specialization, when I went into financial forecasting and I went to get to do product planning at Cummins, which I got to play with lots and lots of zeros when I was there, I, everyone was, even the finance people and all of it were like, where did, where did you get this math from? Because nobody’s applied this type of math to our space before. And so that’s kind of what got me thinking about this. You know, my dad was a business owner. I kind of always wanted to start a business. And then, um, it was like, well, how do I make it as easy as possible for somebody in finance to be able to use all this advanced statistics without having to go get a master’s degree or a PhD in statistics? And so that’s what our software is doing. I’ll talk about a lot, you know, we, we could get into how nerdery you want to in the math, but the, the, the reason for doing that is highlighting a couple of of things that we’ve already handled. So you don’t, if, if you want to use this technology, we’re talking about, you don’t have to go learn r and you don’t have to go get a master’s in statistics. You know, you’ve already got a degree in accounting. You’ve already got, you know, you already understand your business. So we’re trying to take that complexity off of your plate and make it just automatic,
Glenn Hopper:
You know, the site, um, DataRobot, it’s a, it’s a drag and drop machine learning, uh, website. It was around,
Nate Kaemingk:
Oh, I haven’t actually played with it. I’ve heard of it, but I haven’t, yeah,
Glenn Hopper:
When I was, uh, studying analytics, um, we got free access to this and I, it’s like a $50,000 a year subscription. So super exciting to get these, you know, drag and drop models that you could do and do anything. But, uh, there were people using it who had no idea what they were doing. You know, they didn’t know the difference between, um, you know, clustering and classification or what. And they were, they’re using completely the wrong models. And it’s a very powerful tool, but it’s a very powerful tool. Like, you know, dri like driving an F1 race car, you have to know the basics of how to do it. And I, I’m wondering, I think it’s important that people, you know, if you’re gonna be using these tools that you kind of understand, but it sounds like with, uh, with better forecasting, maybe you kind of have guardrails in there and it’s like, yeah, we’re gonna, we’re gonna take you down this path.
Nate Kaemingk:
Yeah, yeah. And we do. And that’s part of the, that’s actually probably the part of this journey that’s been almost the more difficult one. And it’s how do we make this usable for somebody that doesn’t want to, you know, doesn’t care, doesn’t need to care. Um, our goal is if you’re using one of our ais for forecasting, it’s gonna do all of that stuff for you. It’s specifically trained in these methodologies. And all you have to do is say, well, okay, for this line of business, consider these three line items in our CRM. Or if, if you’re trying to do a rolling forecast and you’re looking at an expense line item, insurance is a good example. All you have to do is tell it, well, here’s our labor code. This, this line has labor, this has, has admin labor, this line has direct labor, and it does everything else for you.
That’s all you have to do. We’re really trying to make sure we’ve, we’ve created a way to capture the business knowledge of the fp and a person and their ability to partner with the business. But then all of the number crunching and everything, we make it as easy as possible. We also try really hard to do integrations with Excel, um, because I love Excel. You know, I, it’s interesting how many times I see, oh, Excel is dead or whatever. I, I always laugh when that happens. But, um, you know, because Excel is such a powerful tool, and I actually, originally, when I started building this, I was doing, I was a CFO and I was starting to try to build my own forecasting. I actually built several of these models in Excel, but I, we kind of ran into a processing limit at 30 models in Excel itself. And that’s when I moved over to Python. And now it’s, okay, well, we’re just gonna try to get you as accurate as possible driver-based forecast models for every line in the chart of accounts. And then we also will do a three statement model. We’ll create a three statement model that somebody can use, or you can use your own and plug it in. We’re just trying to elevate the statistical power of your existing process and the existing tools that you’re doing.
Glenn Hopper:
You mentioned something earlier that we had talked about before the show you when you said rolling forecast. And I think that this is, um, a big, uh, you know, to this day in, in really people in fp and a for the most part, to me, they kind of get the difference. It’s the rest of the company that you have to, um, educate and explain this to ’em. But the, the, the core differences between a rolling forecast and an an annual budget, why do you think it’s so common for people to confuse the two? And, and what do you think leads to that misunderstanding?
Nate Kaemingk:
I’m gonna talk about this the way that I would talk to A CEO or to a non-financial person, because I find, especially when we as finance people are talking like, oh, that’s how that guy explained it. So maybe I can use that to explain this to my CEO, right? So think of it this way, um, I’ll talk about, I’ll start with a budget and then I’ll move into a rolling forecast. So a budget actually contains four different key pieces of information. The first one is, it has a target. It’s a target that we’ve gone through a consensus based process to identify like, Hey, this is our target for the year. You know, we’ll use some fictitious numbers that I threw together just to kind of think of a way to talk this through. Um, the second piece of information it has is a forecast. So a forecast is gonna be, well, here’s the expected outcome.
Now there’s a forecast and a target inside the budget. And when you finish the annual budget, they should be the same, right? It should be, this is how we expect to get to the target. There’s actually two other pieces of information in here, and that’s the plan, the plan for what we’re gonna change to get there. And then there’s also the expected outcome of the plan. Now, I want to, I’m using these four words and let me, I’m gonna walk through a word picture so you guys can have an idea how to talk about the difference between them. The first thing, let’s say we’ve got a $12 million a year line of business, just to make the math easy. So it’s a million dollars a month in, in revenue, but we have a, a budget session. And in the budget session we say, Hey, we’re gonna hit $24 million in this next year.
A little bit outta reality. And there’s caveats to all of this, but I’m just trying to make a point here. So that means we’re in, come January, we’re gonna hit $2 million a month in revenue. So, okay, we put together a plan. Here’s the, the forecast was 12 million a year. This plan is gonna get us from 12 to 24. The end of Q1 comes around and we have our Q1 actuals, and we actually hit one and a half million a month. So we only achieved 50% of our plan. That means we’re at four and a half million at the end of Q1. And the traditional way that I see people do this, we all do this, right, is you do your three plus nine and you say like, okay, well we’ve got four and a half million plus I’m gonna have, uh, 18 million for the last nine.
That means the, uh, we’re on track for 22 and a half million. The issue is, that’s not actually gonna happen. ’cause your plan is only 50% successful. So the forecast shouldn’t say 22 and a half, the forecast should say 18. ’cause you’re at a run rate of one and a half million. But now, but your target’s still 24, your forecast is 18. And what we have to do at the end of Q1 is update our plan. We’ve gotta have a new planning session. We’ve gotta have a way to make up the $6 million gap. ’cause we all know that we agreed to the target at the beginning of the year. That’s what our bonuses are based on. And all of, you know, if you’re publicly traded, you’ve already told your investors. So it’s important to understand the key differences. And that’s that. Okay, well now my second plan, you know, call it a different plan, but is gonna be, here’s my, my $18 million is my forecast.
I have an updated plan for the $6 million that I have to make up to hit the target of 24 million. So that’s, it’s kind of complicated, but it’s like we have to recognize that a budget contains all four of those pieces of information and a whole bunch of assumptions that it’s all gonna work out and, and kind of aggregate into the consensus that we agreed to. And so, the rolling forecast, when I’m doing a rolling forecast, I always have to talk, ask people like, okay, is this a forecast or is this a plan? Because they’re different, a rolling forecast. To me, the forecast is a peer, this is the trajectory we’re on. This is what we’re gonna, is gonna happen if we don’t make any changes. So no changes, no management changes. What we’re doing is working. The existing plan is there, which is different than the target.
If you’re lucky. They’re the same if things, if your plan is a hundred percent effective, which they never are. But if your plan is perfect, then you’re gonna, your forecast is gonna say you’re gonna meet the target. So I always, I, I always have to go into those. And I, and I think what I say when I go to a company and I say, Hey, where’s your forecast? Somebody will give me a budget. And I always say like, no, this is a budget as a forecast, which is B-B-A-A-F. And I call that baffling, uh, because we’re missing a couple of key. Um, it’s really important to understand the decisions that need to be made around, okay, we need to change our plan because the forecast is not working. Or the, the, the existing plan’s not working. The forecast is telling us we’re gonna miss the target.
Glenn Hopper:
Yeah, it’s got, it’s, you’re, you’re giving me flashbacks to my early days in fp and a and I, back then, I, I don’t even think it was called fp and a, I was just the, the finance guy. I was in telecom and we had very aggressive budgets, a lot of m and a activity. And, um, you know, we would give the annual budget to our investors and we would go, you know, we’d get through the first quarter and we’d be 20% behind budget. And, but we would, uh, you know, the, the CFO, which we’re, we’re not gonna re, we’re not gonna recast the budget after one quarter. So it just, the, you’d have your actuals, you’re behind on budget, but then the budget for the rest of the year, you still, when you’re showing that full 12 month view, the budget stays the same. Yeah. So you’re not even, you’re not even making a forecast to say, this is the reality. It’s just put the pressure on sales, we’ve gotta make it up, or, or whatever. Yeah.
Nate Kaemingk:
And that’s a, that’s, you’re, you’re like intentionally skipping the fact that you need to update your plan. Like, to me, it’s really important that the forecast is this is the most likely outcome. Now the rest of that process is important. It has to happen. But if we, if we don’t acknowledge that the, the pieces of data are different, then we end up just ignoring the problem. And that’s not useful. Right? So I always say bad news early is good news. ’cause if, if you get the, if you get the news early enough, you can fix the problem. Now, all of this puzzle piece pulling together is one of the reasons why a rolling forecast is so important. Because a rolling forecast should be like, I always like to show forecast versus target, or forecast versus budget. And so the reason that companies fall into this trap is, is because like we said, because you, well, the budget is the forecast. They call it, you know, they’re, they’re not recognizing the different pieces of the puzzle that are, that are in play in this.
Glenn Hopper:
And again, we’re, we’re, we’re preaching to the choir here. Sure. But sometimes that’s what’s fun about the show. So <laugh>. So, um, you know, I, I think it’s important that we talk about what the purpose of an annual budget is Yeah. And how that supports the decisions and alignments. And then on the flip side, kind of the, some of the pitfalls to traditional budgeting. And it’s, it’s, it really, it’s that Mike Tyson quote, right? Like, everybody has a plan until they get punched in the face. Which by the way, coming up, uh, actually by the time this airs, it will have happened. I don’t know if you’re gonna tune in and watch Mike Tyson and Jake Paul YouTube influencer box. Uh, but that’s coming up this Friday. Oh man. And I’ll be,
Nate Kaemingk:
I didn’t hear about that. That’s, that’s great. Okay. Yeah.
Glenn Hopper:
58-year-old Mike Tyson boxing a YouTube influencer. I don’t know. Yeah.
Nate Kaemingk:
I, I would not wanna box that guy even at 50, at 58, man, what? But I’m not a boxer. So, yeah, I think, I think the, the annual budget is a, is an opportunity. I think it’s important to get consensus on what we’re gonna do. It’s important to set the target because you, you know, that’s when you’re saying, okay, this is, this is our priority. This is what matters the most this year. Um, here’s how we’re gonna go about doing it. And I think one, I I ran into this problem quite often. It’s a similar one to what you’re talking about. And that’s this, okay, well, hey, the forecast update says we’re gonna miss the budget, and here’s the market driver reasons for why. And so a lot of times the operating team will try to say, well, that’s our new target. Like, no, no, no, no.
That’s not our new target. That’s not okay here. Our target is the target. Um, and I think it’s really important to set a target. Like I think that that’s probably the most important piece that comes outta the budget of here’s the target, here’s how we’re gonna get there. And then when you’re doing your, your quarterly reviews or your monthly rolling forecast updates, you should just be tweaking the existing plan. Like, it’s, you’re not, you’re not writing a completely new plan. You’re just saying, okay, well hey, we’ve gotta fix about 20% of this plan to still get to that end target. I think the, the annual, you know, the year round budget is somewhat arbitrary. You know, I know we’re used to doing it that way. I’m probably, I’m, again preaching to the choir here, but I think the real purpose of the, the, the annual budget is here’s the target, here’s where we want to go as a company.
And people spend a lot of time thinking about that. And then I, I think one of the other mistakes that I see is the assumption that a rolling forecast update is a rebuild of the budget. And that’s why this distinction is important, is that when you’re doing a rolling forecast update, the forecast itself should be really simple. The, the, the thing that I run people run into is they say they try to rebuild consensus around the forecast every time they do a rolling forecast. And that’s not what we’re, that’s, that’s not what we’re trying to do. The forecast is just, here’s where we’re most likely to end up. And then you have the option of saying, do we need to update our plan?
Glenn Hopper:
I’m thinking of how many board meetings I’ve been in where, um, or even with the, with the rest of the management team where, you know, you, you start to see the reality, and it’s really weird from the office of the CFO, you know, you start to see the reality that you’re gonna miss the, the budget that everybody planned to. And then, you know, but then nobody wants to recast the budget because that’s, no, we said, we all agreed upon this, we’re we’re gonna do this. So, and you know, you’re, you’re trying to set expectations, whether it’s figuring out cash flow and, you know, just all the parts of where we actually are gonna be. Whereas, you know, sales and marketing are driven by, you know, we’ve gotta have this much revenue. And ops has their own their own goals. And, uh, people lose their minds on it. And then somehow the, the finance person becomes the bad guy by saying, <laugh>, well shoot the messenger. You know? Yeah.
Nate Kaemingk:
And I agree. And I, I also think that one of the, the reasons people have a resistance to changing the budget is because it’s a lot of work. Like, it is a lot of work to, um, put together an updated budget in Excel with those, with those tools. Now, that is also one of the reasons why we’re building the tools that we’re building. And it’s because, you know, and you can do this with some machine learning, you can do it with a really good sophisticated Excel spreadsheet. But it’s this idea that, okay, well, actuals for March just came out. And I’ve heard all kinds of stories of, well, how long does it take to update my rolling forecast? So I’ve heard, you know, it takes two days, which would basically mean you’re dumping the data in, you’re not doing any model updates or reviews to, it takes eight weeks.
And on the eight weeks end of the spectrum, you’re probably updating all the models. You, you’re actually doing a good job. The, the problem I kept running into is like, well, I would like to do that level of sophistication every month, but it’s too much work. And so I’m stuck with doing kind of a, sort of an update, you know, but in reality, because plan’s not working, I need to change my model for a plan, and that adds time. So we’re kind of stuck in this continual trade off between, well, how much time do I have to update the forecast for? How accurate can I get it? Or how, how, how much capability can I provide? If you move over to like a true machine learning or an AI assisted based process, then what used to take, you know, let’s call it a week out of every month for an analyst to go and do an updated, you know, if you’re doing a pretty good update to your rolling forecast, AI can do that in minutes, right? You’re not doing the consensus building, but you are at least rebuilding the picture of the most likely outcome. That’s very easy to automate.
Glenn Hopper:
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I mean, and maybe this is something that even in <laugh>, even in finance, I think we may be lose sight of or forget. So we know the main purpose of the annual budget and you know, you’ve got the, this is the strategy for the company, this is the sales and marketing plan, how we’re gonna get there. This is what everybody agrees to spend and what it’s gonna cost and all that. But then the <laugh>, the rolling forecast almost becomes like the excuse Yeah. <laugh>, like for not hitting your plan. But walk me through, I mean, there is, obviously there’s advantages to, to rolling forecasts as, as well as as pitfalls too. But walk me through the main objective of these rolling forecasts, and especially in helping the organizations kind of anticipate issues.
Nate Kaemingk:
The two main areas that I always try to use a rolling forecast, the first one is cash, right? Um, ’cause you’ll put together a pretty sophisticated, you know, okay, if we hit budget, this is where cash is gonna end up being, to me, in a rolling forecast, it’s actually really important to know like, okay, cash in nine months or cash in 12 months is gonna be this, and I want to update that forecast every month. I’m, I’m not talking about the 13 week rolling cash forecast. I’m talking about a 12 to 18 month rolling cash forecast. And the reason we wanna do that is because if you’re not hitting budget, or even, honestly, it’s probably the other way around. If we’re hitting budget or we’re exceeding sales budget, then our, and our receivables are 90 days, then we’re running, we’re running the risk of using more cash than we planned for or needing more cash in working capital.
Um, we all know how that works, right? But to me, the updated forecast is the, the rolling forecast is gonna look for that cash issue to occur because you’re, you know, hey, oh man, I’ve got 90 days, or I’ve got 120 days. I’d rather go increase the, the cap on my line of credit because I’m gonna need it. Or I gotta go talk to sales and say, Hey, it’s great that you’re selling this much, but we’ve gotta change our, our default terms from 90 days to 60 days for everything you sell from this point forward. Because that’s the only way we’re gonna have enough cash Now, because I had that ruling forecast and I could see what levers I had available to play with, I was able to implement a temporary, Hey, for the next two months, we gotta go 60 days. Um, you know, and if they want 90 days, then you gotta come talk to me about it.
But even though your normal is 90 days, does that make sense? So like, you have an, you have the ability to react, and it’s this idea that bad news early is good news ’cause you have time. If we go to the revenue piece of the puzzle, um, it’s, you know, market chaos, right? I mean, we’ve all been through tons of that chaos for the last couple of months. And it’s amazing how often you’ll have people that say after something crazy happens in the market, people will come and be like, oh man, you know, I think I saw that starting to happen six months ago, but I just didn’t recognize that that was what was going on. And so the other reason for a rolling forecast is if you’re only, if you do a rolling forecast every quarter, you might have actually, when you get to say April, you might have actually had an indicator that the market was starting to soften in February, but because you’re not updating the rolling forecast, you’ve missed out on two months out of say, six of time that you had to respond and correct and course correct as a business.
And so what I would like to make sure we understand, the separation between, just because you did a rolling forecast, doesn’t mean you need to change your plan. So do the rolling forecast, it’s gonna tell you how well your plans are working, where you’re most likely to end up, and then you can make a conscious decision not to do any budget updates or not to do any plan changes because you’re okay with where it’s gonna end up. To me, the point is to cause that discussion to happen, it’s actually very important that that discussion happens because the, if and if you’re not separating the budget from the forecast, then there’s a default answer of no, we’re not gonna make changes or, or, you know, we’re not gonna, we’re gonna intentionally not choose to make that decision.
Glenn Hopper:
As you’re talking through that, like, I’m, I’m just kind of going through all the, just the budget recast, uh, items that I had. So it seemed like the rolling forecast always became sort of the excuse deal for the <laugh> for missing the budget, and then nobody wanted to redo the budget. And maybe, you know, maybe sometimes you didn’t have to, I think about all the frustrating things we had to do to satisfy because we didn’t really separate this is the budget and this is the forecast.
Nate Kaemingk:
Yeah. And that’s, that’s, that’s baffling, right? Yeah. That’s baffling is what, that’s the reason I say that word, right?
Glenn Hopper:
So most of my career was in PE-backed companies, and you know, they’re all, they’re just looking at everything, just straight numbers. They’re, they’re great modelers and they’re, you know, they always have, they’re doing their forecast based on what you have. And then I found that the conversation we would get in all the time would be, well, okay, we’re behind, but this is a timing thing. See, we were gonna close this big client and they’re still gonna close, but it got pushed into Q2 and oh, and we had this CapEx project, but we had to move it up because we ran outta capacity or whatever it is. Because you had budget as forecast, you couldn’t just do the sort of the mathematical, like, this is our trend, this is where we’re headed. Because then you’d have to go change everything and say, well, I know we budgeted this in in June, but we actually pulled it up to end of Q1 or whatever. But I think just differentiating between the two and keeping that clear, maybe that, um, helps some management and maybe some, there’s some unnecessary work when you try to jam the two together and make them the same thing.
Nate Kaemingk:
Yeah, definitely. And, uh, I’ll, I, I love that example because that’s a, I’m gonna shift a little bit to, to market indi leading market indicators, and then we’ll come back to why this is important. So, you know, there’s been talk for the last two or three years with the, the Federal Reserve interest rate and the rate coming down on all this that like, oh, are we gonna hit a recession? Are we not? You know, are we already in a recession? You know, I tend to try and stay away from, you know, global based starting things. And we really wanna focus on, well, is my company’s market getting soft? When money gets tight, the first behavior change is that people will wait longer to make decisions. They’ll, they’ll try to hold onto their money and save up for a little bit longer. The second behavior change is, okay, now we’re gonna go do price shopping.
We still wanna buy the thing, we still need to buy a new car, but we’re gonna try to maybe find it for cheaper. And then the third behavior is, well, we’re just not gonna buy the thing. Now I can use that in consumer behavior, but it’s the same in business. And so it’s, what’s interesting is when you use that like, oh, it’s a timing change, it’s actually really important to update your assumptions on the timing of everything else downstream. Because if the one first big project is delayed and you don’t have a timing model set up in your forecast, okay, great, that $6 million project got pushed back by a quarter. But what happened to everything else that was in gonna be in Q2 right now, it’s not necessarily a one-to-one, but it’s actually really important in a modeling, from a modeling standpoint of how healthy is my, my pipeline.
One of the key pieces of the equation is, well, how long is it taking me to close? Because if I have one or two major projects that are starting to end, uh, let me take this back to your example. If timing happens, I should be, and I have a forecast that’s totally separate from my budget, then I can plug in that change of assumption to my forecast and it say like, well, if, if this one’s changed, and that’s say 30% of our business or 30% of sales, uh, plan, I have to evaluate the scenario of what if 30% of our entire pipeline for the rest of the year is delayed by a quarter? That’s actually a really important piece of analysis to go to. And you wouldn’t be able to do that if you’re not keeping the two separate.
Glenn Hopper:
You know, one of the things we talked about, uh, before the show was using kind of the snapshot from the rolling forecast to, to be the basis for starting the budget. And I think looking back at my career, I mean, there are times where, you know, you know, sort of where the trends are and you’re, you’re, you’re growing fast or whatever the, the big macro trend is going on and, and you use that. But then I think about budget season, and we had the idea of run rate, but run rate was an average of the however many months prior. You, maybe it’s 12 months or whatever that has, there’s no seasonality, no trend information. So take, you know, going away from the, the revenue forecast, just looking at, uh, expenses, that that’s where whether we were, you know, it, it was always some mix of kind of top down and bottom up budgeting, you know, how, how that always works out.
Nate Kaemingk:
Absolutely. Yep.
Glenn Hopper:
But when we give people the run rate, there’s no, there’s nothing talking about the trend and what that’s showing. It’s just, well, here’s the average, this is where you start your budget. So I maybe talk a little bit about really the, a better way by using that, that forecast rather than just doing the average run rate and, and how that could be used to start the, the budget
Nate Kaemingk:
Process. Yeah, yeah, definitely. When I’m looking at doing this, I’m, any number that we’re gonna use and project, and this is where we get into the inferential part of statistics, right? So descriptive statistics would say, my run rate, or, you know, my, my i’ll I’ll use accounts are receivable, because that’s a pretty, that’s a bit of an easy one, right? So we’re all gonna use DSO or days sales outstanding and, and say, well, we have an average of 47 days outstanding. And then you’ll project that forward and you’ll infer what’s gonna happen in the future. Well, the issue is there’s DSO is actually seasonal. If you take a look at it, there’s actually seasonality associated with DSO. There’s quite often, well, it depends on which line of business. So you’ll have one line of business that’s 90 days, the other line of business that’s 30 days.
And if you have a change in mix, then okay, that’s modelable. But the other part of this is, well, if cash gets tight or, or if interest rates skyrocket, which is what we just saw happen, now all of your customer CFOs are having it. You all had this discussion, but all of your customers CFOs and FP and a teams are saying, well, if we just hold onto our money a little bit longer, we can pay down our line of credit and we won’t pay as much in interest. Well, your customers are doing the same thing. And so it’s a huge mistake to just assume like, oh, well we’re gonna use the, the last five years average DSO was 47 days. So we’ll project that into the future. Well, there’s actually been a trend for the last 18 months where we’ve gone from an average of 47 up to say 52 days.
And that’s, that trend is heading in that direction and that trend is actually aligned with interest rates. You know, you know, you can figure out what the market drivers are for that trend, but it’s pretty common to see that, let me take this back to the very beginning, is that any ratio that I’m looking at from the past, I’m always gonna ask the question of like, okay, well is there a way I can figure out which, how this ratio has been changing and use that going forward? So DSO is a really easy example ’cause everybody’s always working on it. But if I see a trend that DSO has gone from 42 to 47 days for the last 12 months, it would be prudent for me to assume it will grow by another five days over the next 12 months. That would just be the trend. That doesn’t mean we think the trend is gonna happen, but it’s important to have that as a starting point.
Glenn Hopper:
So, you know, 12 trying to figure out DSO, it, it is, you know, that’s the, the biggest to me was always the hardest part of the, uh, the three statement model is, you know, you, you know, when you’re, you’re selling and you know what, you’re kind of where you are with your, your collections on your AR and, and everything, but trying to make it all all tie out to the cash flow statement, um, and, you know, it’s one thing, 12 months is hard enough, but even beyond that, say over like 24 months, um, is it, it feels like, I mean, you might as well, I, I mean, and you’re just, you’re guessing at that point, but I, I mean maybe, I mean, I guess there, you know, you have to factor in the exogenous factors and, and all the, all the stuff you were just talking about. So really a two-part question to this one, why is long range cash forecasting, you know, over that extended period so hard and, and then maybe an example of how, how bad it can be if you have that forecast wrong?
Nate Kaemingk:
Yep. Yeah, definitely. So I’ll use, I have another kind of, you know, stick frame company that I’ll use for this as an example. So let’s say we’ve got a company that made, you know, again, $10 million in 2023 and they’ve got about a 30% gross profit. That means their gross profit’s $3 million. And then let’s say their expenses are 2.3 and their net profit is seven, $700,000, right? So fairly typical, you know, 7% net profit margin company and they’ve got 90 days receivables and 30 days payables and inventory is 20% of cogs. So pretty standard, you know, we’ll call it a manufacturing company. If they grow by 10% and we continue those ratios forward, then they’re gonna have a total change in cash of somewhere around $400,000, right? So just 10% growth in revenue and everything else stays the same. The issue is, is if I have a 5% error in my revenue forecast, that would be $550,000.
Well, my total cash position change was $400,000. So a 5% error in revenue is more than my entire ca cash position. Now, obviously that’s not a one-to-one relationship, but it just highlights why even a 12 month cash flow forecast, how sensitive it is to very small forecast errors on the front end. And so because of that, because we’ve spent so much time trying to do 12 month, 24 month cash flow, I’m always looking for the, you guys have done sensitivity analysis. You can use Monte Carlo to do this. You can do it, you know, you can use one ofat, it’s called one factor at a time. You know, there’s all kinds of ways to go ahead and build a model for it. But it’s interesting because of, of how sensitive long range cash is to small changes in the model itself. So what do we do about it?
Right? And so the, the reason that in the first place, , as the CFO, back before I started this company, the reason I started getting into machine learning and inferential statistics and all of this stuff is because I was trying to improve the accuracy of these items so that I could get a better picture of what cash would be down the road. And the reason I cared so much about cash is because if I had nine months preview to a cash problem, that gave me enough time to make structural changes for the company. If I didn’t have 12 months preview to, or nine months or 12 months preview to a cash problem, all of the contracts terms are already out the door. All of the hiring plans are out the door, all of the project bids and the the markup on the projects that’s out the door, it’s too late to make any changes.
And I didn’t, I wasn’t okay with that. I wasn’t okay with that level of, I’ll call it blindness to the future. Another example, so DSO I’ll, I’ll pick on that one one more time. The best accuracy that I’ve seen for DSO is that they’re able to get somewhere around 80 to 85%, um, of your actual receivables in using DSO. If we switch to a machine learning based process, it will get, it can generally explain about 50% of what’s left over. And so while it seems like, well, why do you care so much? You know, keep it simple, Nate, why are you trying to make it so complicated? I’m trying to make it complicated because I’m, I shouldn’t say I’m trying to make it complicated. I’m trying to take the complexity out of the way with the software, and that was why I wrote it. ’cause I was sick of the complexity.
But the idea is 5% error on revenue doesn’t sound like that big of a deal until you recognize that that 5% error in revenue is over a hundred percent of my cash position. And so 5% error is not the issue. It’s, well, what’s my, what’s my percentage of cash that is attributed to revenue forecast error or to cost of goods sold forecast error or to receivables forecast error. And so I want to keep it simple, but to me it’s worth adding complexity when it can explain that much of such a critical piece of the business.
Glenn Hopper:
So maybe this is a good sort of way to wrap all this. So we’re in the middle of budget season. Hopefully people are getting close to being finished. I hope so. Um, with, with budgets for 25 right now, when we’re doing budgets and we’re arguing with department and division heads about, you know, their allocations and what they’re gonna spend next year versus last, it’s, you know, it becomes, there’s the political part of it and then just the math and then how cool your model is. Yeah. Don’t call my baby
Nate Kaemingk:
Ugly. Yeah,
Glenn Hopper:
Yeah. <laugh>. Um, but really what we’re doing here, and this is where when I talk to businesses that are trying to really lean into data-driven decision making and forward-looking CFOs who are trying to be strategists more than just the, the bean counters, you know, not the, um, always looking backward. It’s, yes, we’re reporting on this, yes, we’re forecasting we’re being accurate, but there’s another level of detail if you get this forecasting right, yeah. That you really are adding a level of value to the business. And you talked about this before the show, but if you could elaborate a little more on how a long range forecast can help reveal these structural issues. ’cause I think we talked about material cost sensitivity, misalignment, and pricing and labor costs. These are areas where, I mean, this is a very smooth running finance office who has this forward looking, this is where you are providing value to the company. You’re not getting caught flatfooted because you see this early because of the data you have. So if you could walk through that a little bit, I think that that’s maybe a good reminder this time of year for all of our fp a folks too. The reason for the struggle
Nate Kaemingk:
Here is always, well, how much time and effort and man hours or man years, you know, depending on your situation does it take to build a driver based model. I think you guys have all heard the term, you know, here’s a driver based model for my business. Um, it takes a lot. It is really difficult to do and build and it’s, it just, um, I have, I have real world examples from my, my career where it was, you know, it was a team of 12 people that took three man years and nine calendar months to build a forecast. Like, it’s just, it’s, it’s really hard to build a good one. And so oftentimes the, the decision is, well, it’s too complicated to do that. It’s too hard to go do that. And I think that that’s, that is true unless you have a tool that helps make it easier.
And I think that’s why I’m so excited about this AI ML space. And you know, we’re not the only ones that, obviously there’s other ways to go about doing this, but that’s the advantage. Now I’m talking about it ’cause like, hey, it’s cool, but let’s, let’s look at the ideal state of if you, if you have a driver based model for your company and you’re truly doing a 24 month forecast for the entire business, what that means is I would actually have, okay, I can look at my balance sheet in September of 2025, and if there’s something I don’t like for that, I have time to fix it. Like I have time right now to forecast it. Now obviously there’s, you know, I always, the critics are gonna say like, oh, well your, your budget’s always wrong anyway, so why even bother? It’s like, well, yeah, it is, but you can actually quantify how wrong it’s gonna be.
Like it’s, it’s actually possible to say through sensitivity analysis and there’s a bunch of other methods we could, we’re not gonna get into. You can, you can have a defense for that, but the real reason for it is because, and, and this is what I started running to when a, a 13 week cashflow forecast, it’s very useful from a tactical perspective, but if I am seeing a cashflow issue that’s 12 or 13 weeks from now, I don’t, that’s hardly enough time to fix it. Like, I can go get a, what, what levers do I have available? If I only have 13 weeks preview to cash? I basically can get a line of credit, I can hold onto some cash from my payables, but that’s not really what I wanna do. If I have a 12 month or 24 month preview to my cash, I can start to recognize like, hey, we are, might be an expense reason or might be a cost of good sold reason.
We’re like, we’re running into a cash issue because of how much we’re expecting to grow next year. It’s great that we want to grow, but we’ve gotta switch to 60 day terms, not 90 days. You know, you, you have the ability to, to change things before they happen. And I’ll use the word picture for this one, but it’s this idea of like, I think it’s called upstreaming. I can’t remember the official name for it, but if you keep seeing people, like there’s somebody drowning in the river that’s flowing down the river, you can jump in and save the person, right? That’s a tactical thing. And, but if, if seven people came down you, yes, you could go save all seven, but what you actually need to do is go up the stream and figure out who keeps throwing people in the river. <laugh>. Like right, <laugh>, you, you gotta go upstream, further upstream of your business. And this is one of the key ways to do that.
Glenn Hopper:
That’s the kind of insight that we want. And that’s, you know, that’s what we hope to get out of our, our models. And, um, you mentioned, um, uh, using AI and machine learning. I know you guys recently launched your AI powered forecasting tool. Tell me a little bit about that and what gaps you saw kind of in the existing tools that are out there and, and how your, your product, um, addresses that.
Nate Kaemingk:
Yeah, so our, our tool is a, anybody that comes in with a set of financials, it will build you a forecast from those set of those financials very quickly, right? And then we get into, okay, yes, we need to add some and tweak some accurate tweaks, some pieces of the picture. So every business is different. So we always have to do a little bit of tweaking or training as we’re calling it. We do all of this nerdy statistics stuff that we talked about a little bit, and I apologize if we put you to sleep with that, but think about it as like we’re handling all of that on the backend. The only other examples I’ve seen, I’ve seen a lot of people, like there’s some, I love that it’s happening, but I’ve seen like, okay, well if you wanna get better forecast accuracy, you’ve gotta learn Python and you’ve gotta go get these libraries and here’s some code, or you’ve gotta get like chat GPT to write the Python for you, you know, that’s another way to do it.
But then you’re still learning how to code, you’re still having to learn how to write Python code. Now I don’t have a problem with anybody doing that. I, I love writing code in Python, but that, you know, now you’re kind of stuck on the trade off of, well, it’s gonna take me a year to build a, a machine learning model in Python. The amount of effort it takes, we make that something that’s available today. Um, so I think that the, the, the gap that I’m really trying to solve for is when you get to a certain size that 1, 2, 3, 4% forecast accuracy is a big number. It makes a really big difference. And there are, with Excel, as much as I love Excel, getting an extra one or 2% forecast accuracy on a revenue model using Excel is a lot of work. Like it is, it is just an incredible amount of work to go do it.
It’s fun. I I would enjoy it, you know, that, but you know, some people I’m sure would enjoy it, but you just don’t have time to do it. And so what the, the gap that we saw that we’re going and trying to solve for is we want to take what you’re already doing, but we want to add, I’m gonna, I’m gonna flip the script and say, I’m not trying to get more accuracy. I’m gonna, I’m trying to reduce uncertainty. So best in class forecasting is gonna have 10 to 20% uncertainty on all of your forecast. You know, some might say, well, 5%, but if you’re, if you’re gonna talk to me about average error, then we’ve, that’s a different discussion. But I’m saying like, actual total error, if I can reduce my uncertainty by 50%, I’m going from 10% error, maximum error to 5% maximum error.
That’s huge. It’s a huge amount of increasing confidence of what we can do as a business. The problem is the amount of work it takes to go from, you know, 85 to 90% total, uh, error. Explained to that, getting that extra little bit is an incredible amount of work. Our goal is to eat the complexity. Our goal is to make it so that that’s all handled. It’s less work than it would take for you to go add accuracy, add capability to the models you already have, and it’s gonna do the absolute maximum level of capability that we know how to do.
Glenn Hopper:
This is, I mean, this is so cool and I, I love the automation of, of this kind of, of thinking, and I, I keep going back to like, imagine if every fp and a professional had access to this tool and you know, how, how efficient they would be. But I also, I go back to my DataRobot example because I just, and it’s, it’s almost like, you know, if you’ve ever sat in a board meeting and gotten dragged over the coals by the, you know, that one angry board member who’s gonna pick apart every model that you do. And I think I think about that and I think about auditors, not that auditors in in your forecast, but you know, sort of the equivalent here where I’m the CFO and my team has put all this together and they don’t have that, the, the full skillset to explain it.
And I have to justify the numbers. I’m like the guy who didn’t know <laugh> what, what model I was using in DataRobot and having to justify it. So I guess this is an important question to me because I’m, I’m, you know, I travel around and I’m, I’m talking to people about using AI and machine learning and finance and, and like you said at the beginning, if you are gonna be writing Python or, or to, honestly, I would say I go so far as if you’re gonna be using chat GPT for anything that you’re, you’re doing in your business, you can’t, you have to have, I’m not saying you’ve gotta be a developer or a machine learning engineer, but you’ve gotta have a basic understanding of what’s happening. Otherwise, you might as well be, uh, you know, shaking up that magic eight ball, asking the question, shake up the eight ball and get the answer from that. And I don’t wanna be the guy that’s standing in front of the board and saying, yeah, these numbers are good, that, you know, we put it into the black box and it came out. Yeah. So I think, I mean, I, I guess I would say super cool that tools like this are available, but I almost feel like it’s incumbent on the users. And you, this may go against your, your sales plan here because you
Nate Kaemingk:
No, this is actually great. I, I have a little bit. Yeah. This is great. Yeah.
Glenn Hopper:
So, yeah. So you know where I’m going. Absolutely. I’ll, I’ll stop talking and I’ll, I you No,
Nate Kaemingk:
I totally agree. And I mean, I had that question of like, okay, well hey, we use our AR to forecast ar and here’s, here’s how it does it. And then I had somebody ask me, well, how does that compare to DSO? We have to start with the, the I, I’ll call ’em industry best practice methods and then demonstrate that we’ve added value beyond the industry best practice methods. And so one of the pieces of our tool set that we’ve built in is to help with that. So it’s not just being able to get a good forecast, but it’s being able to get a good forecast, explain the source of, of stuff that’s happening. Well, here’s all, here’s the seven drivers that are contributing to ar. It’s actually these invoices from three months ago. This much of, you know, this much from 60 days, this much from 30 days.
But then it’s also like that board member, we’ll talk about like that board, why didn’t you just use DSO? Great question. DSO got me 83% accuracy and I added 7% by using these other methods. Would you like me to go into how that ha You know, and if he, and if he wants to go into a further discussion, a lot of what we’re building in the tool is the explainability side of things. I think it was Einstein that said it this way. If you understand something well enough, you’ll be able to explain it simply. And so it’s not just, if you want to go learn it yourself, I love it, please do. But you have to recognize that you’re gonna go on this journey of knowing and understanding, but not being, being able to explain it to a board member. And then eventually you’ll kind of get to, I don’t, you know, I’m not drawing the curve correctly, but get to the point where you can explain it simply, we’re we’re trying to do is we’re trying to bypass that entire process. We’re trying to make it as easy as possible for you to, you know, if you get that board member that says, well, why didn’t you use DSO? You can put a chart up that says, well, DSO explained 80%, we had this a little bit left over, we added value. It’s better. Right?
Glenn Hopper:
Yeah, I love it. And this way you can, uh, you were, you were drawing the curve too, and I was saying you, uh, when you were drawing the curve, I was thinking, and you can avoid the Dunning Kruger effect
Nate Kaemingk:
Yes. As
Glenn Hopper:
You’re <laugh>, as you think you understand it and are trying to explain it. And you’re, uh, completely wrong. <laugh>.
Nate Kaemingk:
Yeah, I already went through that for y’all
Glenn Hopper:
Don’t do <laugh>.
Nate Kaemingk:
Yeah.
Glenn Hopper:
So th this has been great and we are, we are coming to the end and we’ve got some questions we like to ask everybody, um, at the, at the end of the show. And the first one is, um, what is something that not many people or that people may not know about you? Yeah,
Nate Kaemingk:
Great question. When I was at Cummins, um, I actually left Cummins and took a sabbatical. So my wife and I sold everything, bought an rv, and we drove around the country for three years. Uh, we actually went to all 48 contiguous states. And, uh, it was awesome. And so that’s not, it’s not that a lot of people know about it because I get interesting responses. 80% of the time people think it’s cool. And 20% of the time people look at me like, what’s wrong with you? Right? So it’s not <laugh>, why aren’t you out there producing? What, what do you mean? You know? And then, but it was amazing. I’m so glad we did it. It’s actually when I started this company because I was on the road driving around in an RV and I had time to think and I was, you know, it’s like, oh, well let’s go do some forecasting. So, um, that was awesome. If anyone’s thinking about doing it, just ask yourself this question. Am I 10 years from now, am I gonna wish that I had done it or am I gonna wish that I hadn’t done it? Alright,
Glenn Hopper:
The next one, I’m, I’m, I am actually very curious to hear what you say on, on this one because, uh, being the, the big nerd that you are, um, <laugh> and I know you’re in, you, you know, you said you love Excel, but you’re also, you’re an r you’re in Python and all that, but what is your favorite Excel function and why?
Nate Kaemingk:
Yeah, so my, my, uh, the one I use all the time is actually index match. Um, which is, seems fairly simple, but it’s amazing how often sequencing data helps figure things out. So index, match, rank, uh, combined all of those together. Now, if I had to pick a more sophisticated one, I’d say Lin Est know, if I’m getting into lint, then I should be in Python, not in Excel. Now it’s, again, if you’re doing, using that to do regression in Excel, more power to you. That’s awesome. Um, I just found that you could only run so many of those before Excel just says, well hope. But Yeah.
Glenn Hopper:
But you’re, I mean, so you still go to Excel a lot. Oh yeah. But you’re, I mean, I guess you’re working a lot more though in, in Python and, or, or I, I don’t know. I mean, I get, even, even doing data science stuff, I still excel’s always a great place to start for me, even for making charts or something, you know? Yeah,
Nate Kaemingk:
Yeah. So I use it. It’s, it’s easier to build a chart. So I think of, um, I think of Excel as the original no code or low code solution. Like it is, it is, and it’s the best prototyping out there. Like if I have to prototype it, like, okay, hey, we need to add this new algorithm or whatever, I’m probably gonna prototype it in Excel and then move over to Python just because it’s easier to put it together and visualize and do all of that stuff. So I use it all the time. So if I’m doing a prospectus, which is a totally different type of forecasting, I’m not gonna try to do a prospectus in Python, I’m probably gonna do a prospectus in Excel. And then the result of the Persec prospectus we might pull into, um, Python. ’cause it’s more of like an ad hoc and it’s, so I like the flexibility of it. Yeah. I love, I love Excel. I am, I probably use Python 60% of the time in Excel 40% of the time, whereas before it was, you know, a hundred percent of the time.
Glenn Hopper:
Are you, um, and I have not explored this a lot. I’m a, I know enough Python to be dangerous and to be a mess and chat. GPT has really helped me there. Yeah. But, uh, are you using Python in Excel? I haven’t talked to a lot of people who are really diving into that yet.
Nate Kaemingk:
I, I have not. Um, I saw the demo, so Excel did a, or Microsoft did a demo where they did a forecast and they asked, um, office 365 copilot to use Python to do a forecast. It was a really cool demo. But I looked at the method that they used and they used ar Arima, ARIMA Max is the methodology that they used. And again, it gets to that like, do you understand what the shortcomings are of that function? And so we, we actually don’t use Arima Max, not because it’s bad at forecasting, but because it doesn’t help you explain how you got there. And so we have a whole different methodology that we took to solve the downstream problem that that’s going to create for you. So it’s a good starting point, but it’s not gonna help you with the, well now I gotta explain how Arima Max works to a board member. That’s never gonna happen.
Glenn Hopper:
How you can get out the charts and show the residuals
Nate Kaemingk:
And seasonalized
Glenn Hopper:
And det, that’ll be exciting. Board meeting,
Nate Kaemingk:
You know, we just found that there’s other ways to get to the, the just as accurate or more accurate forecast. They’re more work, but they do a better job of giving you something we can, that’s explainable at the end of the day. So I haven’t like really dug into it. I’m super fascinated. I’m super excited that it’s there because of, you know, everybody here has probably written VBA scripts that, uh, <laugh>, you know, all the problems that come with that. Um, but no, I, I, I, I probably need to go play with it, but I just haven’t yet.
Glenn Hopper:
Yeah, I’m in the same boat and, and with Copilot I keep thinking, you know, you’ll see the demos and they look great there, but then when you go play around with it, it’s like, well, they’re not quite, I feel like Microsoft can get, they’re showing you they’re 15
Nate Kaemingk:
Four hours in 10 minutes, right? Yeah. So they’re, they’re showing you the ideal state, which is, it’s there, it’s capable, it’s awesome, but it doesn’t mean it’s, you know. Yeah.
Glenn Hopper:
<laugh>, that’s a whole other show. Oh yeah. <laugh>. So. Well, this has been great. I guess before we let you go, um, how can our listeners, uh, connect with you and, and learn more about better forecasting?
Nate Kaemingk:
Oh yeah, totally. So go to better forecasting.com is our website. Um, and there’s a way to get ahold of us there, uh, or find me on LinkedIn. So the website, I think is probably the best way. There’s a form in there that kicks off a thing for our team to pay attention, uh, <laugh>, make sure we don’t, we don’t get too busy writing Python to miss you. So yeah.
Glenn Hopper:
<laugh>. Well Nate, I really appreciate you coming on the show and sharing your insights and, uh, and, uh, wish you the best of luck. Yeah,
Nate Kaemingk:
Thanks Glen. Thanks for having me.
