---
title: DAU/MAU vs Retention Rate: What Each Tells You
description: DAU/MAU vs retention rate: stickiness measures how often this month's users return, retention how many of a cohort stay. Formulas, pitfalls and a calculator.
url: https://appmetricslibrary.com/compare/dau-mau-vs-retention/
---

# DAU/MAU vs retention rate

Two engagement measures that sound alike and answer different questions: how often active users come back, and how many new users stay at all.

**Short answer**

DAU/MAU (stickiness) is average daily users divided by monthly users: how many days a month the typical active user shows up. Retention rate is the share of an install cohort still active n days later. Stickiness can stay flat while retention falls, because it only looks at users who are already active.

## What is the difference between DAU/MAU and retention?

DAU/MAU measures frequency among users who are active this month; retention measures survival of a group of new users over time. One is a snapshot of the current base, the other follows a cohort from install.

|  | DAU/MAU stickiness | Retention rate |
|---|---|---|
| Formula | Average DAU ÷ MAU × 100 | Cohort users active on day n ÷ Cohort size × 100 |
| Population | Everyone active this month | One install cohort |
| Question | How often do active users come back? | How many new users are still here? |
| Time frame | One month | Days since install (D1, D7, D30) |
| Blind spot | Users who already left | How often the survivors use the app |

**Install cohort:** all users who installed on the same day or in the same week, followed together over time.

## Can stickiness be healthy while retention is falling?

Yes. If new users leave quickly but a loyal core keeps using the app daily, DAU/MAU stays steady because it only measures this month's active users, while D7 and D30 retention for new cohorts fall. That is why growth teams read both, and why a stable DAU/MAU is no proof that acquisition is bringing the right users.

The calculator puts both metrics side by side from example inputs, and shows how many of a cohort's users remain as a share of the month's actives.

> Interactive tool (metric calculator): runs in the browser at https://appmetricslibrary.com/compare/dau-mau-vs-retention/#metric-calc. Its text content follows.

**DAU/MAU vs retention rate calculator**

**Example inputs: change them to your own numbers.** The examples only make the calculator show a worked answer; they are not benchmarks or targets. Runs in your browser; nothing you type is sent anywhere.

**DAU/MAU stickiness**

25%

**Active days per monthly user (30-day month)**

7.5

**D30 retention of the cohort**

7%

## What each one is good for

- **Stickiness** suits habit products and live services: it shows whether the existing base has made the app part of its routine. See [DAU/MAU stickiness](https://appmetricslibrary.com/metrics/dau-mau-stickiness/) for the WAU/MAU and DAU/WAU variants that fit less frequent apps.
- **Retention** suits acquisition and onboarding decisions: it shows whether a channel, campaign or onboarding change brings users who stay. See [retention rate](https://appmetricslibrary.com/metrics/retention-rate/) for classic, rolling and range retention.
- **Churn** sits between them for subscription apps, measuring the base lost over a calendar period. See [churn rate](https://appmetricslibrary.com/metrics/churn-rate/).

## How to read them together

- **Both rising:** new users stay and active users come more often. The product is improving.
- **Stickiness flat, retention falling:** acquisition quality or onboarding is getting worse; the loyal base hides it.
- **Stickiness falling, retention flat:** the base is using the app less often; look at content, notifications and seasonality.
- **Both falling:** a product or market problem rather than a channel one.

First-session experience drives early retention more than anything else. One common fix is sending new users straight to the content their ad or link promised; Deep Link Doctor explains [how deferred deep linking carries that context through the install](https://deeplinkdoctor.com/problems/what-is-deferred-deep-linking/).

## Common mistakes

- **Comparing apps with different natural frequency.** A weekly-use app will always show lower DAU/MAU than a messaging app. Use WAU/MAU or retention ranges instead.
- **[Rolling retention](https://linkrunner.io/glossary/what-is-rolling-retention?utm_source=appmetricslibrary&utm_medium=referral&utm_campaign=compare-dau-mau-vs-retention&utm_content=inline) next to classic.** Rolling retention is always higher and keeps rising; keep one definition per report.
- **Single-day DAU.** A weekend or promotion moves one day's DAU a lot; average it over the month.

The glossary entries on [stickiness (DAU/MAU ratio)](https://linkrunner.io/glossary/what-is-stickiness-dau-mau-ratio?utm_source=appmetricslibrary&utm_medium=referral&utm_campaign=compare-dau-mau-vs-retention&utm_content=inline) and [retention rate](https://linkrunner.io/glossary/what-is-retention-rate?utm_source=appmetricslibrary&utm_medium=referral&utm_campaign=compare-dau-mau-vs-retention&utm_content=inline) give short definitions of both.

Linkrunner

## Measure these by channel

**Track this automatically**

Linkrunner shows D1, D7 and D30 retention for each install cohort by channel, next to the campaigns that brought those users.

- [How to Read a Mobile Retention Curve](https://linkrunner.io/blog/how-to-read-a-retention-curve?utm_source=appmetricslibrary&utm_medium=referral&utm_campaign=compare-dau-mau-vs-retention&utm_content=panel)

[Create a free account (25k installs free)](https://dashboard.linkrunner.io/sign-in?utm_source=appmetricslibrary&utm_medium=referral&utm_campaign=compare-dau-mau-vs-retention&utm_content=panel)

Entries

## The metrics in this comparison

[15 Stk DAU/MAU stickiness](https://appmetricslibrary.com/metrics/dau-mau-stickiness/) [22 Ret Retention rate](https://appmetricslibrary.com/metrics/retention-rate/) [23 Chn Churn rate](https://appmetricslibrary.com/metrics/churn-rate/)

FAQ

## Questions

**Is DAU/MAU a retention metric?**

Not strictly. It measures how often active users return within a month, while retention measures whether a cohort of new users is still active after n days.

**Which should an early-stage app track first?**

Retention by cohort, especially D1 and D7. Stickiness becomes useful once there is a stable base of returning users to measure.

**Why is my DAU/MAU stable when installs grow?**

New users add to both DAU and MAU, but if many are active on only one or two days, MAU grows faster than DAU and stickiness can even fall. Check retention for the new cohorts.

**Can I convert DAU/MAU into retention?**

No. They use different populations and time frames, so neither can be derived from the other.

**Do I need an MMP to measure these metrics?**

Not for all of them. Engagement and monetisation metrics come from product analytics and billing data, while metrics split by ad channel, such as CPI, ROAS or retention by source, need installs attributed to campaigns.

**Are the calculator's example inputs benchmarks?**

No. The example inputs only make the calculator show a worked answer when the page loads; they are not typical, good or target values. Replace them with your own numbers.

More

## More comparisons

- [**CPI vs CAC vs CPA** Three cost metrics that are often mixed up. Each prices a different step of the funnel, from the install to the paying customer.](https://appmetricslibrary.com/compare/cpi-vs-cac-vs-cpa/)
- [**ARPU vs ARPPU** Two revenue-per-user metrics that differ only in who you divide by, and that can move in opposite directions.](https://appmetricslibrary.com/compare/arpu-vs-arppu/)
- [**LTV vs ROAS** Lifetime value looks forward at what a user is worth; ROAS looks back at what a campaign has earned so far. Read together, they tell you when spend pays back.](https://appmetricslibrary.com/compare/ltv-vs-roas/)