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
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
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.
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.
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
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.
- 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
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."
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.
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
VCs need to own this mistake and kill the financial model requirement. Stop wasting entrepreneurs' time.
JESUS TAKE THE WHEEL!
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’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.
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?
And if it's not the intention, this would be useful to me for that reason.
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.
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.
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.
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.
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.
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
Without a decent model, I would be absolutely lost.