See how many trials turn into paying customers, the MRR they add each month, and how many signups you need to hit a target - so you know whether to fix the trial or pour in more traffic.
How many people start a trial each month.
Share of trials that become paying customers.
$ MRR each converted customer adds.
Monthly new MRR you want this funnel to add.
New customers and new MRR at different conversion rates, holding your signups and ARPA fixed. This is where points of conversion turn into dollars.
| Conversion rate | New customers /mo | New MRR /mo | vs goal |
|---|
no card up front, self-serve
card on file, auto-converts
free plan, not a time-boxed trial
Source: ChartMogul SaaS Conversion Report, 2026. See our trial conversion rate definition for the full benchmark breakdown.
New MRR moves on two inputs: how many people start a trial, and the trial conversion rate - the share that converts. Traffic adds signups at the top. Conversion turns the signups you already have into customers.
Conversion compounds, because it works on every future signup too, not just this month's. Raising it is usually cheaper than buying more traffic: a few points of conversion lift every cohort you will ever acquire, while more traffic has to be paid for again every month.
New MRR = signups × conversion% × ARPA Example: 400 signups at 25% conversion and $60 ARPA:
If your conversion sits below the opt-in benchmark, the culprit is usually time-to-value in onboarding: users sign up but never reach the point where the product clearly helps them. Small onboarding wins - a guided first action, a shorter path to value - beat more ad spend, because they lift every cohort instead of buying a one-off bump.
Once conversion is healthy, more qualified signups scale new MRR in a straight line. Instrument the funnel - signups, activation, conversion - so you know which lever you are pulling and can tell a traffic problem from a product one before you spend.
For an opt-in trial with no card up front, roughly 15-18% is a common benchmark, with great performers reaching 20% or so. An opt-out trial that requires a card converts far higher, often around 50% and up to 60% at the top end, because it auto-bills when the trial ends. Freemium is a different model and sits much lower at roughly 2-5%. The right target depends on your motion and how qualified your signups are. (Source: ChartMogul SaaS Conversion Report, 2026.)
Opt-out trials, where a card goes on file up front and billing starts automatically, convert far higher because the default is to keep paying. They draw fewer signups, but the ones you get are more qualified. Opt-in trials cast a wider net and convert lower, since the user has to take an active step to pay. Neither is wrong - they trade volume against conversion.
Shorten time-to-value so users reach the point where the product clearly helps them. Guide activation in onboarding instead of dropping people on an empty dashboard, and trigger the upgrade prompt on the 'aha' action rather than the calendar date. Our guide to SaaS customer onboarding walks through the activation steps that move this number.
Time-boxed trials convert higher per signup because there is a deadline and the user has already shown intent. Freemium converts a low single-digit percentage of free users to paid, but it runs on a much larger top of funnel, so the absolute numbers can still be large. Trials optimise conversion rate; freemium optimises reach. Pick the one that matches how buyers evaluate your product.
Seven to fourteen days is the common range. It needs to be long enough for users to reach real value and short enough to create urgency to act. Match the length to your actual time-to-value: if people hit the 'aha' moment on day two, a 30-day trial just delays the decision and invites drift.
No. This is gross new MRR added each month from converted trials, before anyone cancels. Churn is what sets the ceiling on where your MRR finally settles, so a healthy conversion rate paired with high churn still caps your growth. See churn rate to model the other half.
Divide the trials that became paying customers by the total trials that started, then multiply by 100. So 100 conversions from 400 trials is a 25% rate. The one decision that changes the number is the measurement window: counting conversions against the same month's signups understates the rate, because some trials convert after the period closes. Baremetrics and most analytics tools use a 30-day rolling version - conversions in the last 30 days divided by new trials in the last 30 days - to smooth out the daily noise.
Most of the action happens around the trial deadline, not spread evenly across it. ChartMogul and Baremetrics data shows conversions cluster as the trial expires, and incremental conversion after roughly day 14 falls to about 1% - users who have not reached value by week two rarely come back to pay. That is why time-to-value in the first seven days matters more than trial length: if the 'aha' moment lands early, the deadline does the rest.
Directly, because CAC is acquisition spend divided by customers won, and conversion sets the denominator. If a trial costs £100 to fill, 10% conversion means £1,000 per paying customer; doubling to 20% halves it to £500 - same spend, twice the customers. A few points of conversion cuts CAC across every future cohort, which is usually cheaper than buying more traffic. See our LTV:CAC ratio guide for how that flows into payback and unit economics.
Modelling is step one. Mowt reads your Stripe data and shows the new MRR your funnel actually adds each month - conversion, new customers and net growth after churn, updated daily - so you know which lever is working.
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