The benchmark picture in one paragraph: strong performers (75th percentile) retain roughly 30 to 40% of new users on Day 1, 10 to 15% on Day 7, and 5 to 8% on Day 30, while the median app across categories lands near 4% by Day 30. In other words, even good apps lose the vast majority of installs within a month, and the difference between good and median is made almost entirely in the first days.
The numbers, and where they come from
No single company measures all of mobile, so benchmarks are stitched from attribution and analytics vendors, chiefly AppsFlyer's State of App Marketing and Adjust's mobile trends reports, whose figures typically land within a few percentage points of each other. The ranges above follow UXCam's 2026 compilation of those sources. Treat every number as a directional band, not a target with decimals: measurement windows, install sources, and category mixes differ between reports.
- Day 1: about 25 to 30% average, 30 to 40% for strong performers.
- Day 7: about 11 to 15%. The steepest part of the curve is already behind you.
- Day 30: about 4 to 6% median, 5 to 8% for strong performers.
- Category spread is wide. Social and fintech apps retain materially better than casual games or utility apps; comparing yourself to the all-apps average can flatter or slander you by 2x.
Business model shifts the curve too. Subscription apps retain multiples better than ad-supported ones by Day 30 in the compiled data, partly because payment is itself a commitment device, and partly because subscription apps are built around a job worth returning to.
Why benchmark-chasing misleads
Benchmarks answer "are we roughly normal?" and nothing else. Your retention curve is shaped by acquisition mix (paid installs retain worse than organic), by geography, and by how your category uses apps at all: a flight tracker with 5% Day 30 retention may be thriving while a habit tracker with the same number is dying. The useful comparison is your own curve, cohort over cohort, after each change you ship.
The shape of the curve tells you where to work
- Cliff between install and Day 1: a first-session problem. Users did not reach value once. This is an onboarding problem, and it is where most of the retention gap between median and strong apps lives.
- Decay from Day 1 to Day 7: a habit problem. Users saw value once but had no reason to return. Look at reminders, streaks, fresh content, and whether your permission prompts earned the right to re-engage.
- Slow bleed after Day 30: a value-depth problem. The product delivered its first win but not an ongoing one. No flow fixes this; the product itself has to deepen.
The first-session cliff deserves most teams' attention because it is the largest number and the cheapest fix. A user who never activates is pure loss, and activation is determined by a surface you can iterate in days, not quarters. Our onboarding best practices guide covers the specific levers.
Measure cohorts, then iterate weekly
Retention only becomes actionable when you can see a cohort's curve change in response to a specific change you shipped. That loop needs two things: per-screen funnel analytics on the first session, and the ability to ship the next onboarding iteration without waiting for a release cycle. Setgreet provides both: flow-level analytics on every plan, including the free one, and native flow updates that go live in minutes.
A retention diagnosis you can run this week
- Pull the last 8 weekly cohorts and plot Day 1, Day 7, and Day 30 for each. Trend beats snapshot: a flat 20% Day 1 tells you less than a 24% that slid to 17%.
- Split the newest cohorts by acquisition source. Paid installs retaining half as well as organic is normal; paid retaining a tenth as well means the ads are selling an app you did not build.
- Find the activation event that best predicts Day 30 retention (first workout logged, first document created). That event, not installs, is what your onboarding should be optimized to produce.
- Read the first-session funnel screen by screen and mark the biggest cliff. That screen is this week's work.
- Ship one change against that cliff, then watch the next two cohorts. One change per cohort window, or you will not know what worked.
Teams that run this loop monthly tend to stop asking about benchmarks within a quarter, because their own cohort-over-cohort slope becomes the number that matters. The benchmark answered "are we roughly normal?"; the loop answers "are we getting better?", and only the second one compounds.
Frequently asked questions
What is a good Day 30 retention rate for a mobile app?
Across categories, the compiled 2026 benchmarks put the median near 4% and strong performers at 5 to 8%. Subscription apps and social apps should expect meaningfully higher; casual games and one-job utilities lower. Compare against your category and acquisition mix, not the global average.
Why is Day 1 retention the number to fix first?
Because every later cohort is downstream of it. Improving Day 1 lifts the entire curve, it is measurable within a day of shipping a change, and the surface that controls it (the first session) is the easiest one to iterate.
Do these benchmarks apply to subscription apps?
Directionally, but subscription apps track two curves: usage retention and subscriber renewal. RevenueCat's State of Subscription Apps is the better reference for the renewal side; the usage benchmarks here still govern whether anyone survives long enough to subscribe.