Building a Financial Model for Your Series A
Founders building their first financial model for a Series A round make the same mistake over and over: they think the model needs to predict the future. It doesn't. No investor expects your three-year projection to be accurate. What they expect is evidence that you understand the mechanics of your business -- how you acquire customers, how much they're worth, how your costs scale, and where the leverage points are.
A good financial model tells a story. It says: "Here's what we know, here's what we're assuming, and here's what happens if we're right." A bad one is a spreadsheet full of hockey-stick curves and round numbers that nobody believes, including the founder who built it.
I've sat on the technical side of several fundraising processes. What follows is what I've learned about building a model that actually helps you raise -- and more importantly, helps you run the business after you do.
What the Model Needs to Show
The model should cover:
Revenue projection (18 months, with a looser 36-month view). Monthly granularity for the first 18 months, quarterly after that. Show the drivers, not just the output. How many new customers per month? At what ACV? An investor who sees "$500K MRR by month 18" wants to understand the path, not just the destination.
Cost structure. Broken into three categories: people (your biggest cost), infrastructure (cloud, tools, services), and everything else (office, legal, insurance). People costs should map to a hiring plan. Infrastructure costs should map to your architecture.
Unit economics. This is the heart of the model. More on this below.
Cash flow and runway. How much cash do you have? How fast are you burning? When do you run out? What does the raise give you in terms of runway? Investors want to see 18-24 months of runway post-raise.
Scenario analysis. Base case, upside case, downside case. The downside case is the most important because it shows you've thought about what happens when things don't go as planned. If your downside case still shows a path to the next milestone, that's a strong signal.
Unit Economics That Matter
There are dozens of SaaS metrics you could track. For a Series A, focus on the ones that tell the story of your business model's viability.
Customer Acquisition Cost (CAC). How much do you spend to acquire one customer? Include everything: marketing spend, sales team salaries, tools, the fraction of engineering time spent on growth features. Most founders undercount CAC because they exclude headcount. Don't.
Lifetime Value (LTV). How much revenue does one customer generate over their lifetime? For subscription businesses: average revenue per account divided by churn rate. If your monthly churn is 3%, your average customer lifetime is ~33 months.
LTV:CAC ratio. The classic benchmark is 3:1 or higher. Below 3:1, your growth is expensive. Above 5:1, investors will ask why you're not spending more on acquisition.
Payback period. How many months does it take to recover the cost of acquiring a customer? For B2B SaaS, 12-18 months is healthy. Above 18 months, your cash efficiency is poor and you'll need more capital to grow. This is often more important than LTV:CAC because it directly impacts how much cash you need.
Gross margin. Revenue minus the direct cost of delivering your product (infrastructure, support, third-party APIs). For software, this should be 70-85%. If it's below 60%, you have a cost structure problem that will concern investors. If your AI inference costs are eating your margin, model out how that changes with scale and optimization.
Burn multiple. Net new ARR divided by net burn. A burn multiple below 2x is excellent. Between 2-4x is acceptable at the Series A stage. Above 4x means you're spending too much relative to growth, and investors will push back.
The 18-Month Rule
Three-year projections are fiction, and everyone knows it. The market will shift, your product will evolve, your team will change.
Focus your precision on the next 18 months. This is the period where your assumptions are grounded in data you actually have: current pipeline, known contracts, realistic hiring timelines, and infrastructure costs you can estimate with confidence.
For months 19-36, show the trajectory with wider error bars. Quarterly buckets, not monthly. Ranges, not point estimates. The investor needs to see a plausible path to the next milestone, not that you can predict Q3 2026 revenue to the dollar.
European B2B SaaS Specifics
If you're building a B2B SaaS company in Europe -- which is the context most Conectia clients operate in -- your model needs to account for dynamics that differ from the US playbook.
Longer sales cycles. Enterprise sales in Europe typically run 6-12 months, compared to 3-6 months for comparable US deals. Don't model 60-day closes because a US benchmark says so.
Lower average contract values. European B2B buyers are more price-sensitive, especially in the mid-market. If US competitors charge $50K/year, your European pricing might land at $30-40K. Model your actual pricing, not aspirational pricing.
Higher retention. The flip side: European customers tend to be stickier once they commit. Annual net revenue retention of 110-120% is achievable. If you have the data to support strong retention, highlight it -- it's one of the strongest signals for Series A investors.
Multi-currency exposure. If you're selling in EUR but paying cloud costs in USD, currency fluctuations affect your margins. A simple sensitivity analysis at different EUR/USD rates shows sophistication without overcomplicating the model.
How the CTO Contributes to the Model
This is where I spend significant time during fundraising, and where many CTOs under-contribute. The financial model isn't just a CEO/CFO exercise. The CTO owns critical inputs.
Infrastructure costs at scale. How do your cloud costs change as you go from 1,000 to 10,000 to 100,000 users? If the relationship is linear, that's a red flag for gross margin. If you've architected for sublinear cost scaling, show it.
Team scaling plan. When do you need to hire the next backend engineer? The first SRE? The hiring plan drives the biggest line item in the model. It should map to the product roadmap: "We need two more engineers in Q2 to build the enterprise features that unlock the $40K ACV segment."
Build timeline for revenue-unlocking features. If your model shows enterprise revenue starting in month 8, the CTO needs to confirm that the enterprise features (SSO, audit logs, role-based access) will be ready by month 6. Revenue assumptions should be constrained by what the engineering team can actually deliver.
Technical debt and its cost. If the current architecture requires a significant refactor before it can scale, that's a cost and a timeline risk. Include it honestly -- investors will discover it during technical due diligence anyway.
Common Mistakes
Over-engineering the spreadsheet. A model with 47 tabs and 200 linked assumptions isn't more credible -- it's more fragile. Every additional variable is a place where an error can hide. Keep it as simple as your business allows.
Ignoring seasonality. B2B SaaS has seasonality. Budget cycles mean Q4 is typically strong and Q1 is weak. If your model shows perfectly linear monthly growth, it looks like you haven't actually sold anything yet.
Optimistic hiring timelines. It takes 2-3 months to hire a senior engineer, not 2 weeks. If your model assumes you'll double the team in a month, the investor knows your hiring plan is fiction.
Forgetting the raise itself costs money. Legal fees, due diligence costs, the founder's time spent fundraising instead of operating. Budget 3-6 months of distraction and $50-150K in closing costs.
At Conectia, we see the hiring timeline problem firsthand. Founders model aggressive team growth, then discover that finding senior engineers takes months. Our senior LATAM engineers can be integrated in 1-2 weeks instead of 3 months, which means your revenue-unlocking features ship on the timeline your model promises.
Build the model to be honest, not impressive. A model grounded in real data and genuine understanding of your cost structure is more compelling than the most elaborate spreadsheet ever built.
Does your Series A model assume a team that doesn't exist yet? Talk to a CTO -- we help startups scale engineering on the timeline investors expect, with senior LATAM engineers ready in weeks, not months.
