Very True by Verissimo

Alex Oppenheimer (00:07.288)
So today I'm actually going to do something that should be interesting and engaging.

Alex Oppenheimer (00:21.304)
Today we're going to go through one of my posts from years ago that outlines the financial operations flow.

And while this might seem simple or seem like something that should be simple, anyone who's done it and done it properly knows that it's actually a really extensive process. So years ago, I wrote a post called the FinOps flow before the term FinOps was hijacked by people optimizing their AWS and other cloud spend. And I just gave six simple steps. And so I'm going to run through those steps and elucidate a little bit more about what each of these means. So step one is.

is that you start with building a model. And the key with a model is that it accurately represents what a company is doing. We've all heard the term digital twin in the last few years. The Excel model of a company is the original digital twin. It is a digital representation and a digital model of what a company does. And what a company fundamentally does is they make stuff and they sell stuff. And the mechanics through which they do that is what the model

should represent. Now this might seem simple, sometimes it is, sometimes models are really really simple, a lot of the best business models are really really simple, but sometimes the mechanics are a little bit more complicated and there's always an additional level of detail you can add. Really important to note, more detail is usually not better.

The key in any model building exercises is knowing when to take averages so that you can get the most amount of detail that you need without adding unnecessary complexity, fragility, and just a...

Alex Oppenheimer (02:03.958)
making it unreadable and unusable by a normal person. So when someone sends me a model that has 3000 lines and the company has, you know, $250,000 of revenue and seven employees, it's just not helpful. Oftentimes those are from templates, which again, not helpful. Templates should start simple and then you add the complexity. Also because anyone who's ever spent time in Excel knows well that you can never remove the complexity because one, incorrectly placed control minus and

you throw reference errors all over the model. So less is more when it comes to modeling. And the way that you do that is by taking averages. So I'll double click on what that means when I say taking averages. So for example, I'll see a model that I have three different revenue builds. It'll have small customers, medium customers, and large customers. So the first thing I ask when I see that to a founder is, do these customers actually behave differently from each other? Or are they just kind of a different size? And maybe they're on a different plan.

because that's what the product and marketing and sales team figured out would work best for them. And that might be the case. There's no reason to change that. But from a behavior standpoint, that's really what we care about. If they're running with the sales teams in the same sales processes, getting billed the same ways, pricing is generally the same. It's not like one's usage-based and one's a fixed annual amount. Then

there's no reason to have those things split out into three different mechanical operations from a revenue build perspective. so instead, you collapse them and you take an average. You say, OK, it's good to know the 25 to 75 percentile deviations.

amongst that average and understand what the median size or the average, know, mean size is of those customers. But if mechanically they enter your funnel the same way and they exit your funnel the same way, there's no reason to split them out. It's all about behavior. So if you don't start with modeling, then the whole thing is really pretty difficult and very arbitrary. You're unlikely to get something that's meaningful by

Alex Oppenheimer (04:17.058)
putting your finger in the sky and saying, think we're going to be at $2 million of revenue two years from now. Now, we'll get to that. That's step two of goal setting. But you can't really start there. You've got to start with, how does this business actually function? And I always say the best models start in PowerPoint. You start with a flow chart. You start with, let's move from

how we get customers to how we deliver our product to those customers, how much does each of these cost, what is a point in time, what's a measurement that we can take, what's an assumption that we need to make, and these are the key things that we need to isolate. Our independent variables where we decide, and oftentimes what that ends up being is the marketing spend, we're usually completely in charge of how much money we pump into.

you know, Google or Facebook or Capterra or G2 or whatever else, you know, marketing, whatever else it may be. And we're actually in control of very little else directly. the other thing to really think about is what I call, I mean, I didn't invent this term, but rate limiting steps. You've got to figure out what are those rate limiting steps and oftentimes building the model really allows you to see that. And again, building the model in a PowerPoint where you say, wow, we're,

Struggling here because we just don't have enough a ease on staff yet that are ramped and can really handle all these customers Great problem to have I you know I've seen models where that's the case where they have customers waiting in the pipeline Just to get on the phone with one of their a ease it's a great problem But it is a problem nonetheless that needed to be identified so the model piece again. There's I've made many

Loom videos and other spiels about how to do that. There's really no end. There is no great, know, one and done, just I wanna plug my numbers into a template. It is really an interactive process to accurately represent what is that business and build that digital twin in the simplest, most practical way possible.

Alex Oppenheimer (06:24.002)
The next step is forecasting. And when I think about forecasting, think about the image that always comes to mind is the chalk line. When people are doing construction and they need to figure out where to cut along a piece of wood, they carry a chalk line. And one side sticks into the wood. The other side they pull out. Inside this thing, it's almost like a tape measure, is a string, but there's tons of chalk.

And so they run the whole thing out and then they lift it up and snap it down and it sets a line. Now that line isn't permanent, but it provides a really, really good view of where things are going. And so a revenue forecast is going to be not just where do we think we are in two years, but what does that line or that curve really look like on the top line basis over that period of time? And again, the only way you can get there is when you isolate your inputs in the model and your outputs in the model. Obviously one of the main ones is that that revenue

number and play with those functions until you get to a point where you say, you I think this is realistic. I go through the different steps in the model. see here's what we're going to need in terms of sales headcount. Here's what we're going to need to spend on marketing. And here's how much we're going to need to invest in our product to make this work. And here's how many customer success people we're going to need to support this. And here's how much time we're going to need for implementation. Here's what we're going to need to stand up from a payments perspective to actually make this a reality.

You know, and it could that this is that early stage and then you start looking. Here's what's in our pipeline. You know, here's here's what's in our marketing funnel. And really start to understand that. Get a feel for things. Okay, we think we're going to be at 1.3 million here, 1.7 million there, 2.2 million there. You know, let's say those are the numbers over the next three quarters and then you commit to that. And that's where when you hear about.

you know, projections, that's what projections are, right? They are a forecast. And oftentimes, again, it was companies get later stage, especially public companies, they'll have multiple forecasts that they're going on. They've got the management case, they have the analyst case, they have the worst case scenario, they have the board case.

Alex Oppenheimer (08:30.286)
Usually in the order of the management case is the most aggressive, the board case is the second most aggressive, and then the analyst case that you actually would push to Wall Street analysts is going to be your least aggressive because you always want to be beating and raising and never missing expectations. But that's the next critical point. And then the third step is building budgets. So what I always have to explain and I feel like I have to explain is that budgets are for costs.

Forecasts are for revenue, budgets are for costs. And so the main three categories are sales and marketing, research and development, and general administrative.

One thing that I'm always mind blown is that these outsource CFO and accounting places, they actually don't even know how to do these categories. They'll just have a line for payroll. And part of it is, you know, they'll ask the founders like what goes into what category and the founders don't respond. Well, the answer is why not? It's such an easy thing to answer. Obviously the salespeople go in S and the engineers go in R &D.

You know, the CEO go in GNA and the of HR goes in GNA. Like, so what's the, what's the issue? And the answer is they didn't just ask that question. I had a company once where their accounting, their ex, you know, external accounting firm sent them a Google sheet with 300 questions.

in no particular order, no priority, whatever. And the founder's like, I'm just not going to answer any of these. And part of it is like, there is not a better system for this. This is actually one great use case for AI of like expense categorization, because if you don't get the historical categories right, it's very difficult to project things forward. And so when I build a model, again, you've got all these different line items, and then you just open a new column and you tag them.

Alex Oppenheimer (10:09.88)
This is an S and this is an R and D, this is a G and A. If you want to abstract that up a level, you go, this is a variable expense, this is a fixed expense, and you can actually even, I recently did this in a model with a company, you have one column that says which of those three categories it falls into, and then a column right next to it that explains how it grows. Now is it just flat?

Or does it grow with revenue? Does it grow with headcount? Does it grow with R &D headcount? Whatever that may actually be. But once you've done these three steps and you've really worked through it, which again, this is not a quick and dirty exercise. Really, I would say the most quick and dirty part of this would be the forecasting. Building a budget is annoying. If your model is good and your forecast is good, then building the budget should actually be pretty easy. But doing that on a really detailed departmental level is hard.

I'll say one more thing, which is that there's a certain level of detail in the model itself, which I always will draw a line and say, given what stage of this company is at, given how many other people are involved, I don't care, for example, the details of which channel of advertising spend is producing which type of results at.

the level that we're working on right now at this stage of the company. In part because those things are going to change a lot. And in part because you want to give your head of marketing a very clear KPI of how many leads they need to generate with whatever budget. And it's their job all day, every day to optimize that budget to hit that score.

And so that when you have your model, have, this is how many leads we generated last month. This is how much money we spent. Therefore, this was the price per lead. We're going to use that output number as our input going forward. And that's going to mandate our budget. And if, if our efficiency drops quarter to quarter, again, that's the marketing team's job to optimize that and manage that. So anyway, we've got these three things. We've got the model. That's the mechanic. We've got the forecast, which is something we kind of snap out of the model. Once we play with the levers enough on the different inputs.

Alex Oppenheimer (12:13.902)
And then we have the budget, is, everything from the hiring plan by department to any other fixed expenses that we or variable expenses that we're going to have. So that's the model forecast budget building exercise, which is not that simple, but it's also not that complicated. think one of the biggest issues that people have is they get scared and they run away from it and they don't want to spend so much time on it.

Truthfully, if you're in my portfolio, all you have to do is email me. We can sit together and do this. I can do a bunch of this stuff by myself offline. I work with a couple of other friends of mine who are experts at this, who if you really want to get a lot more detailed and the company's much bigger and has a lot more weight to pull, we can bring them in and they do a great job. But this is the crux of the work. So the next step though, which ...

is a little bit where the rubber meets the road. And if you didn't have this in mind when you built your model and your forecast and your budget, especially really technically and mechanically in that spreadsheet, it's going to be very, very difficult. And the next one is collecting your actuals. Sounds trivial, not trivial. This is also the reason that when people ask me, well, how can you build a model when you don't have the data to go into the model? To which I always respond,

How are you gonna know what data to collect if you don't know what you need in your model? Because when you're setting up Salesforce or HubSpot, when you're setting up Stripe, and we're setting up any of these other tools that you use to manage your users, manage your revenue flows, manage your collections, you gotta know what those outputs need to look like.

and what you care about, and you've got to get consistent definitions, whether it's also in a BI tool, you you're using Power BI or Tableau or any of these other tools, Looker, you've got to have consistent definitions of what these things mean. And so if you haven't sat and figured out these are the inputs that we care about in the model economically to drive value in the business, then you're just kind of leaving it up to individual contributors inside the company to make these really pretty important decisions that if they're not clear,

Alex Oppenheimer (14:22.506)
Not only can it be a huge hassle to get them clear and aligned later, but you're also really likely to just go in the wrong direction. The number of companies that I've met over the last decade where...

They're at 10 million of revenue and they say, yeah, we're going to be at 25 next year. And I popped the hood on the numbers and what they're collecting and how each marketing and sales and engineering and customer success team and person is being motivated. I'm like, you'll be lucky if you hit 12. And you know what? A year later, that's usually about where they're at because they didn't know why what was working was actually working. And even though it's working and you say, ah, shush, don't ask questions. It's working. Like we're not going to mess with it.

That's a really big mistake. You really want to know why things are working. I had a crazy story. Company, I was getting their sales and marketing data. They had an inbound motion and outbound motion. was getting their sales and marketing data in Google sheets that were outputs from, you know, Salesforce. And then this outbound like third party that they were using and, from their marketing tools. And we ran, took that data month over month and ran funnels and each of those two modes of bringing in leads.

and closing deals was reporting separately. then we, know, eight ease would close them. And every month we had like 15 % of our new closed one per our financial system was different, was, it was, it was higher. I mean, they just didn't appear in our pipeline, which again, that's a gift. That's free customers in a lot of ways, but you kind of want to figure out where they're coming from. So you can just do a do it.

Some of it's just intellectual rigor and doing a better job and being more precise and having that lead to better accuracy down the road. some of it might be like, something's working better than we thought. Maybe we should double down on it. But you're not doing yourself any favors by not having your actuals be accurate and in the right format and with the right definitions.

Alex Oppenheimer (16:17.902)
Now, once you've got that in, you got to figure out a way to get it in your model. There's a bunch of ways to do that. You can use Google Sheets. You can use a bunch of other methods. You can just set up a operational program that takes an hour to set up and figure out, okay, these are the instructions of how we update our model every month once the actuals come in. It doesn't take that long. You create drag forwards. move, you figure out how to cut and paste things. a little, you know, I've done this hundreds and hundreds of times with dozens of companies, but it's a really valuable thing to do. So now once you've done that,

You like an example. that's step four. Step five is now refining the model. So it's not, it's two aspects of it. One is you refine your assumptions. say, actually, you know, as we scale up our marketing spend, it turns out our lead cost is going up a little bit quicker than we thought it was. Or it's not, or we're getting more organic as a percentage of our, you know, existing customer base, whatever it may be. You want to refine those assumptions. And then step two is.

you're in the business and you're understanding, is it behaving mechanically, accurately towards our digital twin that is our model? Like, are there people doing what these line items in our model actually represent? Are the equations and the simple formulas from one step to the next representative of what is actually happening in our business? And that refining step,

Usually happens very incrementally. It's usually if it's again, if the previous four steps have done been done properly, it's usually pretty simple and it's usually eye opening. And it can just drive a business forward in some of the most amazing ways possible. And then, you know, my step six is just repeat that. Right? Again, you don't have to build the model, but really you're repeating steps four and step five all the time. And sometimes at a certain point, you got to build a new model. You know, you grow out of your model because the model that you are running as a

You know, one to five million dollar revenue company is going to be different than the model that you're running as a 25 to 50 million dollar revenue company. There's just added layers of complexity. Sometimes there's pieces of it that you want to push down to, let's say the sales team. And sometimes there's pieces of it that you want to pull up into the main model from the marketing team or whatever it may be.

Alex Oppenheimer (18:30.006)
And as those levels of detail become increasingly or decreasingly strategic to the core, you know, CEO decision making level of the business. so this is a big reason that I refer to finance as quantitative resource allocation, because this is the job of every CEO who is a CFO. Like, no, no, it's the job of the CEO to do resource allocation. It's the CFO's job to help them make it quantitative.

You know, someone tweeted today and I saw it and they said, you're a seed stage or pre-seed stage company, you don't need a CFO. To which I replied, you do need a CFO, but the CEO needs to be the CFO. And so I have another piece on this, which I'll share at another point that basically you've got to, as the CEO, you do everything. You're the only person in the company by definition who scales.

But early on, means you literally have to do everything. The shining example of this is I give is, know, Mark Zuckerberg continues to be the CEO of, know, Metta and Facebook. And he was only able to do that because of who he was able to surround himself with along the way to fill all the gaps and deficiencies that he may have had. And that mapped to the level of sophistication and complexity and experience required for where that business was at. And that's a whole different, you know,

subject matter of how to build your team around it, but finance and legal just get pushed to the wayside. The tough reality is, and this is why I launched my program, which is called It's All About Everything, that it's these, so many companies die by a thousand cuts. And there's a strong argument that like the companies that are going to be, you know, multi-billion dollar companies, none of this matters, right? Like they're growing so fast, things are working so well, like...

just keep moving faster and faster, figure out how to pump more into these companies and just get it bigger and bigger and faster and faster. And there's no argument against that. But chances are you are not that company. And chances are your portfolio company is not that company. And there's a ton of money to be made in what I call the second to fifth percentile. Let's say the top 1 % of companies a monkey could run. They're just so good. I don't want to take anything away from the founders and the people who run them, but

Alex Oppenheimer (20:49.966)
They're just so fortunate on their market timing and their product execution that like they can do no wrong. And, know, again, people who are lucky enough to run these companies, what you often find is they're saying, what do you mean? I'm doing this and I'm changing that. we're pushing. was just listening to a podcast with, uh, with, you know, Kareem from, uh, from ramp and he's always discontent and always pushing harder. Well, guess what? He's bringing the rigor. And that's part of the reason that.

You know, they're valued at 22 and a half billion and maybe not 10 billion. Now everyone would be happy with 10 billion. but they're valued at 22 and a half because of that extra desire. so one of the things I always say is that if a company has the ability to put their hands on the levers and have a real, like almost, you know, my friend gave me this analogy once of like sitting in the cockpit of a 747, there's a lot of dials. There's a lot of knobs and buttons and lights. If you know what they are and you know what to press.

you're probably in pretty good shape. Now, whether that means you're gonna get to your destination as fast as possible and as high as possible, or you're gonna get there slower but more fuel efficiently, it's actually the exact same skill set. The ability to drive cash flow break even and the ability to control your own destiny, understand the levers in the business, be able to pull on them, see how it works, refine that process.

It's the exact same skill set that you need to go as fast as humanly possible. That's what efficiency is. Efficiency is what can you do with a little resources and what can you do with a lot of resources. So again, there might be another question about prioritization in terms of the broader organization of what you care about, do you care about your salespeople hitting their exact quota and really nailing the quota calculations versus just making them up and telling people to go as fast as possible. Like maybe it matters, maybe it doesn't, but the ability to do that.

when let's say tough times happen is the exact same ability and competencies that you need to make things move as fast as humanly possible.

Alex Oppenheimer (23:01.414)
my gosh.