What is retention rate?
Retention rate is the share of users from a starting group who are still active at the end of a defined period. It is measured per cohort, usually the people who signed up in the same week or month, and reported at intervals such as day 1, day 7 and day 30. It is the inverse of churn rate.
Formula
Retention rate = (users active at end of period / users in the starting cohort) x 100
- starting cohort
- Everyone who joined in the same window, for example all signups in the week of 3 August.
- active
- Your product's definition of a meaningful action, not merely logging in. State it explicitly.
Worked example
The August signup cohort of a SaaS product.
| Signups in the cohort | 1,240 |
|---|---|
| Still active on day 7 | 471 |
| Still active on day 30 | 298 |
| Day 30 calculation | 298 / 1,240 |
Day 7 retention = 38%, day 30 retention = 24% The steep part is between signup and day 7, which points at onboarding rather than at long-term value. Fixing the first week moves every later number with it.
N-day, unbounded, and rolling retention
Three definitions circulate under one name, and they give materially different numbers on the same data.
- N-day retention: was the user active on exactly day N? Strict, spiky, and the right choice for products people are meant to use daily.
- Unbounded retention: was the user active on day N or any day after? More forgiving and better for weekly or monthly products.
- Rolling or bracket retention: was the user active in a window around day N, for example days 5 to 9? Smooths out weekday effects.
Pick the one that matches how often your product is genuinely meant to be used. A B2B reporting tool used every Monday will look catastrophic under N-day retention and healthy under a weekly bracket, and the second reading is the truthful one.
Why retention is read as a cohort grid
A single retention percentage is an average across cohorts that were acquired differently, onboarded differently and shipped different versions of the product. The grid separates them.
Read it two ways. Along a row: how one cohort decays over time, which tells you whether the product holds people. Down a column: how the same week-since-signup number moves across successive cohorts, which tells you whether recent changes to onboarding or acquisition are working.
The shape that matters most is whether the curve flattens. A curve that decays and then goes horizontal means you have found a group for whom the product is genuinely durable. A curve that keeps sliding toward zero means nobody sticks, and no amount of acquisition spend will fix it.
Live demo
See it on real data. The live demo is a fully populated dashboard on demo traffic, with no signup and no card.
Open the live demoGetting retention right
Define active honestly
Counting logins overstates retention badly. Count the action that delivers the value: a report run, a message sent, a deploy shipped.
Never mix acquisition into it
If new signups enter the denominator, growth masks decay. Retention is measured inside a closed cohort for exactly this reason.
Match the interval to the product
Daily for consumer habit products, weekly for team tools, monthly for anything invoiced monthly. Using the wrong interval makes a healthy product look broken.
Look for the flattening point
The week the curve stops falling is your real retained base. Everything above it is people who were never going to stay.
Retention in mrkr
mrkr does not calculate signup-week retention curves or save cohort definitions. Its returning-visitor and active-day reports describe retained observed activity; they are not this retention-rate formula.
Cross-session retention needs identity that persists beyond a day, which cookieless mode deliberately does not provide. mrkr’s optional per-site cookie mode can join observed browser activity across days and requires appropriate consent. It does not turn the active-day report into a signup-week retention curve.
That trade-off is worth stating plainly rather than hiding: a cookieless identifier that expired daily could not honestly report 30-day retention, and a tool that claimed otherwise would be measuring something else.
FAQ
What is a good retention rate?
It depends on the usage cadence and the market. For consumer apps, day 30 retention above 25% is generally considered strong. For B2B SaaS, monthly logo retention above 90% is the usual bar. Compare against your own earlier cohorts before you compare against anyone else's benchmark.
What is the difference between retention rate and churn rate?
They are complements: retention rate plus churn rate equals 100% over the same period and the same cohort. Retention is the natural framing for product and cohort analysis, churn for revenue and subscription reporting.
Can I measure retention with cookieless analytics?
Not across days from anonymous traffic, because a cookieless identifier is deliberately rotated daily and cannot link a visitor to yesterday. You measure retention either by enabling per-site cookie mode, which requires consent, or by attaching your own user id to events for people who have signed in.
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