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.
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.