Click any number to adjust it, or drag conversion rate sliders for live updates Tap any stage to adjust its number or conversion rate
A SaaS conversion funnel calculator turns a customer target into a traffic and activation plan. The funnel above is the SaaS preset — Website Visitors → Sign Up → Onboarding Started → Onboarding Complete → Active User → Paid Conversion. Set the number of paying customers you need this quarter, put your real rates on each stage, and the calculator shows how many visitors that takes and how many signups will stall in onboarding along the way.
It runs forwards too: anchor Website Visitors at what your channels deliver and read the forecast at Paid Conversion. Because every stage is visible, the model makes the usual SaaS argument concrete — a few points of activation rate are worth more than a lot of top-of-funnel traffic. Everything is free and runs in the browser; nothing is uploaded unless you choose to save or share it. Other pipelines: sales, e-commerce, recruiting.
There are three honest ways to use a funnel calculator. They look the same but the workflow differs:
For all three, the calculator's 8 presets (Recruiting, Sales, SaaS, E-commerce, Marketing, Fundraising, Support, Product Adoption) give you a labeled starting point — duplicate a preset, rename stages, and tune the rates.
Benchmarks are starting anchors, not targets. Your own data — once you have a few months of it — beats any industry average. The ranges below come from public B2B/B2C reports and are intended to help you sanity-check whether a stage is in the ballpark or is genuinely an outlier.
| Stage transition | Typical range |
|---|---|
| Visitor → Free trial or signup | 2–5% |
| Visitor → Demo request (sales-led) | 1–3% |
| Signup → Onboarding started | 60–80% |
| Onboarding started → Activated | 30–50% |
| Activated → Paid (freemium) | 3–8% |
| Trial → Paid (opt-in, no card) | 15–25% |
| Trial → Paid (card required) | 40–60% |
| Demo → Closed-won (sales-led) | 15–25% |
If your stage is materially below the bottom of the range, there's a real problem to investigate. If it's near the middle, the stage is performing in line with the industry and you'll get more leverage by improving a stage that's further below. If it's above the top of the range, double-check your tracking — measurement bugs are more common than truly exceptional funnels.
Most funnels fail in predictable ways. These are the four patterns you'll see most often:
Every downstream stage is healthy but the absolute output is too low. Conversion rates won't fix this — you need more leads, more applicants, more sourced candidates, or more traffic. Look upstream at acquisition channels, not at process changes.
One stage early in the funnel drops dramatically below benchmark — typically the first qualification step. This usually means the top-of-funnel sources are not well-matched: the right people aren't applying, demoing, or shopping. Tighten the targeting upstream rather than building more downstream process.
A stage in the middle (typically demo-to-trial, second-interview-to-onsite, or evaluation-to-decision) has a low rate that's also volatile month-to-month. This is usually a process or follow-through problem: handoffs between teams, missing nurture, or slow response time after initial engagement.
The final stage trends downward over multiple quarters. Pricing, competition, or product-market fit drift cause this — not "bad sales execution." When you see this pattern, the conversation belongs at the strategy level, not the rep performance level.
A SaaS conversion funnel is the sequence of steps a visitor takes to become a paying customer — typically visit, sign up, start onboarding, complete onboarding or activate, become an active user, and convert to paid — together with the percentage that makes each step. A calculator puts a rate on every transition so you can work backwards from a paid-customer target to the visitors you need, or forwards from your traffic to a revenue forecast.
Roughly 2–5% of website visitors sign up for a trial or free plan, 60–80% of signups start onboarding, 30–50% of those reach activation, and then the free-to-paid step depends on the model: 3–8% for freemium, 15–25% for opt-in trials with no card, 40–60% for trials that take a card up front. Sales-led products see 1–3% visitor-to-demo and 15–25% demo-to-closed-won. Compounded, 0.5–2% of visitors becoming paying customers is a normal self-serve result.
It depends almost entirely on whether the trial asks for a card. Opt-in trials with no card convert 15–25% of trialists; card-required trials convert 40–60% but have far fewer trialists to begin with. Freemium free-to-paid is lower still at 3–8%. Compare like with like, and judge the funnel on paid customers per visitor rather than on the trial step alone — a stricter trial often produces fewer paying customers overall.
Activation is the moment a new user first does the thing your product is for — the action that predicts they will keep using it. In the calculator it is the Onboarding Complete to Active User step. It matters because everything downstream is multiplied by it: with 1,000 users starting onboarding, moving activation from 30% to 40% adds 100 active users and, at an 8% free-to-paid rate, eight paying customers — the same gain as finding 330 more users to start onboarding.
Set the Paid Conversion stage to your target and read the Website Visitors stage. With benchmark rates — 5% signup, 60% start onboarding, 30% complete it, 50% become active, 8% pay — 50 paying customers need about 140,000 visitors. Improving any single rate reduces that number in proportion, which is how the calculator turns a growth target into a choice between more traffic and better onboarding.
Onboarding. Signup to activation is where most self-serve products lose the majority of their users, and it is the stage with the most volatile rate month to month because it responds to every product change. A low visitor-to-signup rate is usually a positioning or pricing-page problem; a low activated-to-paid rate is a value or packaging problem. Compare each stage with the benchmarks above and start with the largest gap.