Revenue Best Practices for Mobile App Analytics
The bottom line – app developers need to know how well they are monetizing users. They also need to know what user behavior is impacting spending to determine how to increase user lifetime value (LTV). In the era of the Freemium app economy, revenue analytics is essential.
The key revenue questions app developers should be able to answer are as follows:
- What is the top line revenue for my app or across my apps?
- What is the average revenue per user (ARPU)?
- What is user LTV?
- What are the revenue drivers and inhibitors in my app?
The app stores provide basic sales information such as the total number of apps sold and type of merchandise sold, but they don’t tie revenue back across your apps or to the user. Nor can these sales data be efficiently used in funnel, cohort and other analyses to promptly determine revenue drivers. Without this information, it is difficult to answer the key revenue questions.
Let’s take a look at sample sales reports from Google and then Apple:
As can be seen, the app store sales reports are best used for accounting, tracking top-line revenue figures and identifying basic sales trends. They don’t answer fully and flexibly the key revenue questions.
What is the top line revenue across apps?
The Apsalar dashboard shows the amount of revenue that has been generated on a cumulative monthly basis. This can be viewed within an individual app, or as seen here, across all applications:
Total revenue can also be viewed in trending reports. In this example, we are viewing weekly revenue across all applications, as well as within each individual app (DogFight & FarmNation):
What is the average revenue per user (ARPU)?
ARPU is an essential metric to track in order to determine whether the amount of revenue generated on a per user basis is increasing or decreasing. If you are bringing in more users and ARPU is steady, then you are doing well, but not as well as if you are bringing in more users and ARPU is increasing. If you are bringing in more users revenue may be showing an upward trend, but if ARPU is declining, then you are no longer monetizing your users as well. ARPU is calculated by dividing the total amount of revenue generated by the total number of active users within a given timeframe. This can be viewed across all applications or down to the individual application level. In the example below, each user is generating nearly $11 in revenue for the month.
A further example illustrates the relationship between your daily active users and ARPU. Notice the correlation of user growth (or decline) and it’s effect on monetization.
(Note: The line graph represents trends by displaying the relative values of each metric. Actual values are viewed by hovering over each line on the graph.)
What is the user LTV?
User LTV can be calculated in different ways by different developers, but typically it is used to determine how much money a user is worth over the time that they are active in your app. If a user spends $10 in your app over 6 months then that user’s LTV is $10. However, different individual users spend variable amounts of time active in your apps and spend variable amounts of money while active. This is where calculating an effective aggregate LTV number can get tricky and can vary from developer to developer.
By adding ARPU metrics to a cohort analysis, developers can define a useful proxy for user LTV by calculating the average revenue per user from the first time they launched the app over the period of time in which they remain active. In the daily cohort analysis below, the ARPU of users that launched the app for the first time on December 25 is $1.32. By looking at a cohort of your users, effectively a cross section segment of your users you can get a snapshot of LTV by tracking ARPU and monitor whether its going up or down by comparing subsequent cohorts, and in particular, those that come after you’ve updated your app with specific features designed to increase revenue.
What are the revenue drivers and inhibitors in the app?
There are a variety of ways to see what behaviors drive and inhibit spending by your users (enough, actually, that we’ll dedicate an entire post to this topic in June). Some examples include a funnel analysis to see how much revenue a particular in-app purchase is contributing and a cohort analysis to determine whether there’s a revenue increase due to a major app update.
Now that you know why its important to track revenue and how to answer the key revenue questions, in our next post, we’ll walk you through how to set up revenue tracking.
If you have any questions, feel free to drop me a line at ted at apsalar dot com.