WAU in Mobile Analytics: What It Is, Activity Metric, and How to Calculate

What Is WAU in Mobile Analytics?

WAU (Weekly Active Users) is a metric that shows the number of unique users who interacted with the app over the last 7 calendar days. Unlike DAU (Daily Active Users), WAU is less subject to daily fluctuations and provides a more stable picture of weekly activity.

Why the WAU Metric Is Needed

The metric helps evaluate the effectiveness of weekly product and marketing activities. If DAU answers the question “how many people came today,” then WAU answers “how many people stayed with us this week.” This is critically important for apps with a usage cycle of several days: fitness trackers, educational platforms, news aggregators.

When to Use WAU Instead of DAU

In apps with irregular but weekly usage patterns, DAU can fluctuate significantly. For example, in task planning apps, users log in once every 2–3 days — DAU would show low activity, while WAU would adequately reflect real engagement. The choice between DAU and WAU depends on the expected frequency of interaction with the product.

WAU’s Relation to Product Metrics

WAU correlates with the Day 7 retention rate: if a user returns on day 7, they fall into WAU at least twice a month. Teams use WAU to track the effect of new features: launching a feature on Monday and measuring WAU the following Sunday provides a direct assessment of the change’s impact on the weekly audience.

SELECT
    DATE_TRUNC('week', event_date) AS week_start,
    COUNT(DISTINCT user_id) AS wau
FROM user_events
WHERE event_date >= CURRENT_DATE - INTERVAL '7 days'
    AND event_type IN ('session', 'purchase', 'content_view')
GROUP BY week_start
ORDER BY week_start DESC;

How Is WAU Calculated?

Calculation of WAU follows a simple formula: the number of unique users over the last 7 days. However, in practice, two approaches are used — fixed week (Monday–Sunday) and rolling window (rolling 7 days). The choice of method affects data interpretation and report comparability.

Fixed Week vs Rolling Window

A fixed week is convenient for regular reporting: WAU is calculated from Monday to Sunday. Rolling window calculates WAU for each day as the sum of unique users over the previous 7 days. The second approach produces a smoother graph and is used in real-time dashboards. According to Amplitude (2025), 73% of product teams use a rolling window for WAU.

Filtering by Targeted Events

Not every app opening should be counted in WAU. Teams often filter users by targeted events: content viewing, purchase, level completion, message sending. This excludes “passive” users who opened the app accidentally. In SQL, such a filter is added via the event_type IN (...) condition.

Accounting for Cross-Platform Users

If a user is active on both iOS and Android, identical identifiers (email, account_id) must be deduplicated. Without deduplication, WAU will be inflated by 15–25%. Use an internal user_id instead of device_id for correct counting. Clickhouse and Snowflake support deduplication via HyperLogLog or Bloom filters.

import pandas as pd

# Loading events for the last 7 days
events = pd.read_sql('''
    SELECT user_id, event_date
    FROM user_events
    WHERE event_date >= CURRENT_DATE - 7
''', conn)

# WAU calculation with rolling window
events['week_start'] = events['event_date'] \
    - pd.to_timedelta(events['event_date'].dt.dayofweek, unit='D')
wau = events.groupby('week_start')['user_id'] \
    .nunique().reset_index()
wau.columns = ['week_start', 'wau']
print(wau)

Common Mistakes in WAU Calculation

The first mistake is counting all events without filtering targeted actions. If any opening is counted, WAU will include users who opened the app for 5 seconds and closed it. The second mistake is mixing time zones: events over 7 days should be counted in a single time zone (usually UTC). The third is the lack of cross-platform user deduplication, which distorts the true audience picture.

WAU vs DAU vs MAU: What’s the Difference?

Three metrics — DAU, WAU, and MAU — form a hierarchy of active audience by time horizons. DAU measures daily activity, WAU measures weekly activity, and MAU measures monthly activity. Each metric answers its own question about user behavior and is used in different product analytics scenarios.

Comparison of Time Horizons

DAU (Daily Active Users) is the most operational metric. It shows how many users came today. WAU smooths out daily peaks and valleys, while MAU smooths out monthly ones. For apps with daily usage (messengers, social media), DAU is the primary metric. For apps used 1–2 times a week (fitness, delivery), WAU more accurately reflects activity. For seasonal or rarely used apps (travel, taxes), MAU is used.

Stickiness Ratio

Stickiness is the ratio of DAU to MAU, showing how often users return. A value above 20% is considered good. WAU is used for intermediate monitoring: if DAU/MAU drops, you can check WAU/MAU — if it is stable, the problem is with daily rather than overall activity. Formula: Stickiness = DAU / MAU * 100%. For a healthy app, this indicator stays in the range of 20–50%.

When Each Metric Matters Most

Metric selection depends on the product’s business cycle. At the growth stage, the focus is on DAU — it’s important to see the daily dynamics of new users. At the maturity stage, WAU becomes the key metric: it shows how many users remain loyal on a weekly horizon. MAU is critical for investor reports and assessing overall reach. By combining the three metrics, an analyst gets a complete picture of the user lifecycle.

def calculate_stickiness(dau, mau):
    return round((dau / mau) * 100, 2)

# Example of DAU/MAU/WAU calculation for a hypothetical app
daily = 15000
weekly = 45000
monthly = 80000

stickiness = calculate_stickiness(daily, monthly)
wau_mau_ratio = round((weekly / monthly) * 100, 2)

print(f"Stickiness: {stickiness}%")
print(f"WAU/MAU: {wau_mau_ratio}%")

WAU Norms by App Category

Norms for WAU vary greatly between app categories. There is no universal “good” value — the metric should be compared with benchmarks within the same vertical. Social media apps show the highest WAU relative to MAU, while e-commerce and travel apps show lower values due to irregular usage patterns.

WAU by App Category

According to the Adjust Mobile Benchmarks 2025 report, the median WAU/MAU ratio for different categories is: Social Media — 0.65, Gaming — 0.55, E-commerce — 0.35, Fintech — 0.45, Health & Fitness — 0.40. This means that in social networks, 65% of monthly users are active weekly, while in e-commerce — only 35%. Values below the median indicate an engagement problem or an incorrectly chosen measurement interval.

Factors Affecting WAU

Seasonality is the first factor. Food delivery apps show a WAU peak on Friday and Saturday, fitness apps — in January and before summer. Push notifications are the second factor: a well-designed campaign can boost WAU by 15–30% in a week. The third factor is app updates: after a major release, WAU usually grows by 10–20% within two weeks and then stabilizes at a new level.

How to Increase WAU in a Mobile App?

Growing WAU is a task that is solved through a combination of product and marketing tools. Unlike DAU, which can be boosted with a one-time promotion, WAU requires systematic work on user returnability on a weekly horizon. Let’s look at the main strategies confirmed by the practice of major mobile products.

Personalized Push Notifications

Push campaigns are the fastest way to impact WAU. According to Airship (2025), personalized push notifications increase WAU by 20–25% in the first week. It’s important to segment the audience by usage frequency: send motivating messages to “sleeping” users (not logged in for 5+ days) and informational ones to active users. Timing matters: for fitness apps, the best time is morning; for games, evening.

Weekly Challenges and Events

The mechanic of weekly activities is one of the most effective WAU drivers. Games use weekly events with special rewards, fitness apps use “7 days without skipping” challenges, and e-commerce uses weekly discounts for subscribers. The key principle: the activity must be time-limited (7 days) to create FOMO and motivate the user to return.

Email and SMS Reminders

Not all users allow push notifications. For them, email newsletters and SMS with weekly digests are effective: “Your weekly statistics,” “New blog articles,” “Products you viewed.” Open rates for emails with personal statistics are 40% higher than standard mailings. Combining push + email gives the maximum WAU increase — up to 35% per month.

-- Users inactive for more than 5 days (reactivation target)
SELECT user_id, MAX(event_date) AS last_active
FROM user_events
GROUP BY user_id
HAVING MAX(event_date) < CURRENT_DATE - INTERVAL '5 days'
    AND MAX(event_date) > CURRENT_DATE - INTERVAL '14 days';