← Glossaryday-N retention
Also called: day N retention, D1, D7, D30, D1 retention, D7 retention, D30 retention, retention rate, classic retention, N-day retention
The share of a cohort that is active exactly on day N after joining. Strict and precise, but noisy for products not used daily.
Common mistakes
- Counting any event as "active". Push deliveries, server-created records and background syncs make retention look better than it is.
- Daily retention for a weekly product. Exact-day D7 or D30 punishes normal usage gaps; match the period to the usage rhythm.
- Mixing up day 0 and day 1. Some tools call the sign-up day "day 1". Check the convention before comparing numbers across reports.
- Ignoring time zones. With UTC days, a user in Bangkok can be "active on day 1" a few hours after sign-up. Decide whether days are UTC or local.
- Comparing with an industry benchmark. "Good D30" depends on the category and the usage rhythm; compare your own cohorts over time and by segment.
Retention rate answers the most important question about a product: do people come back? The standard product version is day-N retention (also called classic or N-day retention): take a cohort of users who started on the same day, and count the share who are active on exactly day N after that.
DN retention=users in the cohortusers from the cohort active on day N×100%
Day 0 is the sign-up day, so D1 is the next calendar day. The usual checkpoints are D1 (did the first session land?), D7 (did a habit start?) and D30 (is the product part of life?). "Active" must be defined as a meaningful action, not any event: for Halves, opening the app or adding an expense by hand counts, while a bill the server created automatically doesn't. A small cohort table shows how it reads:
Day-N retention is strict: a user who comes back on day 6 and day 8 but not day 7 counts as lost for D7. That's fine for products used daily (messengers, games), but for a product used weekly or monthly it is noisy and understates real retention. For those, measure retention in weeks or months ("active at any time in week 2") or use a bracket such as days 7–13. Pick the period that matches the product's natural usage rhythm.
A different formula carries the same name in subscription and B2B businesses: customer retention rate over a period, CRR=SE−N, where S is customers at the start, E at the end and N new customers gained during the period. It measures what share of existing customers you kept, not how a cohort behaves over time. Say which one you mean.
800 users signed up for Halves on June 1 (day 0).
- D1: 240 of them opened the app or added an expense on June 2. 240/800=30.0%.
- D7: 152 were active on June 8. 152/800=19.0%.
- D30: 96 were active on July 1. 96/800=12.0%.
Maya worries that D7 = 19% is low. But Halves is used around shared bills, roughly weekly, so many people simply didn't have a reason to open it on that exact Monday. Counting users active on any day from 7 to 13 gives 312: 312/800=39.0%, twice the D7 figure. For Halves, week-based retention is the number to track; D1 stays useful as a quick check of the first session.
