Day-N retention (D1/D7/D30)
Day-N retention measures the percentage of a user cohort that returns on a specific day after first use — D1, D7, and D30 being the common checkpoints. It is a core mobile and product retention curve. The subtlety is that 'returned on day N' has three competing definitions — classic (exactly day N), range (by day N), and rolling — which produce different numbers from the same data, so the definition must always be stated.
What this means
Day-N retention = (users from a cohort who were active on day N) ÷ (cohort size) × 100, where day 0 is first use. D1, D7, and D30 are conventional checkpoints that together sketch a retention curve. Mobile and product analytics rely on it to judge whether new users form a habit: a steep early drop with a flattening tail is the typical shape.
Three definitions that disagree
The same cohort yields different retention numbers depending on the definition. 'Classic' (or day-N) retention counts users active exactly on day N. 'Range' (or N-day) retention counts users active at any point up to day N. 'Rolling' (or unbounded) retention counts users active on day N or later. Classic is the strictest and lowest; range and rolling are more forgiving and higher. Tools default to different ones, so a D7 figure from one product is not comparable to another's unless the definition matches. Always state which definition and what 'active' means.
This page is educational and not financial advice.
- Cohort active on day N ÷ cohort size × 100
- Classic (exact day), range (by day N), rolling (day N+) differ
- State the definition and the 'active' event before comparing
How it appears in analytics and logs
Higher day-N retention means more of the cohort came back at that checkpoint — a habit-formation and value signal. Because the three definitions disagree, a 'D7 retention' number is only interpretable once you know which one produced it.
Diagnostic use case
Track how well a product holds a cohort over time using fixed checkpoints (D1/D7/D30), to compare onboarding and habit formation across cohorts.
What WebmasterID can help detect
WebmasterID records first-party cohort and return events, so retention curves can be built from defined activity events without cross-site or cross-app tracking.
Common mistakes
- Comparing D7 figures computed with different retention definitions.
- Not stating what 'active' means for the cohort.
- Reading a single checkpoint instead of the whole curve.
Privacy and accuracy notes
Day-N retention aggregates cohort return counts and needs no third-party identifiers. This page is educational and not financial advice.
Related pages
- App retention rate
App retention rate measures how much of an install or first-use cohort comes back after a number of days. Definitions vary: day-N retention counts users active exactly on day N, while rolling or range retention counts users active on or after day N. Because these methods produce different curves from the same data, the retention definition must be stated for the number to mean anything.
- ARPDAU (average revenue per daily active user)
ARPDAU (average revenue per daily active user) is total revenue on a day divided by that day's daily active users. It is a high-frequency monetization signal common in mobile apps and games, where revenue from ads and in-app purchases is averaged across the active base each day. Because it is daily, it reacts fast to changes — but it depends entirely on how a 'daily active user' is defined, which is a per-product convention.
- Cohort analysis
A cohort is a group of users who share a starting event — the week they first visited, the month they signed up. Cohort analysis follows each cohort over time so you can compare like with like. It separates 'are users behaving differently' from 'is the mix of users changing', which a single blended average can hide.
- Event Explorer
Build retention cohorts from first-party events.
Sources and verification notes
- Google — [GA4] Cohort explorationDocuments cohort retention analysis; the classic/range/rolling definitions are an analytics convention.
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.