Contents
- What are MAU, WAU, and DAU?
- Why track activity metrics?
- In what situations should each metric be tracked?
- User segmentation and its significance
- Using analytical tools
- The "stickiness" coefficient and other activity indicators
What are MAU, WAU, and DAU?
The metrics MAU (Monthly Active Users), WAU (Weekly Active Users), and DAU (Daily Active Users) are key indicators that allow mobile app developers to analyze user activity. These metrics help understand how often users interact with the app and provide the opportunity to respond promptly to changes in their behavior.
Growth in MAU, WAU, and DAU indicates the app's popularity and its ability to meet user needs. As the number of active users increases, it is likely that the revenue from additional paid features will also rise. However, if the number of active users begins to decline, it can serve as a warning sign that the app is losing its appeal, and it is necessary to analyze the reasons for the decreased interest.
Why track activity metrics?
Tracking activity metrics is an integral part of successful mobile app management. Here are a few reasons why it is important:
- Assessing app popularity: Active users who regularly log into the app indicate its usefulness and convenience.
- Determining advertising effectiveness: Changes in MAU, WAU, and DAU after an advertising campaign will help understand how successful it was.
- Identifying seasonality: Analyzing metrics allows tracking changes in app usage based on the time of year or other factors.
For example, if the metrics begin to decline after an app update, this may indicate a problem that needs to be quickly addressed. One way to stimulate user activity can be through gamification, as OZON does, offering coins for various actions, including daily visits to the app.
In what situations should each metric be tracked?
Each metric has its own characteristics and is suitable for certain situations:
DAU (Daily Active Users)
This metric reflects the number of unique users who logged into the app at least once in a day. It is particularly important for apps that users engage with daily, such as games or calendars.
WAU (Weekly Active Users)
WAU shows the number of unique users over a week. This metric is useful for analyzing activity on platforms that are used several times a week, such as forums or messaging apps.
MAU (Monthly Active Users)
MAU records the number of unique users over a month and is especially relevant for apps that are used less frequently but still regularly, such as accounting software.
Tracking all three metrics provides a complete picture of user behavior: DAU demonstrates immediate responses to changes, while WAU and MAU provide insights into the long-term stability of interest in the app.
User segmentation and its significance
Segmenting active users allows for a deeper understanding of their behavior dynamics and identifying the reasons for changes in metrics. For example, if a fitness app requires users to fill out a questionnaire, segmentation can help determine how many of them completed this action. This, in turn, can provide clues on how to improve communication with users to increase their engagement.
Some examples of user segmentation include:
- By payments (paid/not paid, one-time payers/repeat payers)
- By visit times after app installation
- By visit frequency
- By geographical location
- By devices
- By completion of specific actions
Segmentation helps identify causal relationships and understand what changes in the product can lead to increased user activity.
Using analytical tools
To effectively track user behavior, it is necessary to use mobile analytics services such as AppMetrica, Appsflyer, or Amplitude. The choice of the appropriate tool depends on the specifics of the business and its needs.
These services allow for the collection and analysis of user data, which facilitates more informed decisions in marketing and product development.
The "stickiness" coefficient and other activity indicators
Based on the metrics DAU, WAU, and MAU, the "stickiness" coefficient can be calculated, which reflects the degree of user engagement. This indicator helps understand how often customers return to the app and assesses its attractiveness.
A high "stickiness" coefficient indicates that users actively use the app, increasing the likelihood of recommendations to others. Conversely, a decline in this indicator may suggest that the app no longer meets user needs.
It is also useful to analyze PCCU (Peak Concurrent Users) and ACU (Average Concurrent Users) to determine the most active hours of app usage and optimize the timing of advertising campaigns.