There’s a chart that investors see so often it’s become a punchline. Revenue is flat for 12 months. Then, somewhere around Month 13, the line goes vertical. By Year 3, the company is doing $50 million in annual recurring revenue. By Year 5, it’s $200 million.
When the investor asks what changes at Month 13, the founder says something like: “That’s when we start scaling our sales team.” Or: “That’s when our content marketing starts generating inbound.” Or, worst of all: “That’s when word of mouth kicks in.”
The problem isn’t the ambition. Most successful companies do eventually achieve rapid growth. The problem is that the model doesn’t explain the mechanics of how growth actually happens. It shows what the founder hopes will happen, not how it will happen. And that distinction is everything when you’re asking someone to write a cheque.
The Top-Down Trap
The most common approach founders take to revenue projection is top-down reasoning. It goes like this: our total addressable market is $10 billion. If we capture just 1% of that market, we’ll be doing $100 million in revenue.
This tells the investor absolutely nothing useful. Every startup can claim to be in a large market. The relevant question is: what is your specific plan for acquiring customers, at what cost, through which channels, at what conversion rate, and at what velocity? Top-down projections answer none of these questions.
Worse, they signal inexperience. An investor who hears “1% of a $10B market” immediately knows the founder hasn’t done the granular work of understanding their acquisition funnel. It’s a shortcut that sounds reasonable but is analytically empty.
There is a place for TAM/SAM/SOM analysis in a pitch — it frames the opportunity. But it should never be the basis for your revenue projections. The moment your projected revenue is derived from a market percentage rather than from actual customer acquisition mechanics, you’ve lost credibility.
Building Bottoms-Up Revenue Projections
The alternative — and the only approach that survives investor scrutiny — is bottoms-up modelling. This starts from what you know or can reasonably estimate and builds forward through the actual mechanics of your business.
For a SaaS company, a bottoms-up model might work like this:
You have 30 paying customers today, each paying an average of $800 per month.
Your primary acquisition channels are outbound sales (converting at 5% from qualified lead to close) and content marketing (converting at 2% from website visitor to trial, 15% from trial to paid).
Your outbound team generates 100 qualified leads per month. Your content drives 2,000 website visitors per month.
That gives you 5 new customers from outbound and 6 from content — 11 new customers per month, adding roughly $8,800 in new MRR.
You’re planning to hire a second outbound rep in Month 6, doubling outbound capacity after a 3-month ramp.
Content traffic is growing at 10% month-on-month based on current trends.
Every number in this chain is testable. An investor can challenge the conversion rate, the ramp time, the traffic growth rate. But they can’t dismiss the methodology, because it reflects how revenue actually gets generated.
This is the approach that modern financial planning tools enforce by design. Rather than starting with a revenue target and working backwards, they ask you the right questions: what’s your current MRR? How many customers? What’s your average contract value? What are your conversion rates by channel? The model is built from these inputs, making it structurally impossible to create a top-down fantasy.
The Power of Cohort-Based Revenue
Once you have a bottoms-up model, the next step is to organise it by cohort. A cohort is simply a group of customers acquired in the same time period.
Instead of projecting total revenue as a single line, you model each month’s new customers separately. The January cohort starts at 11 customers. In February, some of those customers have churned, but the remaining ones might have expanded. Meanwhile, the February cohort adds another group. And so on.
This approach creates radical transparency. Investors can see your retention assumptions, your expansion revenue, and your churn — all in one view. They can ask: “Why does the March cohort retain better than January?” And you can answer with specifics: “We launched the onboarding product in February, and early data shows improved 90-day retention.”
Cohort-based modelling is the gold standard for SaaS companies. It’s how the best-run companies track their business internally, and it’s what institutional investors expect to see from Series A onwards. If you can present it earlier, you signal operational sophistication.
Why a Single Projection Is Always Wrong
Every financial model is a set of assumptions about the future, and the future is uncertain. A model that presents a single revenue line without acknowledging uncertainty is either naive or deliberately misleading.
The solution is scenario modelling. At minimum, your model should present three cases:
Conservative: Growth comes slower than expected. Churn is higher. Sales cycles are longer. This isn’t a disaster scenario — it’s a realistic downside case.
Base: Your best estimate, built from the assumptions you believe are most likely.
Aggressive: Things go better than expected. Conversion rates improve. A partnership accelerates growth. Churn decreases as the product matures.
Investors don’t expect you to predict the future accurately. They expect you to have thought about the range of outcomes and what drives the difference between them. A founder who presents three scenarios and can articulate what moves the business from conservative to base to aggressive is far more credible than one who presents a single optimistic line and defends it as “what we’re going to do.”
The best financial planning tools build this into the model natively — a single scenario toggle that cascades through every calculation sheet, so you’re not maintaining three separate spreadsheets that inevitably fall out of sync.
Benchmarks to Calibrate Against
Even with a bottoms-up model, founders often don’t know whether their projections are in a credible range. Here are some benchmarks for SaaS companies at different stages:
Pre-product-market fit: 5–15% MoM revenue growth is typical. If your model shows 30% MoM before you’ve achieved repeatable sales, it needs justification.
Post-product-market fit: 15–25% MoM is strong. The top quartile of seed-stage companies grow at this rate.
Scaling: 10–15% MoM is solid for companies above $1M ARR. The “triple triple double double” framework (3x growth in Year 1 and 2, 2x in Year 3 and 4) provides a useful long-term benchmark.
If your model projects growth well above these ranges, you need a specific explanation for why your company is an outlier. Without it, investors will assume the projections are aspirational rather than analytical.
When you don’t know a number, the honest approach is to use a benchmark and be transparent about it. Tools that provide industry benchmarks during the modelling process — offering to pre-fill when founders skip a question — produce models that are explicit about what’s data and what’s assumption. That transparency is a feature, not a weakness.
Radley Finance builds your projections from real inputs — pipeline, conversion rates, pricing, and churn — so every number has a reason behind it. When you don’t know a number, the Discovery Engine offers industry benchmarks with confidence scoring, so your model is transparent about what’s data and what’s assumption. The scenario toggle gives you Conservative, Base, and Aggressive cases from a single model in under an hour.
Start building investor-ready revenue projections with Radley Finance now
This post is the second installment in a series on fundraising and investment for founders. Last week we covered the 7 assumptions VCs attack first, and next week we will be looking at the due diligence questions that kill deals.
