What is Trial Conversion Rate?
Formula
Trial Conversion Rate = Trials Converted ÷ Trials Started (same cohort) × 100
- Trials Started
- Trials beginning in the period, after removing obvious duplicates and test accounts
- Trials Converted
- Trials from that same cohort that became paying subscriptions — not conversions occurring in the period from any cohort
- Same cohort
- The two figures must describe one group of trials. Dividing this month's conversions by this month's starts compares different populations
Worked example
A product with a 14-day trial. 620 trials started in May, measured once every trial had run its course.
| Step | Value |
|---|---|
| 1Trials started in May | 620 |
| 2Duplicate and internal accounts removed | 34 |
| 3Qualified trials | 586 |
| 4Converted to paid | 142 |
| 5Trial conversion rate | 24.2% |
| 6Naive rate over unfiltered starts | 22.9% |
| 7Trials completing a core setup step | 301 |
| 8Conversion among activated trials | 43.5% |
Result
24.2% overall, and 43.5% among trials that completed setup. The activation split is the actionable half: the headline rate is mostly a statement about how many trials got started, not about how persuasive the product is once it is running.
A funnel metric that keeps getting filed under churn
The most consequential thing about trial conversion is where it does not belong. A trial that ends without converting is a funnel outcome, not a cancellation, and folding it into churn produces a metric that worsens every time marketing succeeds. Run a campaign that doubles trial starts and a churn number containing trial expiry will spike the following month with no change in how paying customers behave.
The standard treatment is to exclude trials from MRR and from active-customer counts until they convert, so a trial appears in your metrics for the first time as a new MRR event on conversion. Whether trialing subscriptions count as active is a policy — Bastle's metrics engine exposes it as one — but excluding them is the convention, and it is the choice that keeps churn interpretable.
Cohort the numerator and denominator
Dividing this month's conversions by this month's trial starts is the standard implementation error, and it is wrong in a specific direction. With a 14-day trial, most conversions in a given month originate from trials started in the previous one. Grow trial volume and the denominator inflates ahead of the numerator, so conversion appears to collapse. Cut trial spend and it appears to surge.
The fix is to fix the cohort: take the trials that started in a period, wait until every one of them has resolved, then measure. This means the current month's figure is never final, which is inconvenient and honest. Baremetrics handles the small-sample end of the same problem by refusing to report trial conversion at all below roughly ten trials and thirty days of history — a reasonable guard, and a reminder that on low volume this metric is mostly noise.
Card-required and card-optional are different products
Requiring a card up front and not requiring one produce numbers that should never appear on the same chart. Card-required trials filter hard at the front: far fewer starts, and a much higher share convert, since providing payment details is itself a purchase signal. Card-optional trials produce many more starts at a much lower rate, and include a meaningful population who were never going to buy.
Neither is better. What matters is paying customers per unit of acquisition spend, not the conversion percentage — a card-optional funnel at 12% can produce more customers per pound than a card-required funnel at 45%. Judge the two on CAC, and never compare your rate to a published benchmark without knowing which model produced it.
Activation is where the answer usually is
The headline rate blends two very different populations: people who used the product and people who signed up and never returned. Split by whether a trial completed a core setup action — connected a data source, invited a teammate, imported real data — and the two halves typically differ by a factor of two or more, as in the worked example.
That split is what makes the metric actionable. A low overall rate with high activated conversion is an onboarding problem: the product persuades everyone who reaches it, and too few reach it. A low activated conversion rate is a product or pricing problem, and no amount of onboarding work will fix it.
Conversion quality, not just quantity
A conversion rate says nothing about what converted. Aggressive discounting at trial end raises the rate and can lower the value of what it produces — worse first-month ARPU, and often worse retention, since a customer who needed 40% off to start is disproportionately likely to leave when it expires. Follow every trial cohort into its first three months of retention. A funnel change that lifts conversion by five points while halving month-three retention has made the business worse, and only the cohort view will show it.
Where Trial Conversion Rate goes wrong
- Counting expired trials as churn. This makes retention deteriorate every time acquisition succeeds, and sends the retention team after a marketing outcome.
- Dividing this month's conversions by this month's trial starts. Most conversions come from the previous cohort, so the rate falls whenever trial volume grows and rises whenever it is cut.
- Reporting the current period's rate as final. Trials started recently have not finished, so the figure only stabilises once the whole cohort has resolved — mark in-flight periods as provisional.
- Comparing a card-required rate to a card-optional one. The two funnels filter at completely different points and routinely differ by twenty or thirty percentage points on identical products.
- Optimising the rate with end-of-trial discounts without following the cohort. Conversions bought with a heavy discount convert worse into month three, so the funnel improves while the business does not.
- Reading the metric at low volume. Below a few dozen trials per period, a handful of conversions moves the rate by several points, which is why some tools decline to report it under roughly ten trials.
Typical ranges
Published trial conversion rates vary so widely that a single benchmark is not meaningful: the card-required and card-optional models filter at different points in the funnel and routinely differ by twenty or thirty percentage points on the same product, and self-serve and sales-assisted trials differ again. Compare only against your own prior cohorts on the same trial design, and judge changes on customers acquired per unit of spend rather than on the percentage itself.
Related
Metrics that move with this one
No metric explains a business on its own. These are the figures that qualify, offset or explain Trial Conversion Rate.
Churn Rate
Churn rate is the share of customers or recurring revenue lost over a period, most often calculated as the number of customers who cancelled during a month divided by the number active at the start of it. There is no single correct churn rate: customer churn and revenue churn, gross and net, and start-of-period and average denominators all produce different figures from identical data, so a churn rate is only interpretable alongside the definition that produced it.
Learn moreLogo Churn
Logo churn is the share of customer accounts lost over a period, counting each account once regardless of what it paid. It is the customer-count view of churn, and comparing it to revenue churn reveals whether the accounts leaving are larger or smaller than average — the two rates diverging is usually more informative than either level on its own.
Learn moreCohort Analysis
Cohort analysis groups customers by when they started and tracks each group separately over elapsed time, producing a triangular table where rows are signup periods and columns are months since signup. It exposes what an aggregate churn rate cannot: whether retention is improving for newer customers, where in the lifecycle customers leave, and whether a flat headline number is hiding a deteriorating base propped up by durable older cohorts.
Learn moreRetention Rate
Retention rate is the share of customers or revenue from the start of a period that is still present at the end, calculated as 100% minus the churn rate over the same period and definition. Customer retention rate is bounded at 100%, while net revenue retention can exceed it, so the two are not interchangeable despite both being described as retention.
Learn moreNet New MRR
Net new MRR is the change in monthly recurring revenue over a period, calculated as new plus expansion plus reactivation MRR, minus contraction and churned MRR. It is the single figure that reconciles opening MRR to closing MRR, and its value comes less from the total than from the five components underneath it, which distinguish a business growing from acquisition, from expansion, or merely replacing what it loses.
Learn moreCustomer Acquisition Cost (CAC)
Customer acquisition cost (CAC) is the total sales and marketing spend required to win one new customer, calculated by dividing that spend over a period by the number of new customers acquired in it. The figure changes materially with the definition chosen: paid CAC counts only media spend against paid-attributed customers, fully-loaded CAC adds salaries, commissions and tooling, and blended CAC divides total spend by every new customer including the ones who arrived organically.
Learn moreAverage Revenue Per User (ARPU)
Average revenue per user (ARPU) is monthly recurring revenue divided by the number of active paying customers, giving the blended monthly value of a single account. It is frequently written ARPA — average revenue per account — and the distinction matters for any product where one account contains several seats, because dividing by seats and dividing by accounts produce different numbers and answer different questions.
Learn moreTrial Conversion Rate: frequently asked questions
How do I calculate trial conversion rate?
Is an expired trial churn?
What is a good trial conversion rate?
Should I require a credit card for the trial?
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