What Is DAU? Differences from MAU & WAU and How to Use It in App Operations

When discussing the growth of an app or web service, DAU is an indispensable metric. Because it measures not just the scale of your user base but "how habitually the product is used," it is widely used as a benchmark for judging product health.

This article explains the meaning and calculation of DAU, how it differs from MAU and WAU, how to read stickiness (the degree of adoption) by combining the two, and concrete ways to use DAU in app operations.

What Is DAU?

DAU (Daily Active Users) is a metric that indicates the number of unique users who used a service in a single day. No matter how many times the same user accesses the service in one day, they are counted as one.

What matters here is the definition of "active." Do you treat merely opening the app or logging in as "active," or do you count only users who perform a "meaningful action" such as posting, purchasing, or using a specific feature? This criterion needs to be designed per product. If you operate with an ambiguous definition, the numbers can look larger than reality, so caution is needed.

Differences from MAU and WAU

WAU and MAU are metrics of the same "active users" family as DAU. The only difference is the aggregation "period"; the underlying concept is shared.

• DAU (daily): the number of unique active users in one day. Captures day-to-day fluctuations and the immediate effect of initiatives.
• WAU (weekly): the number of unique active users over 7 days. Smooths out day-of-week variation to see the trend.
• MAU (monthly): the number of unique active users over 30 days. Represents the overall scale of the service and is standard in investor reporting.

Note that these are not a simple additive relationship. For example, summing 30 days of DAU does not give you MAU, because MAU counts "unique users who used the service at least once during the period" without duplication. A user who uses the product every day is counted every day in DAU, but treated as one person in MAU.

Which metric should be your primary one depends on how frequently your product is used. For SNS and chat tools that are meant to be used daily, DAU is the center; for business tools used a few times a week, WAU; and for services used on a monthly basis, MAU becomes the central metric.

What the DAU/MAU Ratio (Stickiness) Tells You

Combining DAU and MAU lets you measure a product's "degree of adoption (stickiness)." The formula is simple.

Stickiness = DAU ÷ MAU × 100 (%)

This shows "of the users who use the service at least once a month, what share use it every day." For example, if MAU is 50,000 and average DAU is 15,000, stickiness is 30%. The higher the ratio, the more deeply the product is embedded in users' daily lives.

Rather than a single day's DAU, using the average DAU over the trailing 30 days smooths out day-of-week effects and temporary fluctuations to give a more stable value.

What counts as "good stickiness" varies greatly by product type. As a general guideline only, recent benchmarks report the following trends.

• Social/gaming: 20% to over 50%. Used frequently and very sticky. Facebook has historically maintained over 50%.
• General SaaS: around 13% on average.
• Fintech/finance: roughly 10–22%.
• E-commerce: around 10%.

For products like business tools where usage a few times a week is natural, a low DAU/MAU is not a problem. In that case, DAU/WAU or WAU/MAU reflects reality more accurately. The most reliable benchmark is not a comparison with other companies, but your own historical trend. Whether the ratio is improving, flat, or declining—that direction is what matters.

How to Use DAU in App Operations

Just "watching" DAU is meaningless. It delivers value only when combined with other metrics and connected to improvement actions.

1. Measure the Immediate Effect of Initiatives

The effects of push notifications, campaigns, and new feature releases are reflected in DAU relatively quickly. By tracking DAU before and after an initiative, you can verify in a short cycle what prompted users to return.

2. Monitor Adoption with Stickiness

If MAU (scale) is growing but stickiness (DAU/MAU) is falling, you are in a state where you are acquiring new users but not retaining them. In this case, you can tell that you should prioritize improving onboarding and features that encourage continued use over acquisition initiatives.

3. Anomaly Detection and Early Discovery of Trouble

Because DAU moves daily, a sudden drop serves as an alert that lets you detect bugs, outages, or competitor moves early. Taking into account day-of-week and seasonal cycles, monitor for deviations from the norm.

4. Combine with Segment and Cohort Analysis

Rather than viewing DAU only as an overall figure, breaking it down by acquisition channel, features used, and signup period (cohort) reveals which user segments are retained and where they drop off. A finding such as "users who used a specific feature in their first week retain better" connects directly to improving your onboarding design.

Things to Watch When Looking at DAU

• Fix the definition of "active": if you change the definition midway, you lose comparability with the past. Base it on a meaningful action.
• Don't judge by a single metric: look multidimensionally, combining DAU with MAU, stickiness, and retention rate.
• Choose a metric that fits the product's nature: if it isn't a service used daily, WAU/MAU is often more appropriate than DAU.

Conclusion

DAU is a metric that shows the number of unique active users in a single day, related to WAU and MAU only by a difference in aggregation period. What matters is not tracking DAU on its own, but grasping the product's degree of adoption through stickiness (DAU/MAU) by combining it with MAU.

First, decide on a definition of "active" that fits your own product, and choose the appropriate primary metric. Then, by combining it with measuring initiative impact, monitoring adoption, and cohort analysis, DAU becomes a practical compass that drives growth.