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Startup financial models – Templates compared for SaaS (stephnass.com)
632 points by warpech 30 days ago | hide | past | web | favorite | 41 comments

Great roundup! I'm one of the founders of Causal — https://causal.app (#12 on the list).

We have 2 different SaaS templates built by Taylor Davidson (whose Excel template is #6 on the list):

- Starter version: https://my.causal.app/models/1269

- Advanced version: https://my.causal.app/models/162

They're not good if you need to do a bottom-up forecast based on actual deals (e.g. for high-value enterprise sales), but are hopefully useful for a more marketing-driven biz. The main differentiator vs spreadsheet models is that you can easily bake uncertainty into your assumptions. E.g. churn rate can be "2% to 6%" instead of just "4%".

We've looked at a tonne of spreadsheet models, and Taylor's (#6 on the list) is, imo, a masterclass in spreadsheet design. It's super flexible and modular — you can easily dissect it and add/remove parts without accidentally breaking the whole thing. Even if you don't need a financial model right now, I'd recommend checking it out just to see what a great spreadsheet model looks like: https://foresight.is/standard-financial-model

Casual is awesome! I would like to learn more, Do you guys also hire?

*Causal :D We just hired our first (non-founder) engineer. If you shoot me an email (lukas@causal.app) I'll let you know when we're making the next hire.

I don't think so.

Hi, I’m the founder of Summit. Honored to be included among great and smart company.

Happy to connect anytime to talk financial planning or support usage of the product.

Long-time HN’er, had the idea for Summit while fundraising for my first startup (Stormpulse, recently acquired).

For the forecasting geeks and curious: this video lays out the tech behind Summit’s forecasting engine (password: everest3): https://usesummit.wistia.com/medias/fd3pk1fuvz

The "Out of Beta" podcast which you co-host (and document your Summit journey) has been a blast. Thanks for that and good luck with Summit.

Having built many these models, it's now my opinion that if you are at the stage where you're needing a template for a business plan, you better have a product that fits a real need and that delivers a product with margins. Too often in early stage companies, the model is built top-down to meet someone's expectations about a previous company they sat on the board of, helped found, etc. If a template engenders a more thoughtful, fact-based approach to operating the business, that's healthy stuff. Otherwise, set short-term goals. Try to hit them. (And in today's environment, find a path toward gaining customers without having to raise capital). $0.02

These documents all look very smart, but how does anyone have any idea whether the numbers in them are even close to realistic? I can make up some cute hockey stick numbers for any of my businesses, but why should anyone trust them? Particularly for businesses that haven't even launched yet and therefore have literally zero hard data on whether customers will actually pay them money or how much or for how long. How many startups in that position, with no financial data and no evidence of product-market fit, can credibly predict even a year ahead that they won't pivot to some completely different plan, never mind 3 or 5 years out?

When I started my first B2C, a long time ago now, it was actually the bank where we opened our business account who asked these kinds of questions. We sat down, put our best guesses at plausible numbers into a spreadsheet for things like acquisitions and churn, worked out the money that would result.

Barely any of the key assumptions we made were within an order of magnitude of reality, and they were all in the wrong direction. For example, we have far higher churn than any example startup business plan I have ever seen just from card charges that fail with no obvious explanation each month where we don't subsequently recover and continue that subscription. That problem remains one of our biggest pain points to this day, and that effect alone has turned many an otherwise profitable month negative and reduced that business to a fraction of the size it would otherwise have been by now if everything else was held constant. No-one here saw that coming. No example plans or startup guides or financial advisors we consulted even mentioned the possibility, never mind giving any concrete figures for what we might expect.

The major value of doing this modelling is to be able to do sensitivity analysis on the key business factors [0]. You want to know what things really matter and what don’t.

There is also great value to be gained just from building the model as you are forced to estimate the various factors that will drive the business. Whenever I have gone through this exercise I have picked up at least one important assumption that I had missed.

0. https://en.wikipedia.org/wiki/Sensitivity_analysis

I'm one of the founders of Causal (#12 on the list) — this is one of the key things we've tried to address in a couple of ways:

1. Ranges vs point estimates: instead of assumptions being fixed values (e.g. my 'Growth Rate' is 10%), you can define ranges like '5% to 15%', and we run thousands of simulations to figure out the actual range of plausible outcomes. We also make it trivial to set up different discrete scenarios.

2. Interactive dashboards where you can tweak assumptions: if you think an assumption is too ambitious/unrealistic, you can actually just go in and change it yourself to see what happens. These changes don't get saved to the model, and there's no chance of accidentally breaking anything.

Here's an example: https://my.causal.app/models/1269

Sure. I guess my point is that even picking ranges, your error bars are so wide before you have any concrete data on revenues and product-market fit that I don't believe you can draw any intelligent conclusions anyway. Once you get something into market, and you start spending and making money, and you start hearing back from paying customers and running experiments and iterating, then you can get a meaningful sense of how your product or service is going to perform and choose ranges that might not be so wide as to be completely useless.

The exercise of modeling your business helps you become aware of all that you don’t know, which helps you stay humble and curious and focused on answering the right questions.

I can only speak on behalf of my model (#3), but that's meant entirely for companies who want better visibility in their existing operations. Say, they want to figure out how much cash they have in the bank in 6 months, or can they afford to hire 3 more ppl next quarter.

Personally, I have found it hard to work with pre-revenue companies, especially if they come to me with a plan to hit tens of millions in revenue in just a couple of years since launching. Maybe a small percentage of them do, but given how many don't make even a single dollar I've tried to steer clear of pre-revenue startups. Companies with real revenue and growth seem to be a much better fit.

I feel like there 2 very different use cases for financial models:

- Early-stage fundraising. The numbers are wrong, everybody knows it, but you have to show that curve going up and right.

- Later-stage (maybe 1-year post-revenue?) when there is some level of robustness behind the numbers, and you do it because it's useful to pilot the company

Early stage is also about ensuring that the levers are understood and that the thinking about the right things to measure and what to action are appropriate in understanding the business. Less about the numbers, more about the variables.. why are you measuring this, why arent you measuring that? What actions will you take to try to get to this number? This would give the investor a better sense of how the founder thinks.

Agree 100%. Very different needs with the two lots.

Also agree here.

Rational financial planning is, of course, essential for managing a business sensibly.

However, vague intuition + random luck generator + huge uncertainty != a useful financial plan. I feel like a lot of startups could summarise their financial slides with something like "We anticipate an outcome somewhere between failing within three months and becoming the next Facebook, with somewhere between Ramen profitability within six months and a unicorn exit at 8-10 years being most likely."

That is the same logic of later stage VCs. Which makes me appreciate those earlier stage investors even more as it is really tough statistically to pick the winners.

These models are a great example of why it's important to have these retractions to cull out people who are just riding the wave and happen to be in the right place at the right time.

These models all look like toys. Things to play with. They have not grounding in reality, just dreams and ideas. I'm always shocked to see people use models like these to invest in early-stage seed and Series A even though we all know that dialing in the right product and dialing in the right sales/growth model mean way more to a startup at this stage's success than 1.5 vs 2% churn.

Great list. FWIW, a lot of SaaS companies use the second template from Christoph Janz when they're raising their seed rounds.

If anyone's interested in additional content about financial modeling, I described ~10 common mistakes I see in financial models a few months ago: https://twitter.com/lpolovets/status/1188979329935409152

> I described ~10 common mistakes I see in financial models a few months ago: twitter.com/lpolovets/status/1188979329935409152

Mirror: https://threadreaderapp.com/thread/1188979329935409152.html

If 90% of startups fail, and they are all putting in effort trying to create financial models, what is the point of this exercise other than to follow a broken tradition? The remaining ten percent undoubtedly have deviated far from their seed stage model.

VCs need to own this mistake and kill the financial model requirement. Stop wasting entrepreneurs' time.

So your argument is.. what exactly? 90% of startups fail, so let’s not make any attempts at understanding the trade offs inherent in decisions such as whether to hire, what price to put on things, which sales channel to pursue, what debt instrument (if any) makes sense?


Couldn't have said it better myself. To justify that financial models are a waste of time by saying "90% of startups fail" is aken to saying that professional athletes shouldn't train because "99.9% of athletes don't become pros".

Do you think that the remaining 10% of successful startups aren't utilizing forecasts and testing their assumptions? People love to be contrarian just to be contrarian on this site sometimes.

I think the argument is that financial forecasts for startups are equivalent to wearing a business suit; they are both anachronisms that signal our desire to succeed but have no causal relationship to the nuts and bolts of succeeding.

I’m of both minds. We need a rough business model (a type of Fermi Estimate ) and metrics to help track and validate our assumptions. At some point these estimates become detailed enough to gain predictive power but assuming they begin that way is a recipe for failure.

You don't need to jump to conclusions and put religious faith into your investment decisions, but a lot of investors do and probably haven't yielded worse performance for it.

My argument is that the financial model presented during the pitch is a waste of a great deal of effort because it is inaccurate and futile. As someone running a seed-funding business, you are relying on a piece of financial fiction to make a decision about whether to fund. Do you really care about the forecasts or just the metrics that are more difficult to fudge? Could the model be distilled to something more useful that isn't even a model?

I suggest the following exercise: among your investments that survive, reconcile the early financial model with current performance and market analysis. How accurate were the estimates?

This is ultimately an exercise in creating an illusion of certainty and competence: can an entrepreneur sufficiently obfuscate a business opportunity so that the investor doesn't look bad?

I /think/ this is less for VCs and more for entrepreneurs to quickly get a better idea of if a market is viable before exploring an idea further.

And if it's not the intention, this would be useful to me for that reason.

Thanks for putting so much work into comparing all these models! I'm the author of #3, and a founder of a financial modeling software company (the latter not reviewed here)

One thing I'd add for anyone comparing these models for their own use: Make sure the model you're going to use covers the authors #1-5 criteria for the parts you need. More features isn't always better. For example, if you run a marketing driven SaaS company, it doesn't matter if the model in question can't handle complex enterprise sales.

I have a big update coming to the model this coming week. All of those changes have been made in the actual model template already if were planning to take a look - it's just the update to the documentation that's still missing.

A friend of mine created this one and hosts a half-day seminar every few months to go through how to use it (and what the numbers mean/why you may want to do different things).

I found the seminar quite useful and plan to use his model for our startup once we have enough data to make estimates for the inputs within an order of magnitude.


Thanks Brad. For those interested, the complete seminar is available at dsmpartnership.com/financialmodel. This is available free of charge

Seems like the goal is to impress investors. Unless you’re going into the business of financial modeling, focus on how you’ll provide a service that’s worth more to customers than it costs them. Less experienced investors are going to want to navel gaze with you at this shit. Those who know how value is created will focus on the fundamentals like path to market and your value prop, differentiators and moat.

Having gone through the process of starting to think about my own business and how to start generating revenue, the one interesting thing is that in the USA vs. some other countries you have venture capital vs. immediate profitability.

So, just as an example, in Southern Africa almost every person between the ages of 18 and 50 is trying to start a company (with the key word there: "trying"). But the approach is much different. You are basically just trying to get some kind of revenue stream and if your revenue stream happens to be large enough then, congratulations, you are a business owner.

Thanks for writing this up!

I'm the author of model #22 on the list, which is built from my vast experience collecting SaaS models and writing articles on it. I'm honored to be included in the list even though my model is only available in open office format.

If anyone's interested I've written about how to model investor traction to your model, sort of a metamodelling framework for startup founders. You can see it on my twitter and instagram, if anyone's interested.

On a more serious note:

The best way for engineers to get into modeling is to understand it from the data up rather than from the model down. Engineers don't really need to model out the future and they tend to be bad at that. Instead, they just need to know what their important metrics are.

Even if there are less than 10 paying users in your app, you still have enough data to create the foundation:

1. Define "new user acquisition" with several categories as a funnel towards paid users. "Signed up -> has project -> is active monthly -> paid" is an example. Define the qualifications that can be programmed in to bucket a user into the category. Write code to pull this data out into high charts and see your current state of users.

2. Churn is usually harder to get the right data unless you have your events stored in log format, so approximating churn by looking at the deltas each week or month is fine to start. Create a database to snapshot your weekly or monthly metrics so you can know the deltas. Put it in your roadmap to get the data cleanly later.

3. Over time, capture the average lifetime churn as a percentage, and past quarter churn as a percentage to start, in addition to % change in users for each category over time. These are your growth rates / churn rates. You can then open gsheets and do simple modeling (no need for a Saas template yet) via a simple google search for how to start.

4. Pick the number of users that you want to have in 3 years. Take your gsheets and extend it to 3 years, and then freely move around with your growth and churn rates until that number is hit. You want to get a sense of high churn and low churn scenarios (what kind of growth rate you need to have).

5. For each scenario, how far is it from your current numbers? What will you do to move the growth rate and churn rate towards the numbers you ideally want?

You'll probably get a lot from doing the above then buying a template unless you're well beyond this stage.

How neat! Is there something like this for e-commerce?

Taylor Davidson's Excel model (#6 on the list) is actually general enough to be applied to SaaS or e-commerce: https://foresight.is/standard-financial-model

He's also built a free e-commerce model template in Causal, alongside his SaaS ones (#12 on the list): https://www.causal.app/ecommerce

Nice work. I really appreciate acknowledging the landscape and sharing that work before putting your own into the mix. Underrated step

Whether you use a purchased model or build one yourself, IMHO having a good model that allows you to run scenarios to predict potential futures for your company is absolutely essential. I live in my model every day, running scenarios to test out ideas for financing and to gauge downside risks.

Without a decent model, I would be absolutely lost.

While most of these financial models seem decent for tracking the operating of a SaaS company, if you are looking for a model that PE, investment banks and VC firms actually use to value a business, check out Private Equity Models:


$179 for an excel template, in case anyone is curious. $10,000 value though!

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