Revenue Projection Templates — Formulas for SaaS, D2C, and Marketplace Models

#Two Approaches to Revenue Projection — Top-Down vs. Bottom-Up

The top-down approach derives revenue by multiplying market size by an assumed market share. It's fast, but low on credibility. The bottom-up approach starts from a single unit of transaction and builds up from there. It takes longer to put together, but it's the form reviewers and investors can actually verify.

#SaaS — The Cumulative ARR Formula

Because SaaS runs on monthly or annual subscriptions, ARR (Annual Recurring Revenue) is the core metric. New signups, churn, and upsells all move ARR, so you need a cumulative model with churn built in — not a simple 'customers x price' calculation.

The biggest trap in SaaS revenue projections is setting churn to zero, or to some unrealistically low number. Industry-average monthly churn runs 5-7% for B2C SaaS and 1-3% for B2B SaaS, and early-stage products often run even higher. Validate your own number against your first six months of cohort data.

#D2C — The Order Value x Purchase Frequency Formula

For direct-to-consumer (D2C) brands — think e-commerce, food, or household goods — revenue per order and repeat purchase frequency are what matter most. The ARR concept doesn't really apply here; instead, AOV (average order value), purchase frequency, and repeat purchase rate are the key variables in your revenue projection.

#Marketplaces — The Two-Sided GMV Formula

A marketplace connects suppliers and consumers and takes a cut of the transaction value (GMV) as a fee. The structure is revenue = GMV x take rate, and GMV often carries more signal than revenue itself.

The single most important thing to validate in a marketplace revenue projection is liquidity. The platform only works if transactions per supplier and transactions per consumer both clear a minimum threshold each month. Without explicitly stating your liquidity assumption, you end up with a GMV number that looks big but isn't grounded in reality.

#Scenario Analysis — Base, Best, and Worst Case

A single revenue number collapses the moment one assumption breaks. Presenting three scenarios together — base, best, and worst case — makes your projection far more credible.

#Validating Your Assumptions — Sources and Data Backing

Every assumption behind your revenue projection needs a source. 'It'll probably be like this based on experience' isn't an assumption — it's a guess. Validated assumptions come from one of four sources.

1. Your own product's beta or MVP data — the strongest evidence available
2. Published KPIs from competitors or comparable companies — investor materials, press coverage, industry reports
3. Industry-average statistics — from sources like Statistics Korea, KOSIS, or the Korea Electronics and Telecommunications Industry Association
4. Expert interviews or surveys — with the interview dates, sample size, and respondent profile clearly stated

For a core assumption like 'a 1.5% conversion rate,' cite the source right next to the number in your table. Example citation formats: 'Domestic e-commerce average conversion rate of roughly 1-2% (measured over a 5-week internal beta)' or 'Based on a competitor's publicly disclosed investor materials.'

#Six Common Mistakes

1. Presenting only a hockey-stick curve — flat in Years 1-2, then a sudden spike in Year 3 — with no supporting rationale
2. Assuming 0% churn, as if every new customer stays forever
3. Arbitrarily setting a conversion rate more than 2x the industry average
4. Assuming linear growth in new signups without any corresponding increase in marketing spend
5. Ignoring seasonality or shifts in the market environment
6. Presenting only the base case and omitting worst- and best-case scenarios

#Self-Check: A Founder's Checklist

1. Which category does your business fall into — SaaS, D2C, marketplace, or a hybrid?
2. Did you use the formula appropriate to that model? (Avoid a plain 'customers x price' shortcut.)
3. Does every variable in your bottom-up formula have a source or direct measurement behind it?
4. Are worst-case, base-case, and best-case scenarios all presented together?
5. Do your churn, repeat purchase, and conversion rate assumptions fall within industry-average ranges?
6. Are your Year 1-3 projections tied to a corresponding increase in marketing spend?
7. Is the gap between your top-down and bottom-up results within a reasonable range (under 10x)?