Trial Conversion Rate Calculator — Free Trial to Paid

Background.

Twelve hundred people start a free trial and 156 of them end up paying. That is a 13% trial-to-paid rate, and it is where most dashboards stop. It cannot tell you whether the other 1,044 never got the product working or got it working and decided it was not worth the money, and those two failures call for opposite fixes.

So the cohort is split at activation: the first action in your product that reliably comes before a purchase, such as connecting a data source, inviting a colleague or finishing one real piece of work. Dave McClure made this stage a separate measurement in his 2007 "Startup Metrics for Pirates" talk, the source of the acquisition–activation–retention–referral–revenue funnel. If 780 of the 1,200 activate, the activation rate is 65% and 20% of activated trials buy; multiply the two and you are back at 13%. A weak first rate beside a healthy second points at onboarding and at who gets into the trial in the first place. The reverse points at value or price, which no onboarding work will fix.

The calculation also looks one step past the sale. Of the 156 who paid, 132 were still paying after their first renewal, so 84.62% of conversions held and 11% of the original trials became lasting customers. A trial tuned to convert people the product then loses shows the first rate climbing while the second slides.

There is no benchmark built in, deliberately. Published trial conversion figures seldom say how the trial was run, above all whether a credit card was required to start it, and that single choice changes who enters the denominator. Your own earlier cohorts, run the same way, are the comparison that means something.

What is trial conversion rate calculator?

A trial conversion rate is the share of free trials that become paying subscriptions, measured on a cohort: trials that all started in one window and have all reached the end of their trial period. No accounting standard defines it, so two companies can report the same product's rate very differently. If you publish it, say how it is built; the US Securities and Exchange Commission's 2020 guidance on key metrics in MD&A asks public companies to give each metric's definition and method of calculation.

How to use this calculator.

1. 01Choose a window of trial start dates and wait until every trial that began in it has ended. Enter how many started.
2. 02Enter how many reached your activation milestone. If you do not track one, enter the same figure as trials started; activation then reads 100% and the other rates are unaffected.
3. 03Enter conversions, counted when the first payment succeeds rather than when a card is entered, since failed payments sit between the two.
4. 04Enter how many were still paying after their first renewal. If that date has not arrived yet, enter the conversion count again and ignore the two retention figures for now.
5. 05Read activation and activated-to-paid together to see where the cohort is lost, and multiply trials per customer by your cost per trial to get the acquisition cost of a paying customer.

The formula.

Four counts give six ratios. With S trials started, A activated, C converted and R still paying after the first renewal: trial-to-paid = C ÷ S × 100, activation = A ÷ S × 100, activated-to-paid = C ÷ A × 100, trials per customer = S ÷ C, trial-to-retained = R ÷ S × 100 and first-renewal retention = R ÷ C × 100. The middle two multiply back to the first, (A ÷ S) × (C ÷ A) = C ÷ S, so the split accounts for the whole of the headline rate rather than sitting beside it. The counts must nest: A cannot exceed S, C cannot exceed A, and R cannot exceed C. The second rule assumes every buyer passed the milestone, so if you have more conversions than activations, people are buying without reaching it and the milestone is not on the path to purchase. At least one conversion is needed, since with none the rate is simply zero and trials per customer has no value. Results are shown to two decimal places.

A worked example.

Frequently asked questions.

References& sources.

1. [1]Zhang, L., & Duan, J. (2025). "Longer or shorter? A large-scale randomized field experiment on the impact of free trial duration on sustainable user conversion in the Freemium model." Frontiers in Psychology, 16, 1568868. DOI 10.3389/fpsyg.2025.1568868. Peer-reviewed, open access. Two-year randomised controlled trial across 680,588 new users in 190 countries comparing 3-day and 7-day trials: +11.098% trial adoption, +42.36% delayed conversion, +20.92% overall subscription rate, and NO statistically significant effect on immediate conversion. Retrieved 29 July 2026; figures verified against the article.
2. [2]Foubert, B., & Gijsbrechts, E. (2016). "Try It, You'll Like It—Or Will You? The Perils of Early Free-Trial Promotions for High-Tech Service Adoption." Marketing Science, 35(5), 810–826. DOI 10.1287/mksc.2015.0973. Peer-reviewed; PAYWALLED at the publisher — abstract and findings verified, full text not retrieved. Independent second authority: finds free trials to be a double-edged sword, since a disappointing trial can alienate a prospect permanently rather than merely failing to convert.
3. [3]McClure, D. (8 August 2007). "Startup Metrics for Pirates: AARRR!" Presented at Ignite Seattle. Origin of the Acquisition–Activation–Retention–Referral–Revenue decomposition that this calculator's activation stage implements. Recorded talk; the original slide deck circulates in several mirrors rather than at a single canonical URL.
4. [4]Jordan, J., Hariharan, A., Chen, F., & Kasireddy, P. (21 August 2015). "16 Startup Metrics." Andreessen Horowitz. Source for the cohort discipline that underlies the denominator warning on this page, and for the distinction between a booking and a durable customer. Retrieved 29 July 2026.
5. [5]U.S. Securities and Exchange Commission (30 January 2020). Release No. 33-10751, "Commission Guidance on Management's Discussion and Analysis of Financial Condition and Results of Operations." Requires a registrant presenting an operating metric to disclose its definition and method of calculation — directly relevant to a metric whose value swings by an order of magnitude on whether a credit card was required. sec.gov returns HTTP 403 to automated fetchers; release identifiers verified independently.