How we estimate private-company revenue
Private companies don't publish their revenue, and the numbers scattered online are usually one unsourced figure copied from site to site. We wanted something better: a repeatable, testable estimate you can reason about — with the error bars shown.
Estimate it two independent ways, then reconcile
There's no single public number that gives away a company's revenue. But there are two independent views of it — we build both and reconcile them into one range.
The website funnel
How much traffic does the site get, and what share is real acquisition traffic vs. logged-in users? Published SaaS conversion rates turn that traffic into signups, then paying customers — multiplied by the company's listed pricing, an estimate of self-serve revenue.
The demand model
How much do people search for the brand, how mature is the company, how long has it existed? A model trained on companies whose revenue is public turns those demand-and-scale signals into a total-revenue estimate — including the enterprise, sales-led revenue a website funnel can't see.
We reconcile the two. For a self-serve product, the funnel leads and the demand model confirms. For an enterprise, sales-led company, the demand model leads and the funnel is a floor. The result is a single range.
We look things up — we don't invent them
Every rate in the funnel — visitor-to-signup, signup-to-paid, churn, customer lifetime, tier mix — is a published industry benchmark, not a knob we tuned to get a nice answer. Only the demand model is fitted, and it's fitted on real reported revenues. That discipline is what keeps the estimates honest instead of overfit.
Where a real number exists, we use it
For a subset of companies, revenue has been credibly reported — press, filings, or reputable data providers. Those pages show the reported figure with a Verified badge, not our estimate. Another group blends a third-party estimate with our model. The rest are pure model estimates, clearly labeled as such. We never call an estimate "reported."
How accurate is it?
This is the part most 'revenue estimate' sites skip. Because we know the real revenue for a set of these companies, we can grade ourselves: hide the real number, run the full model as if we'd never seen it, and compare.
The typical estimate lands within about 1.5× of the real figure.
Roughly 7 in 10 estimates fall within 2× of the reported number.
Nearly all land within 3× — the wide tail is large, enterprise-heavy companies.
It's tightest for self-serve companies (whose revenue shows up in web traffic) and loosest for large enterprise companies (whose revenue is nearly invisible to public signals). For estimating private-company revenue from public data, that's genuinely strong — and, more importantly, we show you the number rather than claiming certainty.
Ranges and confidence, not a single number
Every estimate is a range with a confidence label. Read the range, not the midpoint.
A real reported anchor, or two methods that agree closely. Narrow range.
Solid public signal, or a third-party estimate blended with the model.
Thin public signal — the range is wide on purpose. Treat it as a rough order of magnitude.
What this is — and isn't
- A transparent, testable estimate built from public signals and published benchmarks.
- Updated as traffic, pricing, and reported figures change — see 'last updated' on each page.
- Not audited financials, insider information, or a company-confirmed number.
- Not a precise figure — anyone quoting a single exact revenue for a private company is guessing.
Spot something off?
If you work at one of these companies and have a correction, tell us — we'll update the page.