Janis Zech: Hello, and welcome to another episode of the RevOps Lab podcast. I'm here with Philipp, and our guest today is Stephen Diorio. Diorio. Is that like the cookie? Diorio. Okay. Awesome. Yeah. I I I butcher every name, so sorry about that, Stephen. Great to meet you. Welcome to the pod. How are you doing today?

Stephen Diorio: Great. Super excited to discuss revenue operations.

Janis Zech: Yeah. I think, you know, we actually wanted to have you on the podcast for a long time. You actually wrote a book about revenue operations called Revenue Operations and published that in twenty twenty two. You know, we had Sean Lane on the podcast. We had Jako on the podcast. Harry Harris, who all wrote books about revenue operations. I think you were the first one to release it. So curious, why did you write the book?

Stephen Diorio: I've been doing go to market strategies for decades. I was one of the cofounders of the first go to market strategy firms in nineteen ninety three. I've basically been trying to convince senior leaders and boards to reallocate resources to grow faster, more efficiently, more reliably. Over the years, I became a Gartner analyst. I saw the rise of tens of thousands of technologies that could help us grow. But fundamentally, organizations don't understand the math. Their accountants can't describe the value of growth investments. And mostly, they can't work together to grow. So I've written several books about how technology transforms a go to market. But if we're gonna get dogs and cats to live together, sales, marketing, success, IT, and finance to work as a team, we need some type of a unifying principle or system. And so I wrote the book primarily to convince people to work together, and understand the math and the finance that really describes how growth assets and investments work together, to drive revenue and cash flow. So the book's primarily to throw at boards to get them to become smarter.

Janis Zech: Yeah. I love that. I mean, I think what you just said has a big cultural component because, obviously, when you work better together, it's a lot more fun. But today, we're gonna talk about the math of growth and really focus in on how to become more efficient, predictable, repeatable across that entire revenue life cycle. So, I mean, today's topic is the math of growth. You know, like, you see it as one big equation. Can you share a bit what you mean with that?

Stephen Diorio: Yeah. Sure. Well, let's start with the fundamentals. Again, I've been around the block. I'm an engineer. And so I think systemically, in the nineteen eighties, if you looked at the investment or spend associated with growth, it would look like a lot of paid media, a lot of promotion, a lot of salespeople in cars, very, very little technology, some in the call center, and there were almost no digital channels. The Internet didn't exist. Fast forward ten, twenty, thirty, forty years, if you look at what people will call the go to market mix, which is not a spend, but the growth investment mix, it is mostly capital expenditure and in support of growth assets. So what I mean by that, a growth asset is a database or a knowledge base. A growth asset is the digital channel systems, which is roads and bridges to our customers. It is content. It is technology. It is brands. The brand is an asset. In fact, it's the biggest asset in many or most companies. It's at least twenty percent of a B2B firm. So we don't understand how to measure and manage those assets. If we were managing a building, a truck, or a factory as our jobs, we would understand the capital investment, the maintenance involved, the value of that building. Everybody takes care of their house because it's an asset. And if you let it — you don't take care of it, it'll grow in value. That's not the case with marketing and sales at all. We don't think about the value of our customer relationships, the value of the data in our businesses and knowledge in our businesses, the value of our brands, which really drive a lot of the conversion that we try to measure in pipelines. So starting with the basis that people don't even understand, accountants who do the math can't even describe the value of the assets and investments we are making. They describe everything as expense. We're starting in a hole. So, the whole notion that the equation for growth requires different math starts with the foundation of we don't even know what we're measuring. Does that make sense at all?

Janis Zech: It makes a lot of sense. I mean, I think, you know, when you think about the term of sales and marketing expenses, right, and that typically in software companies being fifty to sixty percent of the total capital investors, right? I mean, it says it right there, right? It's like it's an expense, but you don't think of it as an ROI calculation or there's something that you invest in that essentially carries dividends over time. So it resonates really, really well with me, to be honest.

Stephen Diorio: Well, unpack that a little bit. Let's just go through the income statement. You have SG&A, which is sales general — all of what is called go to market, and that's a word. My friend Larry Friedman wrote the first book on that. That's a misused word. But all sales, marketing, advertising, customer success, service and support. Most of it falls under SG&A, which is, you know, twenty, thirty percent of the company. The CPAs, the financial accounting standards board here in the United States, ISO globally does not require you or provide you any guidance on what cost to sell is. It's just all a bunch of cost, and it's all lumped together. I can't tell how much of that cost was associated with a customer, how expensive a treatment model is, what a different customer experience costs. It's certainly not fully loaded. If I have a digital channel, I'll usually just measure the variable advertising costs or content costs going into it. I don't value the infrastructure like I would in a factory. So the whole notion that we don't even understand cost in terms of cost to sell — and financial systems don't give us the granularity to break those costs out, load them up, or even provide standards for — you know, when we amortize our computers, there are rules. There are no rules or math that finance has to adhere to to measure cost to sell. So if cost to sell is a term that everyone throws around, you show me a hundred companies, I'll show you a hundred definitions of it, and none of them are right. So sorry to go on that tangent, but we're starting from a really, really crappy foundation here.

Janis Zech: Yeah. I think what you often see is basically in the first years of a company, right, you just scramble, you try to find, like, some kind of sales motion that somehow works, or like, revenue motion that works and just is repeatable, and you try to scale that up. And then if you do, like, a series A or B, then, you know, you get, like, more, like, bigger checks written for your company, and then typically investors will start to require you to do proper reporting. I think nowadays, it probably even starts a bit earlier.

Stephen Diorio: I'm sorry. Even in public companies like IBM, I was on a global ISO task force to convince organizations to put brands on the books. If you drink beer, Miller Coors is a big beer company. The brand is worth forty percent of their business. We tried to get finance to even recognize, when you buy a company, they bought a company, they paid forty cents out of the dollar for that brand that they don't record it anywhere. It's like if you bought a car and you didn't park it anywhere. You're not even saying I got a car. It got stolen. It's worth this much money. So I would argue, yes, a series A, B, C have varying degrees of sophistication. But IBM or Schneider Electric, they don't do it well either. So there's no gold standard here.

Janis Zech: Okay. So obviously — I mean, I assume that, I mean, you have a view on, you know, to break it down, right? Because I think it's like, let's say, obviously, there's the accounting world, but then there's also the reality of if you don't start — I'd say most companies start to put KPIs in place across the different funnel stages, right? So, I mean, just give us some guidance like, when you think of the entire revenue life cycle, where does it start? Where does it end? And then also, what are typical things or typical challenges you see across that life cycle?

Stephen Diorio: Alright. That's great. Let's get into more comfortable territory. I know I got a little esoteric there, but I didn't want anyone to walk away from this conversation thinking that there was a right way to do it. So let's think about the revenue cycle. First, you know, SiriusDecisions created the sales funnel, and then me or a bunch of other people bolted on the bow tie, because we have to execute transactions, build loyalty, and expand relationships. So I think everyone's familiar with the bow tie and, you know, the Wing by Design guys made it popular. But what's missing there is what's called a closed loop process of continuous improvement. And what that means is all the bow tie does is describe how good or bad you did. You know, here's our conversion rates. Here's our customer health score. Here's our recurring revenues. But no one's asking the question, is that good or bad, or how can I make that better? And so one of the points I would wanna make is, yes, marketing at the front of the funnel does some basic metrics, top of the funnel awareness, consideration, preference. Those are good things driven by market research, driven by advertising metrics. They give you a general sense as to whether marketing is getting through. As you go deeper into the funnel, you know, you're gonna see things like engagements and visits and response rates. These are important, but we also — and I think you know — measure what we can measure versus what's important, but that's fine. And at the end of that process, we have this notion of leads, marketing qualified leads that get improved to sales qualified leads, sales assigned leads. And there's math and algorithms, and that's really where people spend a lot of their energy. And the handoff is certainly a point of friction. But I think people overplay that because, yeah, a third of the opportunities that are really gonna close in a given quarter drop out of the sky based on some action that happened twelve or eighteen months ago or an LLM search. So I think we overestimate that our entire go to market is encompassed in thirty to ninety days, and virtually every piece of business we have was generated by a quality website visit. And I literally have the head of a very large company who thinks that's his entire business. When the reality is it's an eighteen month sales cycle. Half their leads come from what is called LLM search, which is invisible. Half of them are coming from content syndication, which came from all sorts of different places, but they don't measure it because it takes eighteen months to see it come up the back end. So I think we overplay the conversion rates. They're easy, and they bias us towards productivity. I'm not saying they're bad. It is an important point to picture, but there's a lot more associated with that. And that then goes down the logical funnel where everybody's KPIs are advances, opportunities, sizes, stage, proposals, and that's fine. But that's just a revenue forecast. What's really interesting is the other side of the bow tie, and this is where chief financial officers are starting to get scared. Because when I sold you a paper clip or a car or a can of soda a few years ago, if I did all of those things for ten dollars, that ten dollars is pretty stable. I can put it at the books. I can record that revenue. The cash is gonna come in, and I can tell Wall Street I sold that. In a SaaS world, in a complex world with cross sell and all sorts of consumption metrics as a service usage, if I book a revenue for a hundred dollars, that can go up to two hundred dollars. It could drop to fifty, and there's twenty five post booking variables that came from the go to market. Poorly written proposal, poorly written specifications, poor rollout plan — that can either double or cut in half the lifetime value of that customer. No one's looking at those things, governance, over terms, setting the right expectations, being realistic about onboarding. So even if the funnel was really, really predictive, we now have all this variability on the back end based on, well, they didn't certify. We didn't roll it out. Consumption didn't hit our targets. Usage and adoption didn't hit. We got the terms wrong. And all of a sudden, we think we're sending out a hundred dollar invoice, and it turns out to be a seventy or a sixty dollar invoice. And everyone's looking for somebody to blame. And the culprits are typically product marketing. They didn't provide the right specifications. Somebody in sales deviated from pricing norms. Client expectations by customer success weren't great. So we've got all these variables that aren't caught in the typical sales funnel that are really driving the profitability and scale of accounts in terms of cash collected. If I had to give you one metric that you're driving towards, it's reliable and predictable growth in future cash flow. Because once you get to that, that means the growth is profitable. That means you collected the money on time, and every it's growing, and it's a metric that Wall Street cares about. So you really — if you're tied to firm value, your metrics are gonna be more sound. If you're tied to conversion rates, win loss rates, you're gonna have all sorts of variables that you're not factoring in that are either causal or not. So I talked a lot there, but I walked you through the funnel. One last aspect of that, most businesses today are basically saying, we have such upside in our accounts in terms of usage, cross sell, that all we gotta do is grow within our accounts. The average complex software company has probably ten or twenty, thirty products to sell. Their penetration is maybe five or ten percent, yet they have no meaningful way to measure share of wallet, or even total opportunity. How many people with faces, eyes, and lips exist in that account who could buy our product? How many of them have I talked to? How many of them are predisposed to buy me? And what is the likelihood that they will adopt or expand our solution? That's a relatively straightforward picture. It's just that we don't even know who those people are. And they're talking to ten or twenty different people in our organization, and nobody can create that picture. So those types of things — you know, we're telling Wall Street, we have so much upside in our accounts that we're going to drive account penetration, yet most organizations can't show me a credible picture of exactly who and where that's gonna happen. It is this whole notion of account based marketing, account based selling, or the commercialization of customer success. Those are nascent concepts. There's really cool math that you could do to create — holy cow, can you introduce me to five people in the account to sell these four products? That's pretty coherent. I rarely, rarely see metrics that reflect that. So on the back end of the bow tie, there's all sorts of messiness, and no one ever looks at — we always look at whether we book the deal, but it's a completely different person who actually collects that money, and it's shocking how different those numbers are. And there's no one to blame because the guy to blame was eighteen months ago when he wrote the proposal. So it's very, very messy. And then finally, the closed loop. What did all that cost me? Who is adding it up to say, what was my win rate? What was my profitability? Were my plan assumptions right? I'm a huge believer in the scientific method. That whole funnel, the budgets associated with it, the staffing, the treatment model. I think everyone knows what a treatment model is. Call them five times. Send them flowers on the anniversary date. Make sure you meet them face to face once a year. Costs a lot of money. That's a treatment model. You know, these come out of Salesloft and things like that. Every treatment model has a cost. I treat my wife very nicely. That's a very expensive treatment model. My accountant, I send him a card at Christmas or a digital email saying thank you. So all these customer satisfaction, customer experiences — everyone talks about a great customer experience, but no one says what it costs, and no one says, is it worth it? It's really worth it with my wife, sort of worth it with my accountant, but you gotta put a number on it. You know? Who are you gonna buy the flowers for? Whose budget is that coming out of? And at the end of the day, if you buy the flowers, is there gonna be an outcome? You know, happy wife, happy life. So none of those things are answered. We use the word customer experience all the time, and I rarely find a finance person who can say, it's a hundred dollar customer experience. They're gonna say a good customer experience. So, honestly, think about the budgets associated with personalization. Who in the world isn't personalizing their customer experience? That is a huge capital investment that falls in a bunch of budgets. And is anybody adding that up going, holy cow, that's a lot of money, to make somebody marginally more happy when they probably would have bought from us anyhow. And so this is where I kinda go nuts, and it all gets back to, you know, we don't even have the math to describe these things. So I talked a lot there, but I did hopefully walk you through the bow tie.

Janis Zech: Yeah. No. I think totally understood. Like, I just wanna point to a few things that resonate with me and also triggered me in some sense. So I think one thing that is super important, right — like, I think the overall goal of every sales organization is to create repeatable revenue. Right? I think that's just like a fundamental principle that everybody needs to understand.

Stephen Diorio: May I do you mind modifying that to say reliable revenue if you're in an acquisition —

Janis Zech: Reliable. I get it. I buy it. I get the spirit of what you're saying. Yeah. Okay. Reliable. Reliable revenue generation. Right? So, basically, in a sense where you can say, okay, you know, basically, in Q1, we can make plans for Q2 or even Q3, Q4. Right? And you can basically prepare for that. You understand sort of, like, what are the input metrics, what are gonna — if you know the input metrics, you understand what the output metrics will be and so on. So I think that's in general like a good framework to put into consideration here. But then right. So I think — and then if you go further down and look, for example, into a CSM organization or an SDR organization or whatever, right, these are managed by different people who don't necessarily need to look at the overall equation of the entire business. They need to look and understand sort of like, okay, what can I do? What is my —

Stephen Diorio: And that's a brilliant point. If you have great net promoter scores that is highly correlated to a very profitable, repeatable account, but it's not on their scorecard, and it's not being rolled up into a dashboard — I think that's one point you're making, and it's an incredibly good point.

Janis Zech: Yeah. Yeah. I mean, like, basically, what I'm trying to get at is you have different kinds of metrics. But I think there are metrics that are relevant for the leadership team. There are metrics that are relevant for the extended leadership team, let's put it like this. And then there are metrics that are very different that are relevant for the frontline manager, the director level, where they are more on a tactical level. They're operating more on a tactical level. So what they are looking at mostly is sort of like the signals that let them make the next decision. But it's very hard for them to kind of zoom out and look at the overall equation of the entire business because you're just like a small cog, and they just cannot really influence the entire equation.

Stephen Diorio: I think you said four or five smart things, and I'd love to reiterate how smart you are. The smartest thing you said is the thing you didn't say. Accurate. An accurate sales forecast is not an objective. Because if you want an accurate sales forecast, I'm gonna sandbag and give you a very low one because I know I can hit that number. So I like the fact that you focused on reliability and repeatability. I also like the fact that you said I'm doing cause and effects. Actions at the beginning of the revenue cycle lead to outcomes at the end of the revenue cycle, which should inform how well I'm doing or what I could do better. A caveat there is it is not one leads or website visits, but it's many people showing up on time for QBRs, people doing training on time, net promoter scores, fixing problems quickly. And so what I really like about what you said is if I'm gonna have a happy customer, it's a lot of people in a lot of different cogs and places doing the right thing and aggregating those things is really good. Couple caveats there. One, one of the things we never explore is what our assumption was. You know? If I wash the dishes and made the bed and brought flowers home, you know, I'm assuming that my wife will be happy, and tell her friends she's happy. Is that a good assumption or a bad assumption? So a lot of times, we don't say like, we have to have an NPS score of nine in order to retain the account. Because if it goes to eight, our retention rate will go down fifty fold. Good assumption, bad assumption, but no one ever documents the assumption. And I think we all know the scientific method. Establish it. Make a guess. Try something, see if it worked, and adjust. So no one's ever questioning our lead scoring criteria or, you know, different treatments or how many times you should send a gift or how many salespeople in a given market or how many times somebody's gotta see an ad. There's this whole response function, which is poke them five times, they buy. Poke them four times, they don't. You know? So maybe four point five. And so one of the things — it's related to what you said, but it's good to measure the inputs. It's good to measure the outputs, but it's also good to write down the darn assumption of, you know, what the inputs were supposed to be, what's a good input and what's a good output. Because if I don't write that down — that's what budgets are set on. That's what training is based on. That is what the resources are applied to. So I'm just observing. People should write those things down or at least write their guess down. You know, we need five people in any market in order to cover the market effectively. Good assumption, bad assumption. But you could test it if you write it down. So I'm sorry for going down that road, but it really builds on what you're saying.

Janis Zech: It's a great point. I think, like, one problem that I just, you know, see often is like, okay, you need to do something for, like, a couple of cycles until you actually know what is good or what is bad. So you're kinda forced into heuristics quite often because those assumptions also change. Right? Like, if I look at the last twelve, twenty four months of the SaaS industry, right, like, the amount of times things have changed, right, like, new things being introduced, massively changing the environment, changing guidance, and so on. Like, it's very hard to basically, you know, make an assumption in the beginning of the year and then kind of go back to it at the end if you have such a dynamic environment. But I cut Janis off. Sorry. Yeah. So one question I have here is — so, obviously, just playing this back to you. Right? So the top of funnel is fuzzy. Right? There's the dark funnel where it's actually really hard to measure. I think the understanding of brand, the understanding of touch points, the understanding of what happens the six to eighteen months before somebody actually comes into your funnel is often unmeasurable. It's actually really hard to calculate. So I think that's just a thing that I think is worth thinking about because it actually is something that every company has. And we see it a lot at Weflow, actually. We have people that we interacted with eighteen months ago and then suddenly they come in to book a demo and then they buy two months later. But it was actually a very long journey. But the touch points we can measure are extremely short and so actually the conclusion you can draw from that is entirely wrong. So I think that's one observation. Then the other observation is that the whole servicing, onboarding, expansion, renewal cycle and the measurement around that is still very nascent. Would you say those are the areas where most teams struggle, or is it mid funnel when you go from new logo into conversion? Is that the more challenging piece? Because I feel like that's actually a lot more controllable and measurable versus the top of funnel being very fuzzy and often it's kind of the science and art of marketing. And then the success model is actually very measurable, but maybe underinvested and only had a real maybe like awareness in the last two or three years where NRR and GRR have basically become more important in the investor mind. I'm curious how you see those.

Stephen Diorio: I love that, and I wanna break that down because you looked at three vectors. One was time — backwards looking, forward looking, behaviors and customer success that would predict the renewal will or will not happen. Backwards looking, top of funnel. Two, you said dark funnel versus — I'd say different places. Some things, we just don't know. Some things, we just gotta look in a different place. And so let's look at time. Yes. There's a lot of dark funnel there. For example, what is the number now? Ninety percent of the purchase process happens before they even engage you. So our top of funnel is even blind. Right? Ninety percent has happened before. Now couple of things. One easy thing to do is people tend to lock in on quarterly or monthly measures. Go back and look eighteen months in advance. You might find that a third of your opportunities came from something you did a long time ago. Those are findable. Another cool thing, you can use all sorts of signals now in the last eighteen months to say, what are people prompting on? You know? Where are they? Did they go to a competitive website? So, you know, I would say, absolutely, dark funnel exists, but ask yourself different time frame, different places. What if I was, you know, using something like Demand Science to monitor keywords across all sorts of places off my website? Those are different places that may give me a little bit of a signal. But I also love what you're saying, which is it's backward looking is fine. It's history. It's data. But forward looking is also important. You know? If you talk to someone in customer success, they'll tell you, you know, I can almost guarantee based on the behavior and signals I'm getting from this client that they are not gonna renew. They don't show up to meetings. Adoption is declining. They're not using all the product capabilities. So forward looking signals that say, well, half the people haven't been trained, and they're barely even using all the good stuff in our product that we sold them. So I love — look, try to push forward and get forward looking signals. And another thing that's imperfect — try to go back and in different places to try to get signals and combine them. And even guess that people are going to our competition. Guesses are good as long as you write them down and look for sources. I mean, so much of what we do is gut feel. And I'm totally acknowledging that a lot of stuff is really hard to find or expensive to find, and you have to do that. But you made some really, really good points. And I think forward looking signals are available now in different departments from different signals, including — think about product telemetry data. Our products can tell you whether someone's gonna stick around. And, again, this isn't rocket science. It's just being diligent. So I think that's good. Here's another thing. I used to run the Forbes marketing accountability practice. CMOs cry or whine, depending on your preference, that nobody respects them and that no one appreciates their value. But the reality is it's really difficult to measure. There's a brilliant man named Frank Finley who runs the marketing accountability standards board, which is a bunch of academics and scientists who are looking at that. You know, you talk about the funnel, and you have to live in the funnel of the bow tie. But if you really think about it a little different way, all everything we do — whether it's drive a car, go on a sales call, do an ad campaign, build a channel, write a white paper, run a cadence — any action we take to grow or investment drives only nine bit outcomes. So you talked a lot about outcomes, MQLs, conversion rates, sales, but they're all — we're really trying to do is change customer behavior in nine specific ways. Will they buy more? Will they buy faster? Will they eat more? Will they pay more? Will they stay longer? Will they say nice things about us? So it's really funny. No one ever looks at that as an outcome but price sensitivity. If I do a lot of stuff like hire a fancy model or spend a lot of money on my advertising, if people are willing to pay twice as much, that paid off. Are we asking — if we're likable, will they refer us? Are we looking at share of wallet, buying faster, you know, winning more, buying bigger things? So it's funny. If you just think about it as a human customer and you're poking and prodding them and throwing money at them or resources, are they paying more? Are they staying longer? And so a lot of times people don't look at those outcomes. Price sensitivity — people think that's a great sales job. IBM basically says if it's a risky deal and it's a tie, we win. Or, you know, a luxury brand — if it's within twenty percent, they'll pay twenty, thirty percent more for me for the same thing, not forty percent. What makes that happen? Price sensitivity is fantastic. So I love that because every one of those actions has a business outcome, whether it's volume, share, lift, velocity, margins, cost to sell, or even future option value, which is, wow, every time this guy brings me a really cool product, an idea, it's great. So if he brought me another idea, I'm gonna buy it. I mean, quite frankly, you know, there are certain companies like Apple, or even Miller Coors who can say, I know I sold you dog food last month, but now I'm in the car business, or now I'm in the tractor business. And they're like, oh, okay. I'll give it a shot. I mean, apparently, Miller Coors has such future option value that if they rolled out a line extension for, you know, anything, you name it, you know, Latvian gluten free beer, they're like, sounds cool to me. I'll give it a shot. And so if you're rolling out a line extension into cybersecurity or content management and it's adjacent to your space or it's an extension of your product, their receptivity to it is huge. You know? Like, I'm not buying dog food from Oracle, and I'm not buying a car from Miller Coors. But, you know, they'll buy anything from Apple.

Janis Zech: So I think it's actually really interesting what you say because — so we are, right, like, a real example of this where we see touch points matter a lot on top of funnel. Right? Like, what we see is, like, you know, after twelve to eighteen touch points, people start to know us. They're actually aware of what we do, and they start to actually have an interest. And I think it's very much like you center basically a brand around creating value to your core buyers in different ways that are totally, you know, unrelated from your product, so that at some point they drive awareness. And I think this is such a big topic that often the CMOs cry because, well, that is kind of the reality that nobody understands. It's very hard to measure. But you look at, for example, Revolut, one of the most successful neo banks in the world. They invested heavily into brand now. They have tons of product and they're rolling them out. And I think the best companies in the world get that right at some point in time. Apple is an example, Revolut is an up and coming example. And I think it's actually the reality in SaaS as well. When you look at some players in our world that have done similar things. But look, I think we could continue on this for a long time. We actually need to leave because I have another meeting coming up. A quick question before we let you go. I mean, obviously, you wrote a book on RevOps. Right? You've been in that space for a long time. Is there any other book you would recommend outside of your book, which we'll obviously link to in the show notes?

Stephen Diorio: Yep. Absolutely. If you haven't figured it out, I'm a big believer in the scientific method. And if we have assumptions that define the bow tie, we have to evaluate and adjust every quarter, every day, every week, every call. That's called the scientific method. Document your assumptions, look at the causal drivers, and adjust. And the number one word I have is even if you're measuring the right things, who is discussing and adjusting? So the book I would recommend is my favorite business book called The Goal by a physicist named Eli Goldratt. And it's actually a book about a factory, but I think it's the best business book ever written, and it's really just a business book about the scientific method, which is — in the book, he assumes automation's gonna work, but he realizes that actually it's a number of other problems that have to be solved, and they adjust it and they learn as they go. So The Goal by Eli Goldratt, I think is the best business book ever written.

Janis Zech: Awesome. I think this is the first. I think we've never had this recommendation. Really, really appreciate it. Stephen, thank you so much for coming on and everything you've done in this space. So really appreciate it.

Stephen Diorio: Great. And I'll send you a couple of articles with some of this math so that it's not esoteric. And I really enjoyed hanging out with you guys.

Janis Zech: Same here. Thank you so much.