
Financial Modeling for Startups: An Introduction - lhh
https://www.fivecastfinancial.com/guides/financial-modeling-for-startups-an-introduction/
======
jimnotgym
Finance person here, this is a good grounding of the basics. The hardest part
to take forward is working out the timing of things. A company is constantly
owed and owing money, and this is the real trick to working out your funding
requirements.

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.

~~~
presscast
I've heard that it's rather common to be technically profitable (i.e.: a
company has a greater income than expenses), but nonetheless insolvent due to
bills coming due before clients pay their invoices.

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?

~~~
grenoire
I'm currently handling bookkeeping for software companies and one thing that's
often overlooked is your clients consistently making late payments on their
invoices. Make sure that you know who those client are and schedule
accordingly.

~~~
ABS
DSO (Days Sales Outstanding) is one of the most important numbers to track for
any company. If you are a small company it is arguably _the_ most important
one to manage cash flow

~~~
jimnotgym
And it is a notoriously fickle metric, most people track it at month end, and
yet their main sales receipts come in after... leading to a rather alarming
result. DSO or debtor days as it is often called in the UK, needs to be read
with an understanding of the payment patterns in that business.

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projectramo
The issue is forecasting revenues.

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.

~~~
ig1
Broadly speaking you should have a MQL -> SQL -> Deal model (i.e assumptions
for ratios between the three) and assumptions for CPL and SQL per SDR/AE.

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.

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thinkingkong
This is great. If you like this sort of thing you can go one step down the
modeling path and take a great coursera course called “Model Thinking” [1]
which totally gave me a different appreciation for spreadsheet nerdery (you
use lots of different tools).

1\. [https://www.class-central.com/course/coursera-model-
thinking...](https://www.class-central.com/course/coursera-model-thinking-317)

~~~
cleansy
Why is it linked to an affiliate website and not coursera itself?

EDIT: here the original coursera link: [https://www.coursera.org/learn/model-
thinking](https://www.coursera.org/learn/model-thinking)

~~~
thinkingkong
I was on my phone and couldnt copy the target link without the app opening the
page instead. :/

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AmericanChopper
When I had to produce my first financial documents like this, I went out and
looked at public companies that had similar enough business models to mine,
and read all their annual reports. Then I figured out what metrics are worth
tracking or that I wanted to track, and pretty much just copied their
methodology (which they described in their statements).

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.

------
daveungerer
Decent article, thanks for writing it. I think more founders should do a bit
of financial modeling.

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.

~~~
lefstathiou
I prefer the bottoms up expense buildup of people as opposed to a broad
percent of total calculation as it introduces more discipline to the forecast
and reduces risk of weird situations where sales go up by x% and all of a
sudden you’re hiring 3 half people (in models I expect to see expenses go up
in steps because that’s how they work in practice). Also gives insight into
the mindset of the forecaster. For example do they assume 2 sales people can
cover 300 accounts while building a pipeline without SDRs? I am an operator,
not a VC so perhaps it’s an audience preference.

~~~
daveungerer
I think it would be good to have a bottom up forecast for the first 12 months.
Apart from that, a disciplined forecast will clearly show how the calculation
was derived and what assumptions went into it. You should not have to dig
through a bunch of data to gain insight into the mindset of the forecaster -
the forecaster is supposed to put that info in the model! If they can justify
year 1's sales expenses by mentioning that it's for 2 sales people + an SDR,
then great! Just don't expect that granularity in year 3.

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.

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zby
I was just thinking about using stats in startups. For sure you need to know
your runway (and burn rate) - but then for SaaS (and similar business with
recurring revenues) the first useful statistics is _churn_. Customers quiting
is the most important info when looking for product market fit. Customer
groups with lowest churn are your target group (market). Before you start
scaling business you need first to decrease churn - because you don't want to
grow the holes in your system together with it (this metaphor is not mine -
but I cannot now find where it came from - but I googled this article instead:
[https://pakman.com/churn-is-the-single-metric-that-
determine...](https://pakman.com/churn-is-the-single-metric-that-determines-
the-success-of-your-subscription-service-6e82d9d9ea01)).

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](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.

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slowhand09
Nice writeup. A previous company had us participate in a simulation exercise.
Split into teams, move around a board similar to monopoly except you were
collecting accounts payable from customers, paying rent and utilities, paying
payroll, paying taxes on xyz, lawsuits, etc. CashFlow was king. Losers all
were paying bills as they arrived rather than on due dates. They had the
money, on paper, but not on time. Timesheet-driven contracting - it showed how
important it is to fill-in timesheets so customers can be billed. Miss the
cutoff and your 48 hrs gets invoiced two weeks later. Your company doesn't
thrive when it's borrowing money to pay you for time it can't bill yet because
you forgot to sign your timecard.

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tlpappas
Good brief summary but sometimes high level is just as important. eg. Using
the tools to explain how you prioritize spending money raised and connecting
it to material signposts is very helpful for funder communications as well as
testing out your own ideas in a cohesive framework.

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rkagerer
Good basics but the article is too much on the simple side for me.

"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.

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icedchai
In my experience with early stage startups, these models are often pure
fiction and completely divorced from reality.

They look nice to investors. Reality is very, very different.

~~~
FailMore
(Ex VC here)

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.

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keenans
nice post. also in the 'financial modeling for startups' space:
[https://foresight.is/](https://foresight.is/)

