A weekly active user (WAU) is a unique user who performs at least one qualifying action in an app or website within a seven-day period.
What is a Weekly Active User (WAU)?
WAU sits between Daily Active User (DAU) and MAU, and teams generally reach for it when a product's natural usage rhythm is a few times a week rather than daily, such as a fitness app, marketplace, or learning platform. The window can be measured as a fixed calendar week or as a rolling seven days, and most teams prefer a rolling window because it avoids the artificial reset a fixed week creates. As with DAU and MAU, the definition of a qualifying action is a team decision, ranging from any app open to a specific meaningful action, and it should stay consistent across all three metrics for comparability. One common caveat is that WAU alone doesn't reveal how often within the week a user returns; teams pair it with DAU/WAU to see whether a "weekly" user shows up once or most days.
Why Weekly Active Users Matter
Not every product should be measured daily. A fitness app, a marketplace, a learning platform, or a hobby community can be healthy when users return two or three times a week, and forcing a daily metric onto that pattern makes normal behavior look like churn. WAU gives product teams a window that matches real usage cadence, smooths out weekday and weekend swings that distort DAU, and still reacts faster than MAU when something changes. It also gives growth teams a practical activation target: a new user who is still active in week two is far more likely to still be there in month three.
How to Calculate Weekly Active Users
WAU is a deduplicated count of users who met the activity definition inside a seven-day window. The main choices are the window type and the activity threshold.
| Element | Options | Notes |
|---|---|---|
| Window type | Calendar week (Monday to Sunday) or rolling 7 days | Rolling windows avoid artificial weekly resets |
| Qualifying action | App open, or a meaningful action such as a post, message, or purchase | Stricter definitions give smaller, more useful numbers |
| Deduplication | One count per user per window | 4 visits in a week = 1 WAU |
| Ratio use | WAU/MAU shows what share of monthly users return weekly | 60% and above suggests a strong weekly habit |
Worked example: over the seven days ending Sunday, an app sees 48,000 unique users perform at least one qualifying action. Its 30-day MAU is 80,000. WAU/MAU is 48,000 divided by 80,000, or 60%, so three in five monthly active users came back at least once that week. If DAU averaged 16,000 across the same week, DAU/WAU is 33%, which tells the team that a typical weekly user shows up on roughly two days out of seven.
Tracking WAU by cohort is often more useful than the raw total. If users who joined a group or followed another user in their first week keep returning at double the rate of users who did not, that points to the onboarding step worth investing in.
Weekly Active Users and social.plus
Weekly return behavior usually depends on whether an app gives users something new to check between core tasks. social.plus provides engagement infrastructure, including feeds, groups, chat, and live streaming, that apps add through SDKs, APIs, and UIKit so that content from other users creates those reasons to return. Smart Fit, a fitness brand, saw 60% month-over-month growth after adding community features to an app where members naturally engage around a weekly training rhythm rather than a daily one. Brands using social.plus features report retention lifts of 10-35%, and for weekly-cadence products that improvement is most visible in WAU.
Key Takeaways
- A weekly active user is a unique person who performs a qualifying action within a seven-day window, counted once regardless of visits.
- WAU fits products with a multi-times-per-week cadence and smooths out the weekday swings that distort daily figures.
- WAU/MAU shows how much of the monthly audience returns weekly; DAU/WAU shows how many days per week a typical weekly user is present.
- Cohort-level WAU reveals which early actions predict lasting engagement.