Users' Activity

Your application is already running, new users appear, audience accumulated. The number of the unique users that logged into your application on a particular day is called DAU (Daily Active Users) of that day.

The number of the unique users that logged into your application within a week is called WAU (Weekly Active Users). Please note, WAU - it is not the sum of DAU for 7 days, it is the unique users who visited your application within a week. The same user may visit your app every day for a week, and then he will increase DAU of each day by 1, but WAU will be also increased only by 1, as in WAU the repeated visits are not considered.

Similarly the MAU (Monthly Active Users) indicator is calculated - the number of unique users who logged into the application within a month.

Indicators DAU, WAU, MAU determine the scale of your project. And it is about them you will be asked in the first place when entering into partnership agreements. Of course, all the indicators should grow over time. In order to ensure their growth, you need to maximize the flow of new users and the percentage of retention.

It is also interesting to calculate the ratio, for example, DAU to MAU. This indicator is sometimes called Sticky Factor, it shows the regularity of the users logs. If it is assumed that there are 1000 users in the project, and each of them logs every day, then  DAU, and MAU are equal to 1000, and the indicator of Sticky Factor equals 100%. If every user logged only once within a month, Sticky Factor equals only to 3.3%. The higher the number of this indicator, the more regular users log into your application.

It often happens that the indicators DAU, WAU and MAU vary considerably due to the unstable flow of new users. In order not to take into account these variations there are LDAU (Loyal Daily Active Users), LWAU and LMAU metrics.

LDAU – the number of unique loyal users run the application on a particular day.In this case, the loyal user is the one who runs application at least once a day after the first visit. Similarly LWAU and LMAU are calculated.

It turns out that the closer to each other the indicators of DAU and LDAU are, the less there is so-called "one-day" - users in the application (users who do not return to the application the next day after the first visit). So, the closer to each other are DAU and LDAU, the higher is the day-1 retention.

Sometimes you may need to know how many users are in the application at a particular moment. To do this, there is the Users Online metric, which estimates the number of users simultaneously playing at a particular point and is updated every five minutes. This metric is especially relevant for online games, where the interest to the game depends on the number of simultaneously playing users.

Also pay attention to the maximum Users Online within a day. Firstly, it will point out for you the peak load of the server. Secondly, it will help to identify the optimum time when the application has the maximum number of users (for example, to send push-notifications). Thirdly, it's a well-known indicator to compare the popularity of several applications.

Every visit of the application by the user is called session, and Sessions metric indicates how many sessions were in the app within a period.

And if you divide the total length of all sessions by their number, you get the Average Session Length indicator, showing the average session length. Thus it is impossible say that the longer duration of the session is better than the short one. In applications for Taxi service, the session is short: the only thing required is to reserve a car, so the shorter the session, the more convenient the service is. And, for example, in applications for reading, the sessions are usually much longer.

The Lifetime metric shows how many days in an average user apply to the application from the first to the last launch. It is recommended to use this metric for the narrow custom segments: for paying and non-paying users, for the users reached a certain level. In this case, you will know the most probable lifetimes of the users from each segment and would be able to offer user something that he is going to be interested in at the right time: push-notification, discounts and special offers, gifts, new quizzes, etc. In addition, the lifetime indicator may be used in the planning of any regular activities in your project (eg advertising campaigns). By knowing the average time a user spends in the project, you may set up events in such a way that for the most users, these events were a novelty.

It is necessary to increase the lifetime indicator as the longer the user is with you, the more loyal he is, and the greater the probability of his payment is. Think of what would encourage players to stay with you for a longer period. In online games lifetime is easily increased by introducing the regular (daily, weekly) tasks for the users.