`Trial to Paid Rate = Trials Converted to Paid / Trials Started in the Cohort`
Cohort by start date or the number lies
The single most common error is dividing this month's conversions by this month's signups. Those are different populations. A trial that starts on the 28th cannot convert inside the same month, so a growing signup month mechanically depresses the rate and a shrinking one inflates it.
Cohort by trial start, then measure conversion at a fixed horizon past the trial length. If the trial is 14 days, a 30 day window captures the users who convert after a short lapse. Report the same window every period.
Segment before you compare
| Cut | Why it matters |
|---|---|
| Opt in vs opt out | Card up front changes the rate by a large multiple |
| Self serve vs sales assisted | Different cost structure and different expectations |
| Company size band | Enterprise trials often precede a procurement cycle |
| Acquisition channel | Channel quality shows up here before it shows up in revenue |
Where the rate is actually won
Conversion is decided by what happens in the first days of the trial, not by the renewal prompt at the end. Identify the activation milestone that separates retained accounts from abandoned ones, measure the share of trials reaching it, and measure how fast they get there. That share moves the conversion rate more reliably than trial length or in app messaging.
Second lever is trial length itself. Longer trials give buyers more room to defer, and a deferred evaluation follows the same failure path as a stalled sales deal.
Feeding it into the forecast
Trial cohorts are one of the few forward looking inputs available before an opportunity exists. Applying historical cohort conversion to the trials currently running gives a revenue expectation weeks earlier than pipeline can, which is exactly the visibility that sales forecasting built only on created opportunities misses. Pair the conversion rate with expansion behavior after conversion, since trial sourced accounts frequently start small and grow, and that growth lands in net revenue retention rather than in new business.
Frequently Asked Questions
How do you calculate trial to paid conversion rate?
Divide the trials that converted to paid by the trials that started in the same cohort, measured over a fixed window that extends past the trial length. Cohort by trial start date, not by conversion date. A conversion date cohort mixes trials that began in different months and makes the rate impossible to compare period over period.
Why do opt in and opt out trial conversion rates differ so much?
An opt out trial collects a credit card up front and converts automatically unless the user cancels, so its rate is structurally much higher and its denominator is much smaller. An opt in trial requires no card and lets anyone in, so it produces more signups at a lower rate. The two numbers are not comparable, and a team that switches models mid year will see a rate change that has nothing to do with product or marketing performance.
What is the strongest predictor of whether a trial converts?
Reaching the activation milestone that correlates with retained usage, and reaching it early in the trial. In most B2B products that milestone involves real data being loaded and a second user from the same account being invited. Trials that hit it in the first few days convert at a materially higher rate than trials that hit it on the last day, which is why activation timing is worth reporting alongside the conversion rate.
Should sales assisted trials be measured separately?
Yes. A trial with a rep attached is a different motion with different economics, and blending it with self serve trials produces a rate that describes neither. Report self serve, sales assisted, and opt out trials as separate lines, then look at the mix before reading any movement in the blended number.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like trial to paid conversion rate into prescriptive action for your team.
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