Frequently Asked Questions
Tools like Baremetrics are excellent for analyzing your historical/short-term future financial performance; However, they're not a full pro-forma financial model, and can't produce the same comprehensive output that investors and boards require. Simply put, they don't have bottoms-up logic to create meaningful projections beyond the next 6-12 months.
They're also not built specifically for SaaS companies. To produce credible projections, FlowCog uses SaaS benchmarked industry data to guide you. Our bottoms-up approach stretches all the way from marketing and sales efforts to onboarded customer behavior; each funnel or primary driver is carefully considered at the most atomic level, along with time periods and lagging effects. The model includes funnels for marketing spend and sales team conversions, all the way through to win rates and onboarded customers. From there, customer behavior and headcount simulations are run to project out renewals, upgrades, downgrades, and churn.
Excel models can be a great way to get a back-of-the-napkin estimate of your company's trajectory. Unfortunately, a lack of standardization means that most spreadsheets are riddled with formula errors and hardcoded outdated numbers. They're difficult to build and very hard to maintain properly. It's also unlikely that the spreadsheet builder has thoroughly researched the SaaS industry. An Excel spreadsheet also won't have some of the more sophisticated simulation and scenario analysis capabilities that FlowCog does.
The onboarding process gets you up and running quickly. After purchase, you'll receive an onboarding email with next steps.
- You'll fill out the 10-question onboarding form.
- You'll go to the web application where inputs will automatically populate the model based on your form responses.
No. In fact, when FlowCog initially populates your model with your responses from the onboarding form, it infers a lot of inputs used for projections based on SaaS industry benchmark data.
No model is perfect, and all models are only as good as their inputs (garbage in, garbage out). However, some models are less wrong than others in terms of their methodology (top-down with naive growth assumptions vs a robust bottoms-up approach) as well as structure (which dictates how error-prone a model is).
If you're a very early stage company (e.g. seed funding), some of the more advanced features like growth simulations or headcount modeling may not be relevant just yet; however, an accurate and transparent model can still be very helpful for fundraising and understanding your growth trajectory.