On top of the model every business needs an operational cash flow forecast going out say 3 months at least. For every day you enter the brought forward balance from yesterday. Then you add and subtract all of the line items of cash inflow and outflow for the day to forecast a closing can balance. It is more than possible for your financing model to show profitability and yet to be insolvent, because you are paying money out before it comes in. Like maybe a big customer pays on the 28th but payroll goes on the 25th...
Cash is king as they say, and a daily cash-flow forecast is the main tool that a financial controller would use to maximise it.
From what I was told, this mostly affects supply-chain heavy companies; software companies are mostly spared this kind of consideration.
What are some of the red flags that founders should be aware of when reading their own cashflow statements?
Think of it like this: if you are providing a service, but are not getting paid for this in advance, you are essentially giving the customer a short term loan. You cannot infinitely provide those short term loans considering that you have bills/salaries to pay. It might still be money that belongs to you, but if you do not have it in your bank account when your bills are due, you are insolvent.
Things to be very wary of when looking on your balance is having very low ratio of liquid cash in respect to your debtors post (e.g. unpaid outgoing invoices). This means that if your debtors are going to pay later than expected, you have very little runway to cover that. Now, what risk this poses to your company depends a lot on how many customers you have, whether they pay their invoice automatically, and what their history of late payments is. Furthermore, the same applies for you on the purchasing side: if the majority of your costs are on the purchasing side and you can afford to pay those bills later without getting your servers shut down, then being illiquid is less of a risk. If the majority of your costs is in employees, then you are in trouble: not paying employees is a big no-no, so that increases risks and allows for less wiggle room.
Otherwise you should model what happens when the sales come late or not at the level you want, Braintree hold your cash, etc. If you can't flex your overheads to stay within your cash facilities, then you are risking insolvency.
Sales receipts, payroll and sales tax are the big numbers.
That said, I think most founders should not be forecasting salary expenses on a per-position basis, even if they're under 100 employees. In my experience, you definitely won't know which positions you'll be hiring for further out than 1 year. If you're trying to impress investors it might work, but it will have limited utility for you personally.
Instead, you should group salaries by function (e.g. sales, engineering) and then make explicit your assumptions about labour efficiency. In the model described in the article, these assumptions are also there, but spread out over 40 rows in a table - not good! Assumptions in models should always be explicit.
For example, you could say that, in order to maintain your projected growth, you need to spend 10% of your revenue on sales staff. Or if you're aiming to be funded, you might instead work out how much labour it might take to make one sale, and then extrapolate based on how many sales you intend to make in the year.
You could look at engineering and decide you need 1 person in your engineering team (disregarding job title) per 100 clients. Then extrapolate, once again, based on number of projected clients. Obviously, software is meant to be scalable, so this all depends on how much up-front development you intend to do and at what pace you intend to add new features, so you might want to factor your growth targets in too.
Now, organisations normally bring in layers of management as teams grow. Do you need to account for this? Probably not. Remember, we're focusing on labour efficiency. These managers might increase your costs, but the idea is that they also help your teams function in a scalable way. And if you have good managers, the average tenure at your company should increase, leading to higher productivity.
The benefit of the above approach is that, now that all your assumptions have been made explicit, you can easily tweak them to see how they impact your model, rather than having to dig through many rows of data.
Lastly, and this is nit-picking, but ignoring income tax means this model should only be used to forecast up to periods where the company is not profitable. As soon as there's profit it will be completely wrong. Although it's a nice simplifying assumption if all you're trying to model is your road to break-even.
It's quite easy to avoid the "3.5 people" issue by making it a step function (i.e. rounding). Once again your assumptions become explicit, which is good. E.g. you might decide that one person can do the work of 1.3 employees (people can do this for a while when it's needed!) and round everything above that up to 2.
However, when forecasting 3 years in advance as in the article, the fact that your model has you hiring fractional people becomes less important.
It is still very useful for variance analysis. Like, I made 100k, expected 120k...because x person cost more than expected and x person was hired early. It's nothing to get upset about, but it aids your understanding.
A granular 12 month forecast is very useful for the reason you described, but we're discussing longer time horisons here.
Main reasons for this are:
- It enforces discipline. It's easy to make hand-wavy assumptions like forecasting costs as a % of sales and calling it a day
- Small changes to the hiring plan can have a drastic impact on a startup's finances, including the timing of those hires
- If you tether all your expenses to sales, you can't really explore the downside, because you'll always be showing consistent profit margins
To your point though, I agree that it's important to look at expenses both bottom up and top down.
Zoom in and the issue is forecasting unit sales.
Zoom in and the issue is forecasting new unit sales.
In the example, this line does a lot of the work in the model: "Forecasting New Subscriptions (line 10). We've just entered hardcodes here for simplicity, but these could be the result of calculations related to a marketing / sales funnel"
I submit that this single assumption will carry more weight than the rest of the model, and is the most difficult to forecast.
With these you can tie sales forecasts to marketing spend and sales hires.
Obviously these won't be perfect, but when you're off target you can see why (i.e which assumption was false) and then either try to fix it or correct the false assumption giving you a more accurate model going forward.
Edit: For existing businesses this metric is much more predictable by the way, but especially in B2B it might be obfuscated because the finance department does not know how much value has been provided for which there was not an invoice created for it yet.
1. https://www.class-central.com/course/coursera-model-thinking...
EDIT: here the original coursera link: https://www.coursera.org/learn/model-thinking
They look nice to investors. Reality is very, very different.
Before you launch, yes (ish) - it can give you an idea of where you'd like to start charging and why. Models come into their own post launch. They provide a clear structure on how to optimise the economics of your business. E.g. If you are currently selling at $20 and losing $3/sale due to support costs and returns, it provides a great structure to focus on a) increasing the price, b) reducing support costs / order and c) reducing returns.
A dream for any VC is a startup with fantastic economics at day 0, but this is rare. The majority of high quality companies have negative unit economics during their infancy. We liked investing in companies with negative unit economics with founders that understood the drivers of their economics deeply and were optimising them aggressively on a weekly/monthly basis (and doing all this inside of a H-U-G-E market). (The best ones did it on a weekly basis.)
An interesting example is Just Eat, one of Europe's best performing startups (IPOed at £1.5bn). This company had negative economics for ~3 years, but the market was very big and investors could see a pathway to positive unit economics through optimisation. This allowed them to fund the company through the negative UE period.
Another thing worth considering, if you hate The Sheet, or if it’s getting out of control, considering putting your data into a database and reporting with redash. It’s a also a very convenient way to share data internally.
Also, in my experience, the thing that mattered most was customer acquisition cost to lifetime value ratio.
Sales statistics become important after you have satisfying churn rate (i.e. after product market fit). With advertising you can quickly grow sales - but if your churn is too big - then the LTV (https://en.wikipedia.org/wiki/Customer_lifetime_value) will be too low. And generally customers that come from advertising will have a bigger churn than those who learned about the product organically.
"There are no hard and fast rules on how to categorize your expenses"
Pro tip: Loosely aligning them within recognized tax lines can help simplify things in the early days and reduce work for your accountant.
