Analytical metrics for mobile applications

Analytical indicators for effective tracking

Nowadays mobile services take an integral part in everyone’s life. A high popularity of applications creates a high competition in a mobile business. A large amount of applications appear every month, but only a small part of them receives the planned attention from users.

After the creation and the launch of the application and during the development of a promotion plan the following question are arisen: how can we monitor its effectiveness and what metrics should we use for this purpose?

There are several groups of analytical indicators: indicators that help to monitor audience growth, user retention, user activity and monetization. These indicators help to monitor the effectiveness of the created product and make the required management decisions for the further development of the project.

The indicators of audience growth

New Users is the indicator that shows the number of new users that appeared on the particular day or period. The project will grow up when the number of new users exceeds the outflow from the application. The ways of involving new users are advertising campaigns and the introduction of viral mechanisms.

Total Users is the indicator that shows the number of users who are in the database to the selected date.

Downloads show the number of downloads of the application for a specific period.

The indicators of user retention

The retention of users is very important both for the start of the project and for its development. These indicators help to determine the effectiveness of the project and to analyze the dynamics of the interest of users.

1-day Retention is the percentage of users who has returned after the first visit. Such an indicator directly reflects the user's interest in the project. Your product does not suit user if he didn’t return per day after the visit. Most often it is associated with the inconvenience of the product or the lack of functions required by the user.

7-day Retention is the percentage of users who has returned after 7 days since the first visit.

28-days Retention is the percentage of users who has returned after 28 since the first visit.

7-day Retention and 28-days Retention provide an opportunity to analyze the retention of users for a longer period. The user is interested in the product, if he returned after 7 days since the first visit and especially if the user returns after 28 days since the first visit. Ideally, all three indicators should be 100%, but it happens very rarely.

Rolling Retention is the percentage of active users who has opened the product for`the first time on the selected date. The users who have entered the application at least once within a certain period of time are taken into account.

The indicators of user activity 

The following indicators are used to estimate user activity:

DAU (Daily Active Users) is the number of active users who logged into the application on a certain day. It helps to determine the user activity throughout the day.

WAU (Weekly Active Users) is the number of active users who visited the application during the week. It helps to determine the user activity during the week. But it is not the amount of DAU for 7 days. If a unique user enters the application every day for 7 days then this indicator will increase.

MAU (Monthly Active Users) is the number of active users who visited the application within a month. This indicator is calculated in the same way as WAU.

These indicators help to determine the scale of the project, and their growth is very important. To ensure this growth it is required to maximize the flow of unique users and the percentage of their retention.

Sticky Factor = DAU / WAU or WAU / MAU, reflects the regularity of user inputs.

The indicator shows how regularly users visit the application and helps to determine their interest in your application.

But these indicators have strong fluctuations, because of the unstable flow of new users. To avoid such fluctuations the following indicators were developed: LDAU (Loyal Daily Active Users), LMAU (Loyal Weekly Active Users), LMAU (Loyal Monthly Active Users). The difference from the previous indicators is that these indicators take the number of unique loyal users into account. In this case the user is loyal if he launched the application at least once a day after the first visit.

It turns out that the closer to each other DAU and LDAU indicators are, the fewer users are who do not return to the application after a day since the first visit. Accordingly the closer to each other DAU and LDAU are, the higher is the indicator 1-day Retention.

Users Online shows how many users are in the application at a specific moment. This indicator updates every 5 minutes. Especially useful is this metric for online games. The maximum Users Online during the day allows you to determine the peak load on the server and the optimal time when the application has the maximum number of users (this can be useful for sending push notifications, for example).

Sessions is the indicator that shows the number of sessions (the number of visits of apps users) over the period.

Average Session Length is an average duration of the session (determined by dividing the total duration of all sessions by their number). This indicator is relative: in some applications a long duration of the session is a good indicator (for example, in games), in others a short one is good (for example, applications for calling a taxi).

Lifetime shows the duration of the apps usage from the first to the last entrance. It is the most effective metric for narrow user segments. It helps to determine the lifetime for each segment and to offer discounts, share gifts, etc., just in such moment when it’s required most of all. This indicator needs to be increased because the longer the user in the application is, the more loyal he is.

Monetization indicators

Paying Users is the number of active paying users per day.

Paying Share is the percentage of users from all active users during the period that have made payments.

Ideally, each user is a paying user, and Paying Share is 100%. In fact, this indicator is much smaller, and its growth of at least 1% is already a great achievement for the project.

Paying Conversion is the percentage of paying users among all those who registered for a certain period. As in the case of Rolling Retention try to find a jump in the behavior of this indicator. It is possible that it will indicate to you some conditions in which the percentage of paying players from the number of registrations is maximum. Each payment of the user is a separate transaction in the database herewith one user can make several transactions for a period (you will be only happy).

Transactions is the total number of transactions per period.

Transactions by User shows how many transactions an average paying user has made during a certain period. If no user have made repeated payments it means that it is one Transaction. Mostly this indicator becomes higher in free-to-play games, because in such projects there is a high probability that after the first payment will follow the second, the third and so on.

Gross is the total amount of all player payments for a certain period.

However, this money will not come to the developer in full measure: the application store takes their commission.

Revenue is the indicator that shows the income minus the commissions of the stores.

ROI (Return on Investment) shows the percentage of return on investment in paid traffic. It is calculated by dividing Gross by Install Costs. If ROI> 100% it means that the traffic has paid off.

ROI 7,14, 30, 60, 90 is the return on investment for 7, 14, 30, 60, 90 days respectively. They allow to evaluate the effectiveness of advertising companies for different periods of time by placing them in equal conditions. The more days have passed since the first time you log into the application, the more objective ROI will be.

RROI is calculated by dividing Revenue by Install Costs.

ARPU (Average Revenue Per User) determines the average income that is received from one active user for a period. It is calculated by dividing Revenue by Users (active audience of the period: DAU, WAU or MAU respectively). While calculating this indicator, the entire audience is taken into account: both non-paying and paying. ARPU shows the monetization efficiency of the project: the higher the indicator is, the greater the income per user for a period is. It is very convenient for comparison the effectiveness of projects.

ARPPU (Average Revenue Per Paying User) determines the average income that is received from one paying user for a period. It is calculated by dividing Revenue by Paying Users. While determining this indicator, only users who have made a payment for a certain period are considered. And the period plays a very important role: for example, the monthly ARPPU will show several payments made by the user per month, while the daily ARPPU will not show it.

Cumulative ARPU is the indicator that helps to track the audience that has appeared for a certain period. In this case the income that these users brought, as well as their number are taken into account.

Cumulative ARPU N days (for 7, 14, 30, etc. days) is a nondecreasing metric: that is, the more days since the registration have passed, the higher is this indicator.

Average check is calculated by dividing Revenue by Transactions. Many people confuse this metric with ARPPU, but the difference is that Average check takes into account the number of transactions (including repeated ones, which will not be taken into account in ARPPU).

LTV (Lifetime Value) shows the quantity of money that a user brings in average for the entire time of usage the application. This indicator combines both the duration of the user's activity and his payments, and allows to determine the price of attracting a new user. For an effective business, it is necessary LTV to be more than the cost of attracting users. Therefore, the longer the user in the application is and the higher the average income from him is, the higher will be LTV.

In this article we have surveyed key analytical metrics. Nevertheless, the most important thing while analyzing an application is not the indicators, but their interpretation.

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