Trial-to-paid conversion is the share of free trial starts that become a paid subscription. In RevenueCat's 2026 dataset the median runs from 25.5% for trials of four days or fewer to 42.5% for trials of 17 to 32 days, from 43.5% in Travel down to 22.2% in Photo and Video, and from 34.2% in North America down to 15.2% in India and South East Asia. Adapty's 2026 dataset reports a global average of 25.6%. There is no single correct benchmark, and the useful question is not whether your rate is good but which slice you should be comparing it against and what it does to the CPI you can afford.
Trial-to-paid is the most quoted and most mangled number in subscription app marketing. The mangling is rarely deliberate. It happens because four different rates in the same funnel all get called conversion, they all produce a percentage somewhere between one and forty, and only one of them has trial starts in the denominator. What follows is the 2026 benchmark data with its definitions attached, an explanation of why the two most credible sources disagree, and the part most benchmark articles leave out: the arithmetic that turns a trial-to-paid rate into a media bid.
What trial-to-paid actually measures
RevenueCat defines trial-to-paid as the share of free trial starts that convert into a paid subscription. The denominator is trial starts. Not installs, not downloads, not paywall views. That one clause is what separates it from the three rates it is most often confused with.
- Install-to-trial divides trial starts by installs. RevenueCat measures the equivalent, which it calls download-to-trial, within 30 days of the download date. Adapty puts its global average at 10.9%.
- Download-to-paid divides paying subscribers by installs, and RevenueCat measures it within 35 days of install. The global median is 2.0%.
- Trial-to-paid divides paying subscribers by trial starts, which is the subject of this page.
- Renewal and retention rates measure what happens to a subscription after the first payment, which is a different question again.
A 2.0% download-to-paid rate and a 25.5% trial-to-paid rate can describe the same app on the same day. Anyone comparing those two figures as though one contradicts the other is comparing a rate per install with a rate per trial. The two rates on the full funnel and how they chain together are covered in the subscription app marketing strategy guide.
The measurement window is the second half of the definition and gets almost no attention. A trial that started yesterday has not had the chance to convert, so any trial-to-paid rate calculated over a period shorter than the trial length plus a settlement buffer is structurally too low. If you run a 14 day trial and read a rate over a rolling 14 day window, roughly the whole denominator is still unresolved. Cohort the trial starts by the day they began, then read the rate once the cohort has aged past the trial length.
Trial-to-paid benchmarks by trial length, 2026
These are medians across apps in RevenueCat's State of Subscription Apps 2026, which covers more than 115,000 apps, over $16 billion in revenue and more than a billion transactions. The top quartile column is the threshold an app has to clear to sit in the best performing quarter of the panel, which is a more useful target than the median if you are already healthy.
| Trial length | Median trial-to-paid | Top quartile |
|---|---|---|
| 4 days or fewer | 25.5% | Above 38.5% |
| 5 to 9 days | 37.4% | Above 52.8% |
| 10 to 16 days | 35.4% | Not published |
| 17 to 32 days | 42.5% | Above 59.4% |
The spread is about 17 percentage points between the shortest and longest bands. It is tempting to read that as an instruction to lengthen your trial, and it is not one. Apps that run long trials differ from apps that run short ones in category, price point, product complexity and audience, and the data cannot separate the effect of the trial length from the effect of being the kind of app that chooses a long trial. Treat this table as a guide to which median applies to you, not as evidence that changing your trial length would move your rate.
Trial-to-paid benchmarks by category
Two independent datasets rank categories here, and it is worth showing both while being clear that they are separate readings.
| Category | Median trial-to-paid | Top quartile |
|---|---|---|
| Travel | 43.5% | Above 62.4% |
| Health & Fitness | 37.7% | Above 51.4% |
| Gaming | 25.0% | Above 39.8% |
| Photo & Video | 22.2% | Above 33.1% |
Those four are the figures RevenueCat states in text. Travel and Health and Fitness lead the category table, Photo and Video sits at the bottom. Adapty's State of In-App Subscriptions 2026, a separate dataset of more than 16,000 apps and $3 billion in subscription revenue, ranks its own categories with Health and Fitness highest at 35.0% and Entertainment lowest at 19.1%. The direction of travel agrees across the two panels. The absolute numbers should not be mixed into one table, for reasons covered in the next section.
Health and Fitness performing near the top on both panels is consistent with what we see in the category. Intent is high at the moment of install, the product delivers something observable within the trial window, and the promise in the ad is easy to make concrete. That is a favourable setup, and it also means a fitness app sitting at 20% trial-to-paid has a genuine problem rather than a hard category. For what the acquisition side looks like in that vertical, the health and fitness CPI benchmarks cover the cost side of the same funnel.
Trial-to-paid benchmarks by market
This is the table that matters most for paid acquisition and the one that is least often published.
| Region | Median trial-to-paid | Top quartile |
|---|---|---|
| North America | 34.2% | Above 47.9% |
| Asia-Pacific | 31.9% | Above 45.6% |
| Western Europe | 29.7% | Not published |
| India and South East Asia | 15.2% | Above 25.0% |
North America converts trials at more than twice the rate of India and South East Asia. Every app that expands its media buying into cheaper markets should expect its blended trial-to-paid to fall, and should expect that fall to have nothing to do with the product, the paywall or the creative.
Take a hypothetical app producing 10,000 trial starts a month, all of them in North America, converting at the regional median of 34.2%. That is 3,420 subscribers. It opens up India and South East Asia and within a quarter half its trial volume comes from there, converting at that region's median of 15.2%. The same 10,000 trials now produce 2,470 subscribers and a blended rate of 24.7%. The app has lost nearly ten points of trial-to-paid and not one thing about it has changed. Whether that is a good trade depends entirely on what it paid for the second half of those trials, which is the only question worth asking.
This is why a blended trial-to-paid rate is close to useless as a management number, and why the first thing to do with a rate that has moved is to split it by market before anybody touches the paywall. The same warning applies to platform and campaign mix. Read the rate the way you would read a CPI or a cost per trial, which is to say never in aggregate.
Why two credible reports publish different trial-to-paid numbers
RevenueCat's trial-length medians and Adapty's 25.6% global average are both defensible and they are not comparable. Three things separate them, and none is that one of the two is wrong.
Different panels
RevenueCat reports on more than 115,000 apps and Adapty on more than 16,000. These are the customer bases of two different subscription infrastructure providers, which means they are samples of whoever chose that vendor, not samples of the App Store. Neither claims otherwise.
Medians against averages
RevenueCat publishes medians with top quartile thresholds. Adapty publishes a global average. On a distribution as skewed as subscription conversion, where the top quartile of apps converts at roughly one and a half times the median, those two statistics answer different questions. A median tells you what the typical app does. An average tells you what the panel does in total, and it moves when a handful of large apps move. Setting one against the other as though a gap between them meant something is a category error.
One of them does not publish its window
RevenueCat states its denominators and windows, including the 30 day window on download-to-trial and the 35 day window on download-to-paid. Adapty's report page gives the headline figures without publishing the denominator or the measurement window for trial-to-paid. That is not a criticism of the data, but it does mean there is no way to confirm the two are counting the same event over the same period, so they cannot be blended into one table.
Which brings us to the specific trap on this page. Adapty's 25.6% global average and RevenueCat's 25.5% median for trials of four days or fewer are not the same measurement. They sit a tenth of a point apart and they look like two independent sources landing on the same answer. One is an average across a whole panel, the other is a median for one trial-length band within a different panel. If you see them quoted side by side as confirmation of a global trial-to-paid benchmark of about 25%, the article has stacked a coincidence on top of a definitional mismatch. There is no global trial-to-paid median stated in RevenueCat's report at all.
The other number that gets mislabelled
The single most repeated error in this area is the hard paywall figure. RevenueCat's 2026 data shows apps using a hard paywall converting at 10.7% against 2.1% for freemium apps, and that is a download-to-paid rate measured within 35 days of install. The denominator is installs. RevenueCat's own summary post has described it as a trial-to-paid rate, and a great many third-party articles have copied the wrong label forward.
If you quote 10.7% as a trial-to-paid rate you are dividing subscribers by a denominator several times larger than the one the number was built on, and you will conclude that hard paywalls convert trials worse than average when the data says the opposite about installs. Any benchmark you are handed should survive the same three questions: what is in the denominator, over what window, and from which panel.
What a trial-to-paid rate is worth in media terms
Benchmarks earn their place when they change a decision, and the decision trial-to-paid changes is how much you can pay for an install. The chain is short. Install-to-paid is install-to-trial multiplied by trial-to-paid. Allowable CPI is your target subscriber acquisition cost multiplied by install-to-paid. Trial-to-paid sits directly in that multiplication, so a proportional move in the rate is a proportional move in the bid.
The figures below are hypothetical and are not client data. Take an app spending £10,000 a month on Meta at a £2.00 CPI, so 5,000 installs, with an 11% install-to-trial rate producing 550 trial starts at £18.18 each. Hold all of that constant and vary only the trial-to-paid rate across the span of medians in the trial-length table, with a £60 target cost per subscriber for the allowable CPI column.
| Trial-to-paid | Subscribers from 550 trials | Subscriber acquisition cost | Allowable CPI at £60 target |
|---|---|---|---|
| 25.5% | 140 | £71.30 | £1.68 |
| 37.4% | 206 | £48.62 | £2.47 |
| 42.5% | 234 | £42.78 | £2.81 |
Same spend, same creative, same auction, same CPI. Moving trial-to-paid from 25.5% to 37.4% cuts the cost of a subscriber by 32% and raises the CPI the app can rationally pay by 47%, from £1.68 to £2.47. In a competitive category that is frequently the difference between being able to buy volume and being outbid by an app with a better paywall.
The general point is worth stating plainly, because it is the reason this benchmark matters more than the cost benchmarks that get more attention. A percentage improvement downstream of the install is worth the same as the identical percentage improvement in CPI, and it is usually easier to get, because it does not require the auction to cooperate. Cheap installs are not automatically good installs, and an app only benefits from a lower CPI if trial and paid conversion hold up behind it. The full arithmetic for the cost side sits in the guide to calculating subscription app CAC, and the cost ranges themselves are in the 2026 mobile app CPI benchmarks.
One caution on the timing. A subscriber acquisition cost read too early is always too high, because conversions keep arriving after the cohort closes. RevenueCat finds 19.2% of paid conversions landing in week six or later, as a share of conversions rather than of installs, so a cohort judged at day seven is showing you a partial count. How that distorts an early read is worked through in our piece on Day 7 ROAS for subscription apps.
What to do with your own number
The benchmark is a triage tool, not a target. Work through it in this order, because the cheapest fixes are at the top and most apps start at the bottom.
First, check you are measuring it correctly
Confirm the denominator is trial starts. Confirm the window is longer than your trial. Confirm cancelled trials that have not yet expired are not counted as failures, because a 14 day trial cancelled on day one still runs to term and occasionally still converts. A surprising share of alarming trial-to-paid rates are arithmetic rather than product.
Second, split by market, platform and campaign
The regional table above shows a 19 point spread between the best and worst regions. Before concluding anything about the paywall, check whether the blended rate moved because the mix moved. The same applies to platform, where iOS and Android cohorts differ on both cost and quality.
Third, compare the rate across creative concepts, not in aggregate
This is the step most teams skip and it is the one that usually finds the answer. If two concepts deliver trials at the same cost and one converts to paid at half the rate of the other, the problem is the promise in the ad rather than the paywall behind it. That gap is invisible in an aggregate number and obvious the moment you break trial starts out by concept. Ranking creative on cost per trial alone lets the most misleading ad win, which is a theme running through how to lower CPI without wrecking the funnel.
Fourth, then look at the paywall and the trial
Price, plan mix, trial length and paywall timing all sit here, and they are real levers. They are fourth on the list because they are the slowest and most disruptive to change, and because the three checks above will frequently show that the paywall was never the problem.
If you are at or above the median for your trial length, category and market mix, trial-to-paid is not your bottleneck and you should stop working on it. Look at install-to-trial, at retention past the first renewal, or at whether the cost side is where the money is leaking. Optimising a healthy metric because it is the one you can see is one of the more expensive habits in app growth.
Frequently asked questions
What is a good trial-to-paid conversion rate for a subscription app?
There is no single good number, because the published medians span from 15.2% to 43.5% depending on the slice. RevenueCat's 2026 dataset puts the median at 42.5% for trials of 17 to 32 days and 25.5% for trials of four days or fewer, at 43.5% for Travel and 22.2% for Photo and Video, and at 34.2% in North America against 15.2% in India and South East Asia. The useful comparison is the slice that matches your trial length, category and market mix, not a global average. If you are at or above the median for your slice, trial-to-paid is not your bottleneck.
How is trial-to-paid conversion rate calculated?
Trial-to-paid is the share of free trial starts that go on to become a paid subscription. The denominator is trial starts, not installs and not downloads, which is what separates it from install-to-trial and download-to-paid. RevenueCat defines it as the share of free trial starts that convert into a paid subscription. The measurement window matters as much as the formula: a trial that began yesterday cannot have converted yet, so a rate calculated over a period shorter than your trial length plus a few days will always look worse than it is.
Why do RevenueCat and Adapty report different trial-to-paid rates?
Three reasons, and none of them is that one report is wrong. They measure different panels of apps, RevenueCat reporting on 115,000 apps and Adapty on 16,000. They use different statistics, RevenueCat publishing medians with top quartile thresholds and Adapty publishing a global average, and a median and an average of a skewed distribution are not comparable. And Adapty does not publish its denominator or measurement window on the report page, so there is no way to confirm the two are counting the same event. Use them as separate readings, never as corroboration of each other.
Is the 10.7% hard paywall figure a trial-to-paid rate?
No, and this is the most copied error in subscription benchmarking. The 10.7% figure for hard paywalls against 2.1% for freemium is a download-to-paid rate measured within 35 days of install, so its denominator is installs. RevenueCat's own summary post has described it as trial-to-paid, and dozens of articles have repeated the wrong label. Quoted as a trial-to-paid rate it understates hard paywall performance by a factor of three or more, because trial starts are a far smaller denominator than installs.
How much does trial-to-paid affect the CPI I can afford?
Proportionally, which is why it is the highest-leverage number in the funnel for most apps. Allowable CPI equals your target subscriber acquisition cost multiplied by your install-to-paid rate, and install-to-paid is install-to-trial multiplied by trial-to-paid. On a hypothetical app with an 11% install-to-trial rate and a £60 target cost per subscriber, moving trial-to-paid from 25.5% to 37.4% raises allowable CPI from £1.68 to £2.47. That is a 47% larger bid on the same economics, which in a competitive auction is often the difference between buying volume and being priced out.
Should I compare my paid traffic against these benchmarks?
With caution. Neither report publishes a split of trial-to-paid by acquisition source, so the panels blend paid, organic and referral traffic together. Paid traffic usually converts differently from organic because it arrives with a different level of intent, so an app whose installs are mostly paid should expect to sit below a blended benchmark without anything being broken. Compare your paid cohorts against your own paid cohorts over time first, and use the published medians for a sanity check rather than a target.
My trial-to-paid is below the benchmark. What should I fix first?
Check the measurement before the product. Confirm the window is long enough for your trial length to resolve, confirm the denominator is trial starts rather than installs, and segment by market, because a geographic mix shift alone moves the blended rate by ten points. If the measurement is sound, the order of investigation is the promise made in the ad, then the paywall and the trial length, then the product itself. A large share of weak trial-to-paid rates are a message match problem rather than a product problem, and that is diagnosable by comparing conversion across creative concepts rather than in aggregate.
The short version
Trial-to-paid is the share of trial starts that become paying subscribers, and the published 2026 medians span from 15.2% to 43.5% depending on trial length, category and market. No global median is published, the two leading reports measure different panels with different statistics, and the two figures most likely to be quoted at you, the 25.6% average and the 10.7% hard paywall rate, are respectively not comparable with RevenueCat's medians and not a trial-to-paid rate at all. Find your slice, measure your own rate with the denominator and window stated, split it by market before you diagnose anything, and convert the result into an allowable CPI. That last step is the one that turns a benchmark into a decision.
Most of the apps we work with arrive with a cost problem and leave having fixed a conversion one, because the gap between the promise in the ad and what the paywall asks for is where trial-to-paid quietly goes. The Steps & Beasts case study covers a fitness app where creative testing alongside onboarding changes moved revenue 145% and active subscriptions 118%, and the consumer apps page sets out how we approach subscription acquisition on Meta. If your app is already spending and you want to know whether this is worth a conversation, our pricing is published.