What a good trial-to-paid conversion rate looks like
"Is our trial conversion good?" depends on two things: which model you run, and what you count as a trial. Here are the benchmarks worth knowing, and two cases where the benchmark said one thing and the data said another.
"Is our trial conversion good?" sounds like a simple question. It isn't, until you pin down two things: which conversion you mean, and what you're counting. Then the benchmarks become useful, and then the more useful number turns out to be your own.
First, which conversion
Three different numbers get called trial conversion, and they don't compare.
Free trial, no card. Anyone can start a trial with an email address. The rate is paying customers divided by trials started.
Free trial, card required. People enter payment details to start, and the trial turns into a subscription unless they cancel. Fewer people start, and far more of them pay.
Freemium. There's no end date. Free-to-paid conversion is the share of free users who ever upgrade, usually read over a fixed window.
Then check the denominator. Trials started, sign-ups, or users who activated? A team that counts only activated trials will report a much better rate than one that counts every sign-up, for the same funnel. And check the window: 14 days is the most common trial length in ChartMogul's 2026 report.
The benchmarks
The most useful set I know comes from Kyle Poyar and Lenny Rachitsky, who collected data from more than 1,000 products with OpenView and Pendo:
- Freemium, self-serve: 3-5% is good, 6-8% is great
- Freemium with sales assist: 5-7% is good, 10-15% is great
- Free trial: 8-12% is good, 15-25% is great
ChartMogul's SaaS Conversion Report from March 2026, built on 200 B2B software products, puts the median free-to-paid rate at 8%, and adds a warning in the same breath: very few products actually sit at 8%. The spread is wide. Its sharpest split is the card: trials that require a credit card convert at 30%, more than five times the rate of trials that don't.
So before you compare yourself to anything, find your row. A 10% no-card trial is a good result. A 10% card-required trial is far below what that model usually reaches.
A higher rate isn't always more customers
The card split tempts teams to switch models. But asking for a card up front raises the rate partly by filtering who starts a trial at all. The number to compare is paying customers per visitor to your sign-up page, not the trial rate on its own.
Run both models through the same funnel math before you decide, and if you can, test the change on new traffic instead of switching everyone at once. A rate that triples while trial starts fall by more than two thirds is fewer customers, not more.
Your own history beats any benchmark
Deviniti Apps builds apps for the Atlassian Marketplace. When we started working on trial conversion, around 30 trials a month arrived and fewer than three in ten converted.
Before anyone touched the product, Deviniti's own data had to settle one question: was this a retention problem or an activation problem? Retention came back healthy. Revenue from new customers consistently exceeded churn, and every customer segment stayed beyond twelve months. Activation was where the money was leaking.
Read that against the free-trial row and 26% looks great. Read it against the team's own history and it was a leak: the same products had peaked above 50% before. That gap, not the benchmark, was the reason to act.
Three months later trial-to-paid conversion was 59%, and monthly recurring revenue had grown 33% in the same quarter. The fix came from one behavior in week three of the trial that every paying customer shared. The Deviniti case study shows how the team found it.
An average can hide two products
UX Pilot, an AI design tool with more than a million users, runs on freemium. Its free-to-paid rate was around 3% when I joined as Head of Product, at the bottom of the good band for self-serve freemium.
Split the users, though, and the average fell apart. In their first seven days, users who never triggered Deep Design, a higher-fidelity generation mode, converted at 1.3%. Users who did converted at 12.3%. The mode existed. It just wasn't on by default for new users.
Turning it on by default became an A/B test on 120,000 users over 11 days, and free-to-paid conversion rose 44.7%, with a 96.2% probability that the change won. Across the whole engagement, free-to-paid went from about 3% to over 4.5%. The benchmark said fine. The split said where the money was. The UX Pilot case study has the test card.
How to use a benchmark without chasing it
- Find your row. Trial with or without a card, freemium self-serve or sales-assisted. Compare yourself with nothing else.
- Build your own baseline. Monthly cohorts, the same window, the same denominator, as far back as your data goes. Your best months are a better target than anyone's median.
- Split converters from non-converters. Look for the early behavior the paying users share and the others don't. That behavior is the number your team can move. Here's how to find it.
- Test one change against it, and read the result in revenue. A conversion lift that never shows up in revenue is a lift in something else.
If your rate is below where it used to be and you're deciding what to buy, I compared an onboarding audit with an activation sprint. The same logic holds after the sale: what good retention looks like.
The Funnel Conversion Sprint is this method run for one quarter: find where the trial leaks, learn why, and put a new experiment live every week.
