In order to begin building a forecast for our business, let’s dig into the most important assumptions about our business — like how much customers will pay for our product — and make a reasonable guess as to what those values might be.
Let’s just start off by making one thing clear – almost no one understands how to forecast the future of a startup business. If anyone thinks there are these genius MBAs with some startup oracle of knowledge that are running stats and probabilities to get these scientific models for the future – they’re wrong!
Everyone guesses – and generally speaking – everyone guesses wrong. And that’s OK.
Startup finance is built around making a series of educated guesses about how things might go. We make assumptions for how much customers will pay for our product, how much it will cost us to acquire a paying customer, and how many times they will keep paying us over time.
We make all of those assumptions to get us started. Then we find out we’re totally wrong. Then we make more adjustments. Then those are wrong too. Then we keep adjusting until eventually, our numbers are right!
That’s precisely what we’re going to cover in this Phase. We’ll also demonstrate how just a few main assumptions (like the cost of acquiring a customer) are really all that matters.
In order to better understand assumptions, let’s look at an example.
If we assume that our average customer will pay $40 for our product, do we really know they will? Of course not! We probably haven’t even started our business, so right now we are just assuming $40 might be a correct number.
Think of assumptions as a placeholder value that we will use to begin building a forecast for our business. In most cases, our startup probably hasn’t been around long enough to know whether any of these values are accurate – and that’s OK.
For the time being, just know that all we need to build our first financial model is to know what the assumptions are and then make a reasonable guess as to what the values might be.
In the early years of a startup, we’ll spend more time forecasting our business than tracking our finances. That’s because in some cases we won’t even have a business launched quite yet and therefore we’re working on a theoretical forecast for what will happen when we finally do launch.
A financial forecast is just what we’d think it is – a guess about how the business might go.
Now of course, we’re freaked out that we’ll make bad assumptions and the forecast will be based on numbers we can never hit. Thereafter our startup spirals out of control and the entire planet gets struck by a giant meteor and we return to the era of the dinosaur! (I’m not really sure how dinosaurs get re-introduced to our biome in this scenario, but let’s just run with it Jurassic Park-style.)
The reason startups don’t understand forecasting is because they tend to think it’s based on information we have on hand right now. Forecasting isn’t intended to predict the future specifically. It’s intended to provide a working model to show us what happens when different assumptions we’ve made will change.
So, let’s just think of our future forecasting as a simple “if/then statement”. “If” our costs per product are too high “then” we’ll need to increase our retail price to maintain the same margin. Our forecasts are just us moving all these levers until we find the right balance of revenue and costs for our business.
Assumptions and Forecasts go together like peanut butter and chocolate. Like Run and DMC. Like Ninjas and Pirates. Like… well, point being – they work well together!
Our assumptions allow us to make really specific guesses about things like what a customer will pay or how much it will cost to produce the product. Our forecasts simply take those assumptions and calculate what will happen if those assumptions are true.
Here’s an example of where just 2 assumptions can tell us exactly how much revenue we can forecast per month:
“If” those assumptions are true “then”…
Forecast (automatically calculated): Each month we’ll add $400 in revenue. ($40 per sale multiplied by 10 customers)
Notice how we’re not “guessing” how much revenue we’re making each month. We’re making assumptions that lead to a forecast in revenue. By focusing our efforts on the assumptions (like how much the product will sell for our how many customers we acquire) we can let our forecasts simply be a calculation.
Once we learn how distilling our business into assumptions gets us closer and closer to numbers that we can actually understand and predict with more accuracy, this whole business of guessing starts to become a heck of a lot easier!
With that said, let’s first dig into how assumptions work, and then once we have a handle on that, let’s see how those assumptions can build a forecast for us.
Every startup financial model is based on a handful of “assumptions” which are the costs and values we think are going to be true about our business.
Some of our assumptions will likely be:
Those are just a few of the most popular assumptions, but there are many. We’re probably thinking “How the heck could I possibly know what any of those values would even be?” and that’s the right question to ask! The answer is this:
We don’t know exactly what any of these values are, but we know exactly which assumptions we need to have answers to in order to build the forecast.
That’s like saying “We don’t know if this recipe requires a pinch of salt or vat of salt – but we know it needs salt.” In this case, we don’t know whether or product will sell for $20 or $40, but we know there will be a price for our product.
When we distill the formula down to specific values that we know we need to prove out (like the amount of salt in this recipe) it changes our concern from “what the hell is this recipe?” to “I know the recipe, now let’s just monkey with the amount of salt.”
So right now, let’s just focus on how assumptions work wonders for making our lives easier. Later on, we’ll focus on what actual values to use and how to make some sweet ass guesses!
We would think that if we’re going to build a financial model, it would have to be pretty damn accurate. I mean, we’re talking about finances, right? We can’t turn this thing into Enron meets WeWork!
As it happens, we only have to be totally right about a few major assumptions. The rest, well – they sorta don’t matter by comparison.
Allow me to illustrate this point in the form of a rant.
begin rant
Startups get super distracted by trying to forecast every part of their business. Most of it is a wasted effort. If a startup can’t sell a product for more than they paid for the product (including marketing) - there’s no business there!
We’d be hard-pressed to find a business that sells dollar bills for 99 cents (re: not sustainable!) and is going to be around for very long (ignore Uber and Tesla). The assumptions of a startup need to be set up so that if they hold true, the startup can operate profitably at some point in the (hopefully) near future.
This doesn’t mean that operating expenses and fixed costs don’t matter – they do. But it’s rare that a company can find a way to sell at a loss and still come out ahead because they “nailed the forecast on office space costs in Year 3”.
/end rant
It’s this simple – there are a handful of assumptions that will make or break our business. The rest of the assumptions can be right or wrong, but it won’t matter if the core assumptions that drive our business model don’t hold up. Instead of listing every assumption that doesn’t matter let’s focus on the major assumptions that matter.
Although it can be said that every business is a little different, the truth is most businesses still have to sell something to a customer at a price higher than what they paid. Within this universal truth lies a common set of major assumptions that nearly every startup uses to develop a successful financial model (or an unsuccessful one – the model is still the same).
We call these the major “Assumptions that Matter”.
While there are TONS of other assumptions that will also matter in different capacities, we’re going to focus on the 3 most typical and important that can make or break the business.
The 3 most important Assumptions are:
When we’re done with this mystical math we’ll be able to determine how much revenue we’ll generate and how much margin will be left over to pay for operational expenses like salaries, rent, and those chocolate Pop-Tarts for the break room. (Side note, as a grown man I existed on nothing but Chocolate Pop-Tarts and Beef-a-Roni until almost 30 years old).
We’ll spend most of our time learning about these three and how they interrelate. We’ll also build some simple business models based on these that we can use to learn about assumptions and add our own later.
Our first step is to construct a series of assumptions that tell us how many paying customers we will get through the door. To be clear – there’s no way we can know how many customers will actually pay us until we get our business up and running. If our business is already operational, that may help seed some data to get us going, but ultimately this exercise is intended to tell us what the moving parts look like – not the perfect final answer.
The Goal is Conversions
Our goal in this exercise is to determine how many “conversions” translate to a paid customer. A “conversion” is just a general term to mean that some potential customer has expressed interest in our product. We can modify how we think of “conversions” to include things like leads, sales, trials, installs, test drives or whatever translates into sales volume for our business. What’s important is that we can take this final value (we’re using “Total Conversions” in this example) and multiply it by our average sale price in the next step to generate a total revenue estimate for the business.
In order to get to “Total Conversions” we must build up a list of assumptions that lead to that number.
Let’s assume that our business relies on driving traffic to our website to generate sales. In this case, we’ll assume that sales happen directly on our website. If they didn’t, we might add some additional assumptions to determine how many customers convert later in the process, but ultimately, we want to know how many paying customers we will have.
Here’s the list of assumptions we’ll use. We will input values in bold – the rest is just a calculation.
How many paying customers will our Web marketing create?
Assumption | Value | Explanation |
Budget | $1,000 | Proposed budget for this period |
Cost Per Visitor | $0.50 | Average cost per visitor (aka “cost per click”) |
Total Visitors | 2,000 | Total Visitors to website ($1,000 / $0.50) |
Conversion Rate | 3% | Percentage of “Total Visitors” that will convert to a sale |
Total Conversions (to sale) | 60 | Total Number of sales (2,000 * 3%) |
According to these assumptions, if we spend $1,000 on marketing, we’ll generate 60 paying customers. Let’s walk through each of the assumptions one-by-one to figure out why we chose these specific values and how to modify them to our own tastes.
What we’ve done is start with a budget of $1,000. That’s arbitrary. We can drive that number based on how much capital we have to spend or just use a placeholder for now to see how the math plays out. We’re assuming some amount of money will be required to buy ads to drive traffic.
We’re going to be buying ads via Google Adwords. Adwords can predict for us how much an average click will cost so that we can get a placeholder value. (Take a look at Google’s Keyword Planner tool for more info on this). The “Cost per Click” will be a really important metric going forward because our budget is relatively fixed – but our cost per visitor can swing heavily in either direction.
There’s no assumption here – just math. We divide our Budget ($1,000) by our Cost per Visitor ($0.50 cents) to get our Total Visitors (2,000). This tells us that if we spend $1,000, we’re going to have 2,000 visitors to our site. We still don’t know if any will turn into paying customers – that’s the next step.
OK hold up! Before we move forward, look at what we just did. We used two assumptions (Budget, Cost per Visitor) to drive our Total Visitors. Within those two assumptions, one we have some control over (Budget) and the other we’ll have to try to manage toward (Cost per Click).
There’s an important difference when we’re building all of these assumptions around those that we have some measure of control over (like Budget) and those that may get out of hand (like Cost per Click). Building and managing assumptions as a startup team is all about saying “here’s what we think we can control, and here’s what we’re pretty freaked out about!” Not all assumptions are created equal.
Now that we know we’ve got 2,000 Total Visitors, we need to build an assumption around how many of those will turn into wonderful paying customers.
“Yay! We have 60 paying customers!”
That’s right. In this model if our assumptions hold true, $1,000 of ad spend will generate 60 paying customers. What’s more important is what we can do with these assumptions going forward.
Instead of saying “I don’t know how many customers we could possibly get!” instead we can say this:
All we have to be concerned about is getting to a $0.50 Cost per Click and a 3% Conversion Rate. The rest is just math. All of our time will be spent trying to manage these assumptions.
Earlier we mentioned a critical assumption known as “Customer Acquisition Cost” (CAC). We’re going to get asked about this about a billion times if we talk to investors, so let’s make sure we know what we’re talking about here.
CAC is the total cost to acquire a customer.
That’s the simple version. It gets a wee bit more complex when we start asking, “Well does that mean a lead, a trial, a paying customer – what?” This can sometimes be interchangeable, but for the sake of this discussion, let’s talk about the cost to acquire a paying customer.
We spent $1,000 in our budget. We got 60 Conversions to a sale. Our CAC is our budget ($1,000) divided by our Conversions (60) to equal $16.67 “CAC”.
When someone asks, “What’s your CAC?” they are really implying “Are you spending more per paid customer than you’re making?” or “Is your cost to acquire customers really high compared to how others in your industry are doing?”
We’ll want to pay attention to our CAC because it directly impacts our ability to keep spending more money on marketing. If our CAC is $16 and our product sells for $9 – we’re in trouble!
Chances are there are some steps in between those assumptions. For example, our “Conversion Rate” may first represent the number of people who became a “Lead” (maybe they only entered an email address to subscribe to our mailing list). Then later some percentage of those people would turn into a “Sale”.
We might modify our assumptions to look like this:
Assumption | Value | Explanation |
Total Visitors | 2,000 | Calculated from the previous model |
Conversion Rate (Leads) | 10% | Percentage of Visitors that become “Leads” |
Total Leads | 200 | Total Leads (2,000 * 10%) |
Conversion Rate (Sales) | 20% | Percentage of Leads that become Sales |
Total Conversions (Sales) | 40 | Total Sales (200 * 20%) |
In this model we began calculating our Leads first from our Total Visitors. Why? Because we may have more activity initially around leads (people sign up before they ever become a Sale) and therefore we want to isolate the lead conversion number first, so that we can focus on the variability of the conversion rate to sales later.
We typically add more assumptions so that we can focus on a more detailed number.
It’s probably harder for us to understand how 2,000 random visitors turn into final sales. But it’s easier to understand how 200 Leads convert to a Sale – because there are less of them, and their intent is much higher. We have a lot more data on them (like where they came from, where they entered their information, or what they told us) so that we can manage that next assumption (conversion to a sale) much easier.
Our model may be very different than what we just walked through. Maybe we’ve got a consulting firm that doesn’t have a website and does all direct sales. That’s fine. All we’d need to do is to modify the assumptions to be relevant to what drives more sales for us.
The key pieces will always be this simple: “If we put this amount of effort forth (marketing budget, outbound calls, number of skywriting airplanes) how many prospects will that generate? And of those, how many paying customers will we generate?”
That’s it. As we discussed, what matters is that we have a series of assumptions to determine how many paying customers we might have. Once we know how many paying customers we have, we can move on to the next step to tell us how much they might pay us.
Now that we have some assumptions set up to tell us how many paying customers we might get, the next step is to figure out how much they might pay us. This begins the fun and fanciful journey of determining our LTV – or Lifetime Value.
We’ve likely got two questions at this point:
The answer to the first question is that there is no answer. It’s one of the mysteries of life that we need to appreciate for its own wonder without trying to peer into the soul of its origin.
The second question – well, that’s what this whole section is dedicated to. So, unlike my first BS answer, let me redeem myself with a ridiculously great answer to the question that really matters - how much people will pay us.
The reason LTV is so important right now (even though we have no idea what it will ultimately be) is that it drives so much of the rest of our model. For example, if our customer – “Joey B” – buys a single pizza from us for $15 and never buys again, we know we can never spend more than $15 (really, a lot less) to acquire him as a paying customer. We can already see where this would impact our maximum CAC from the previous step.
But if our friendly pizza-loving Joey B were to buy 3 more pizzas – we’d have $60 of total revenue – which would give us more profit margin to play with to acquire him. So, it’s not just about a single purchase that drives our business model, it’s about the entire value of all a customer’s purchases.
In the event it’s not obvious, that’s because we tend to only pay to acquire a customer once. That doesn’t mean we don’t have additional cost in bringing a customer back again, which we may also add to our total CAC, it just means that when we try to build a forecast for our business model, we want to focus on keeping our CAC lower than our LTV.
In order to calculate the total value of a paying customer (LTV), we just need to know the average amount they will pay us, multiplied by the number of times they will pay us.
Average Order Value (AOV) | Recurrence | Lifetime Value (LTV) |
How much (on average) does a customer pay us? | How many times do they pay us before leaving or within a particular time frame? | Average Order Value multiplied by Recurrence = LTV |
Joey B buys a pizza for $15 | He buys 3 more pizzas this year | He spent $60 in total with us |
By now there are likely a few frequently asked questions that may be looming already. Let’s try to pick those off before we dig in further so that it’s not distracting.
Assuming we’re good with those concerns, let’s explore each of the moving parts that get us to LTV.
Our first step is to determine our Average Order Value, which means what the average customer spends with us per transaction. For our purposes here, we’re going to focus on how much they spend in their initial purchase and then multiply that across the number of times they recur. That’s just because for now we’re still guessing at how much they will spend over time, so we’re trying to lock in a few variables.
Here’s how we calculate Average Order Value:
If this month we generated $2,000 in sales among 100 first time customers, our Average Order Value (AOV) for a first-time customer is $20 ($2,000 divided by 100).
Assumption | Value | Explanation |
Total Sales | $2,000 | Total sales from new customers |
Total New Customers | 100 | Number of brand-new customers |
Average Order Value | $20 | Average of 100 customers spending $2,000 |
Again, over time we will learn more about our average order value, especially with businesses that have a great deal of variance per customer, such as retailers. Regardless, the AOV will still reveal itself over a period of time as an average. For now, we can just pick a single value with the intent of modifying it over time.
Recurrence is the number of times a customer will purchase our product again. Most businesses have recurring customers, and many business models rely on recurrence in order to get the full value out of their customer (and pay for the costs of servicing them)
Netflix is a great value at less than $15, but if every customer only paid a one-time fee of $15, the business would die a quick and painful death! Therefore, Netflix relies on customers to keep paying month over month (recurrence) in order to cover their initial costs to acquire the customer, and of course the ongoing costs to pay for content and the operating parts of the business.
Our customers don’t have to pay on a monthly subscription basis for us to consider them to be recurring. The recurring aspect can happen in any time frame. What’s important is that we know how many times in the given time frame they might recur.
For our purposes, let’s use a one-year time frame. There are plenty of reasons we may need to extend that time frame, particularly if it takes a really long time to recoup our costs, but unless we see a strong need to extend our timeline beyond a year, let’s focus on what happens in a single year for now.
Some businesses can’t use recurrence reliably. For example, If we ran a consulting business, the price a client paid in one month (or single engagement) may have no bearing on what their next purchase amount is, no matter how frequently they purchased. The same could be said for a retailer whose customers may check out at dramatically different order values each time.
In these cases, we’re going to focus more heavily on total spending per year in our forecasts. This won’t be perfect, and again, some business models require us to forecast well past a year in order to get a proper view of the customer. But for the purposes here, just know that “recurrence” isn’t a universal value or requirement for all startups.
If our business has never operated before, like anything else in this business model, we’re going to guess how many times a customer might recur. What’s important isn’t the value that we use, but the impact that we see this particular assumption has on our business.
The best way to start is to just use an educated guess based on how we think the customer might recur. This gets a little tricky because we have to guess the average number of recurrences. Yes, one customer could buy a pizza from us every day, but how many will the average customer buy from us in a month?
We may come to this conclusion:
Over time as we adjust that figure we will notice a massive impact on our business. The moment recurrence jumps to 8 units we nearly double our revenue per customer, allowing us to market even more aggressively. There’s a whole course to be written about recurring business models, customer retention, and churn rates. But suffice to say, this one is really important.
If we know how much our customers pay on average (AOV) and we know how many times they will pay us again (recurrence) – then LTV is just simple math:
Average Order Value ($15) multiplied by Recurrence (4) = Lifetime Value ($15 x 4 = $60)
Note that while we count the first order as “recurrence” as well. If the customer purchased just one time, the “recurrence” value would be 1.
Now that we know our average customer will yield us $60, we can use that information as the foundation of our financial forecasting.
If we know that our LTV for a customer is $60, then we know for sure that our cost to acquire a customer cannot exceed $60. In fact, it must be much lower to account for the cost of the goods sold (COGS) as well as the operational expenses of a company. But now we have an upper limit that will allow us to manage our marketing budget against.
We may find that the rate at which we lose customers (also known as “Churn Rate”) has more impact on our business than the rate at which we gain customers. This may shift some of our focus toward retention strategies that affect pricing, customer service, or modifications to the product to retain customers.
The number of customers we sell to multiplied by our LTV is our maximum (“top line”) revenue. We don’t have to guess much from there. If our CAC forecast tells us we’re going to get 60 customers, and our LTV is $60, we‘ll make $3,600.
Now that we have a nice handle on “how much will they pay us?” we are ready to move to the third step, which lets us know how much we think the product will cost us – Cost of Goods Sold (COGS).
We already know how many paying customers we have as well as how much it will cost to get them. Now we need to make sure we can deliver the product cost-effectively by determining how much our Cost of Good Sold is.
Cost of Goods Sold (COGS) is the amount it costs to ship a single unit of the product. Before we dig into the assumptions of COGS, let’s talk a bit about why breaking out COGS matters to begin with. Our goal with COGS is to separate our business costs into two buckets – Dynamic Costs and Fixed Costs.
Dynamic Costs – These increase every time we sell a product (basically COGS). If we sell 10 more pizzas we need more bread, sauce, and delicious cheese.
Fixed Costs – These mostly stay the same whether we sell 0 pizzas or 100 pizzas. This includes things like office rent or monthly bills we pay to a gazillion software vendors.
The reason we really care about this is that our business is largely driven by how our dynamic costs change with the growth of our business. When we’re forecasting for the year, we need to know exactly how our marketing will drive more customers, how more customers will lead to more sales, and how much it will cost us as those sales increase.
If we pay ourselves $50,000 in salary per year, whether we sell 10 pizzas or 100 pizzas our salary won’t change. That means we’re not a dynamic cost - we’re a fixed cost. We may need to hire another person once we are selling 1,000 pizzas, but that’s just operational growth. What we care about right now are the specific costs of every unit of pizza sold.
Let’s take a quick look at the common items that are considered COGS (dynamic) versus fixed costs.
Common COGS (Dynamic) | Common Fixed Costs |
Manufactured cost of a product Cost to purchase inventory Personnel paid to deliver product Credit card processing fees | Office rent Office supplies Software fees Furniture and machinery |
“But wait, Wil! My uncle who worked in finance at
Yes, there may, in fact, be a very good reason that some of the costs we’ve suggested as “not COGS” belong in our COGS. There’s nothing here that breaks if we include certain COGS together. The math is largely the same. The importance is how we view each line item of CAC, LTV, and COGS to get an overall picture for how our business model works.
Most new Founders ask the same questions about COGs so let’s try to wrap them up before we spend time determining where and how COGS will affect our business model:
What if we have no real COGS?
There are tons of businesses that don’t have any meaningful COGS. Most software businesses don’t recognize COGS because the cost of an additional unit of sale is near zero. If that’s the case, our emphasis will likely by on CAC and LTV without having to factor COGS into the equation. Incidentally, it’s also why software-based companies like Google and Facebook make so much money.
Are people (staff) considered COGS?
If our business requires us to pay staff directly to deliver the service, then yes. For example, we run a service named Zirtual.com that offers virtual assistance to busy Founders. Each customer requires us to allocate a percentage of the assistant’s salary as the cost of delivering the service. Most consulting businesses work this way because the “product” is the staff – quite literally.
We don’t want to confuse that with “we pay people to build the product.” If we’re thinking about our pizza place again, the staff isn’t the product. If we sell 10 pizzas or 100 our staff earns the same amount, and therefore while they are making yummy pizzas, the cost of the pizza is the dynamic part we want to capture.
Is marketing considered COGS?
Nope. Marketing is very much a dynamic component to most startups yet we’ll want to keep it separate because it can go up or down regardless of whether we sell anything at all. COGS only comes into play when we actually sell something (or get stuck with inventory!)
If we buy inventory, is the COGS the whole cost or “per unit” cost?
If we pay $1,000 for a case of energy drinks that we’re going to sell, our COGS should still be considered on a unit basis. So if we sell one drink for $3, and we paid $1 per unit, our COGS that month is $1. That’s for this specific purpose of forecasting and building a basic income statement (which we’ll cover later). There will be some different accounting that takes place when we need to manage our Balance Sheet which is where we keep track of all of our overall cash costs, regardless of whether we sold anything.
If we have a use case that still doesn’t seem to be covered here – it’s best to use our best judgment. A lot of this forecasting and assumption building is to create a framework for making decisions, it’s not a test to see if we can follow some stringent guidelines. Let’s save that for our income tax returns.
Let’s see what a total breakdown of COGS might look like when we add a group of different items together.
Sample Breakdown of Cost of Goods Sold:
Category | Value | Explanation |
Total Sales | $2,000 | Total sales for 100 Units @ $20 each |
Cost of Goods Sold (below) |
| |
Credit Card Processing | $60 | 3% of Total Sales |
Pizza Ingredients | $300 | $3 Per Unit * 100 Units Sold |
Pizza Boxes | $50 | $0.50 Per Unit * 100 Units Sold |
Total Cost of Goods Sold | $410 | Total of $60 + $300 + $50 |
Gross Margin | $1,590 | Total Sales minus Cost of Goods Sold |
We just isolated all of our COGS into a single group which totaled $410. That means we have $1,590 left to cover the operations of the company, which will include things like staff, office rent, marketing, and of course – company lunches.
We separate “Gross Margin” from “Net Income” (how much profit or loss we had) so that we can understand where the sale of our product itself is profitable, before the costs of the company. If it turns out we are selling $20 pizzas at a cost of $25, no matter what – we’re going to lose money!
We can’t entirely ignore how much we’ll spend in staffing costs or what our rent is going to be – that stuff’s important. When we build out our Income Statement in Phase 3, we’ll talk through how to estimate the different aspects of the business.
What we’re really talking about here is isolating “variable costs” from “fixed costs”. Fixed costs we can’t do much about – that’s why they are “fixed”. It doesn’t mean they aren’t an important part of the model, it just means they likely won’t change as dramatically as our variable costs do.
The more variable our assumptions are, the more we tend to worry about them!
If we were to forecast our business and knew down to the penny exactly what every cost would be, we could make provisions for a big loss by cutting spending, drawing down on a loan, or finding other ways to manage the outcome. The problem is that we typically have no idea how our forecasts are going to play out, and therefore we have to worry about everything that might be “wrong” with our model. The more variable the assumption, the bigger chance that things can get out of control – which makes it far harder for us to plan for. Therefore, we won’t ignore the other values, we just won’t worry about them as much.
With our biggest 3 assumptions in hand, we’re ready to move onto the wonderful world of forecasting. We’re going to start taking the values we discussed throughout this section and building out a financial forecast that will tell us a bit about how our future might play out.
What’s nice about this process is that we can simply tweak each of the assumptions and the forecast will change accordingly. This allows us to take into account lots of tiny changes to our assumptions that can lead to a significant impact into the viability of the business.