App Retention Rate Calculator

Day N retention = (cohort users active on day N) ÷ (cohort size). Two definitional choices move the number substantially. First, day-N vs bounded windows: “classic” retention counts users active on exactly day N, while “rolling” retention counts anyone active on day N or later — rolling is always higher, so never compare one app’s classic number to another’s rolling number. Second, what counts as “active”: an app open is the common default, but a session shorter than a few seconds arguably shouldn’t count.

Always compute retention per cohort rather than across your whole user base. A blended “active users ÷ installs” ratio mixes week-old and year-old users and moves with your acquisition volume, telling you nothing about product stickiness. Cohort curves also reveal the shape that matters: healthy products decline steeply then flatten into a plateau of habitual users; a curve that keeps sliding toward zero means no retained core exists yet, and acquisition spend is pouring into a leaky bucket.

Cross-industry mobile averages cluster around 25% D1, 10–12% D7, and 3–5% D30 — meaning the median app loses three-quarters of its users within a day and over 95% within a month. Category spread is wide: casual games and news apps often beat these at D1, while D30 above 10% is genuinely strong in almost any consumer category. A rough quality heuristic many growth teams use is the 40–20–10 rule (40% D1, 20% D7, 10% D30) as the bar for a top-tier consumer app.

Each milestone diagnoses a different layer. Weak D1 points to onboarding and first-session value — the user never reached the “aha” moment. Weak D7 with decent D1 points to a missing habit loop or notification strategy. Weak D30 with decent D7 points to shallow long-term utility. Fixing them in that order is usually right, because improvements at D1 mechanically lift every later milestone.