# DAU/MAU ratio: how to calculate stickiness and what a good number is

> The DAU/MAU ratio explained: the formula, what a good stickiness number is, a worked example on real-looking data and why a weekly product needs WAU/MAU.

- Source: https://productovi.com/blog/dau-mau-ratio
- By: Sergey Bruh
- Published: 2026-09-30
- Topics: Active users and stickiness

The DAU/MAU ratio is **average daily active users divided by monthly active users**: the share of the month's users who show up on a typical day. If 1,000 people used your app this month and on an average day 200 of them opened it, DAU/MAU is 20%. It's the most common measure of stickiness, and it's only meaningful when your product is meant to be used daily. For a product people need once or twice a week, a low DAU/MAU is normal, and the honest number is WAU/MAU. Below: the formula, what counts as a good ratio, a worked example on a month of data from Halves (a fictional expense-splitting app) and the traps that make the ratio lie.

## DAU MAU formula: daily active users over monthly active users

[DAU, WAU and MAU](/glossary/pa-dau-wau-mau) are the numbers of **distinct users** who did a qualifying action (for example, opened the app) in a day, a week and a month. [Stickiness](/glossary/pa-stickiness) divides a short window by a long one:

$$
\text{DAU/MAU} = \frac{\text{average DAU over the month}}{\text{MAU}}
$$

Two details decide whether the result is right:

- **Average DAU, not one day's DAU.** Daily numbers jump around: a Monday rent day, a quiet Sunday. Take the mean over every day of the month. A handy shortcut: the sum of daily DAU equals the total number of active **user-days**, so average DAU = user-days ÷ days in the month.
- **The same definition of "active" on top and bottom.** If DAU counts app opens and MAU counts any event, the ratio compares two different questions.

The ratio also has a readable meaning. Multiply DAU/MAU by the number of days in the month and you get **how many days a typical monthly user was active**. A 20% ratio in a 30-day month means about 6 active days per user.

## What is a good DAU/MAU ratio?

The ratio can't go below one day in the month (about 3% for a 30-day month) or above 100%. The figures that circulate in the industry go roughly like this:

| DAU/MAU   | What it usually means                                            |
| --------- | ---------------------------------------------------------------- |
| 50% +     | A daily habit: messaging, social feeds, some games               |
| 20–50%    | Frequent use: many consumer apps with a strong routine           |
| 10–20%    | Use a few times a week or less; typical for many useful products |
| Under 10% | Occasional use, or a daily product that isn't forming a habit    |

Treat these as folklore, not standards. They mix products with different rhythms and, more often than not, different definitions of "active". The only fair benchmark is a product with the same natural usage rhythm, measured the same way, or your own ratio over time.

## A worked example: is 17% bad?

An investor tells the Halves team that good consumer apps have DAU/MAU around 50%. Halves's dashboard shows **17%** for June. Here is the recount on 1–28 June (four full weeks, treated as the month).

### Step 1: MAU

The export has 140 users with any event in June. But 12 of them have no app opens at all: their only events were recurring bills that the server created automatically. They never touched the app, so:

MAU = 140 − 12 = **128**

### Step 2: average DAU

The daily counts add up to 611 (40 on rent-day Monday 1 June, 13 on the last Sunday, between 16 and 27 on the other days). The same 611 comes out when you add up every user's active days, which is a free check.

Average DAU = 611 ÷ 28 = 21.82

### Step 3: the ratio

$$
\text{DAU/MAU} = \frac{21.82}{128} = 0.170 = 17.0\%
$$

The dashboard was right. In days: 0.1705 × 28 = 4.77, so a typical June user opened Halves on about 5 days out of 28.

### Step 4: measure at the natural rhythm

Nobody splits a dinner bill every day. Flatmates and couples share costs a few times a week or month, so the natural rhythm is **weekly**. WAU for each week of June (users with at least one active day that week) was 84, 83, 73 and 78:

- Average WAU = (84 + 83 + 73 + 78) ÷ 4 = 318 ÷ 4 = 79.5
- WAU/MAU = 79.5 ÷ 128 = 0.621 = **62.1%**

And 71 of the 128 users (55.5%) were active in at least three of the four weeks. That's a habit, just not a daily one. The honest reply to the investor: DAU/MAU is 17.0%, it's the wrong ruler for bill splitting, and at our rhythm WAU/MAU is 62.1%, with the definition written next to it.

![The same 128 Halves users give DAU/MAU of 17.0% on a daily ruler and WAU/MAU of 62.1% on the weekly ruler that fits bill splitting](/blog/dau-mau-ratio/diagram.png)

### Step 5: split by segment

| Main group type | MAU | Average WAU | WAU/MAU | DAU/MAU |
| --------------- | --- | ----------- | ------- | ------- |
| Couple          | 36  | 31.5        | 87.5%   | 24.7%   |
| Flat            | 48  | 35.5        | 74.0%   | 16.5%   |
| Trip            | 30  | 9.0         | 30.0%   | 15.0%   |
| Other           | 14  | 3.5         | 25.0%   | 3.6%    |
| All             | 128 | 79.5        | 62.1%   | 17.0%   |

Trips at 30% look alarming until you remember what a trip is: one intense week, then maybe a settle-up day. A trip user active one week in four is doing exactly what the product is for. Trips are judged per trip (did the group log expenses to the end and settle up?), not per week.

## Match the ratio to the product's rhythm

| Product                  | Ratio that fits                                      |
| ------------------------ | ---------------------------------------------------- |
| Team chat, messaging     | DAU/MAU                                              |
| Household bill splitting | WAU/MAU                                              |
| Trip expense splitting   | Share of trips logged to the end and settled         |
| Yearly tax filing        | Share of last year's filers who file again this year |

Ask: how often does a satisfied user **need** the product? Measure at that interval. A daily ratio on a weekly product calls a healthy product a failure; a monthly ratio on a daily product hides a fading habit.

## Traps that make the ratio lie

### Mismatched definitions of "active"

Suppose a dashboard counts MAU from any event (140 users, including the 12 server-only ones) but WAU from app opens. Its WAU/MAU would be 79.5 ÷ 140 = 56.8% instead of 62.1%. Counting automatic events as activity inflates MAU and deflates every ratio built on it. Write the [metric definition](/courses/product-analytics/metric-definitions/theory) once and use it for both parts.

### Mix shift

A ratio over a mixed crowd moves when the crowd changes. Say July brings 60 extra trip users who behave exactly like June's trip users (WAU/MAU 30%), and everyone else stays the same:

- WAU = 79.5 + 0.30 × 60 = 97.5
- MAU = 128 + 60 = 188
- WAU/MAU = 97.5 ÷ 188 = **51.9%**

The ratio falls by ten points while no segment changed at all. That's [mix shift](/glossary/pa-mix-shift): report the ratio by segment, or at a fixed mix, before calling it a product problem. The lesson [Volume, quality and ratios that lie](/courses/product-analytics/volume-vs-quality-metrics/theory) walks through a full mix-adjusted example.

### A shrinking denominator

The ratio also rises when casual users leave. If the 14 "other" users disappear next month and nothing else changes, WAU/MAU becomes (79.5 − 3.5) ÷ (128 − 14) = 76 ÷ 114 = 66.7%. Better ratio, smaller product. Always show MAU next to the ratio.

### Months of different length

A 31-day month has more room for active days than a 28-day one. Compare like with like: fixed 28-day or 30-day windows, or the same calendar month year over year.

## DAU/MAU, retention and the north star

Stickiness tells you how often this month's users come back **within** the month. It doesn't say whether new users stay for months; that's what [retention by cohort](/blog/how-to-calculate-retention-rate) is for. And neither MAU nor stickiness is a good company goal on its own: MAU is a volume metric: it would rise with more ad spend or a busy season even if the product didn't change at all. See [north star metric examples](/blog/north-star-metric-examples) for metrics that count value rather than visits, and [what product analytics is](/blog/what-is-product-analytics) for how these pieces fit.

## Key takeaways

- DAU/MAU = average DAU over the month ÷ MAU; average DAU = total active user-days ÷ days.
- Multiply the ratio by the days in the month to get active days per typical user.
- Benchmarks like "50% is good" come from daily products; compare only with products of the same rhythm, or with yourself.
- Measure at the natural rhythm: at Halves, DAU/MAU was 17.0% and WAU/MAU 62.1% for the same users.
- Use one definition of "active" for both parts, split by segment and show MAU next to the ratio.

## FAQ

### Is DAU/MAU the same as retention?

No. DAU/MAU measures how often the month's users return within that month. Retention follows a cohort of new users over weeks or months and asks how many are still active. A product can have high stickiness among its regulars and poor retention of newcomers.

### Should I use a calendar month or a rolling 30 days?

Either, as long as you keep it fixed. Rolling 30-day MAU gives a smooth daily series; calendar months are easier to explain. Don't mix the two in one chart.

### What's a good WAU/MAU ratio?

Because four weeks fit in a month, WAU/MAU runs much higher than DAU/MAU: at Halves it's 62.1%. As with DAU/MAU, compare against your own history and against products with the same weekly rhythm.

### Why is my DAU/MAU going down while DAU is growing?

Usually because MAU is growing faster: a partner promo that pays a sign-up bonus, or a seasonal peak, brings many light users who came for the reward or a one-off need. Split by acquisition channel or sign-up cohort: if each segment's ratio is stable, it's mix shift, not a product problem.

## Learn it hands-on

Compute DAU, WAU, MAU and stickiness yourself on the June file in the free lesson [DAU, WAU, MAU and stickiness](/courses/product-analytics/active-users/theory), part of the free course [Product Analytics: From Data to Decisions](/courses/product-analytics).

## Related lessons

- [DAU, WAU, MAU and stickiness](https://productovi.com/courses/product-analytics/active-users.md)
- [Volume, quality and Simpson's paradox](https://productovi.com/courses/product-analytics/volume-vs-quality-metrics.md)
- [Metric definition: one rule, one number](https://productovi.com/courses/product-analytics/metric-definitions.md)