Acquisition and Engagement Metrics

Get to know major default metrics that can be implemented for any app regardless of its type.
Acquisition and Engagement Metrics

Today we start new series where we'll discuss all major metrics and techniques which can be very useful for mobile app analytics. 

In the first post we will focus on default metrics that can be implemented for any app regardless of its type. 

All these metrics can be divided into three groups: user acquisition, engagement and monetization metrics.

1. User acquisition metrics

Downloads – number of users, who downloaded an app from an app store

Total User – total number of users who has launched an app at least once. It is a cumulative metric which summarizes users who opened an app from the earliest available date till the chosen date.

New Users – total number of new users at the chosen period. This metric is calculated when user launches an app for the first time.


An app was integrated with the analytical system on the 1st of May.

Total users at the 1st of September will show how many users have opened an app at least once during the period 1st of May – 1st of Sept.

At the 1st of September an app has been downloaded by 100 users and 95 of them have opened it. In that case:

  • Downloads =100
  • New Users = 95
  • Total users +95


"New users" metric counts only those users who opened an app that's why the number of new users may differ from the number of downloads:

User actions


New Users

Total users

Downloaded and launched an app




Downloaded an app, but haven’t opened it




First time launched an app downloaded earlier




2. Engagement metrics

Sessions – total number of sessions within a given period. Session is counted every time when the user launches an app.

Average Session Time — average time spent in the app per user. Defined as the sum of the length of all sessions divided by the number of unique users within a given period.

DAU/WAU/MAU (daily active users, weekly active users and monthly active users) – total number of unique users visiting the app daily, weekly or monthly.


Three users have downloaded an app from the App store. User1 opened an app once, User2 – 5 times and User3 didn't open an app after it was downloaded.




Daily active user













In that case:

  • Downloads = 3
  • Sessions =1 + 5 = 6
  • DAU = 2


You may calculate average number of sessions per user by data on MAU and average DAU for a chosen month:

Sessions by user = (DAU*N)/MAU,

where N – is a number of days in chosen month.

LDAU/LMAU/LWAU (loyal DAU, loyal WAU and loyal MAU)  number of unique users visiting the app daily, weekly or monthly. Includes users who downloaded and opened the app for the first time not less than one day before the chosen date.

These metrics are useful when you need to estimate the volume of the loyal audience substracting the users who have opened an app only once.


Due to your carrying out the advertising (marketing) campaign the number of downloads increased.

The sharp jump of such metrics as New Users and DAU doesn't mean that your loyal audience has increased by this number of users, because some of them may launch an app only once and never come back to it.

Its easy to evaluate ad channels quality and to track loyal audience growth by using LDAU, LWAU and LMAU.

At the graph below you can see the difference between DAU and LDAU growth during the ad campaign. Lifetime – average number of days user stays active. This metric shows how long your app is able to hold user attention.

There are several ways to calculate user lifetime: you can either count average number of days user spends in the app or count an average users "age" at the chosen day.

In the first case you also can use cohorts (groups of users composed on the basis of first launch date) to estimate number of days from the first launch to the last one.

In the second case what you are looking at is an average number of days that have passed from the first launch for users who has opened an app at chosen day (week or month).

In the next articles, we will discuss retention rates and monetization metrics.

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