When building reports, AdLibertas Audience Reporting allows you the very powerful ability to choose between two types of dates for reporting.
Absolute Reports allow you to view user activity across a set timeframe (date is on the x-axis)
Relative Reports allows you to view user activity against their age (days since install is on the x-axis)
Absolute reports include all active users within the specified report timeframe, whereas Relative Reports only include users who've installation dates fall into the specified period.
The most familiar type of reporting, absolute reports report on all users in an audience over a date range.
Some metrics — notably retention and LTV– are unavailable in absolute reporting.
Since the reporting timeframe can be defined independently from an audience, consider audience members may not be active during the report timeframe. For instance, if you define an audience of users who’ve installed in April, then report on their performance during March, there will be no user activity.
In the example above, daily active users are mapped against an absolute date range (x-axis)
A very powerful type of reporting, Relative User Day (cohort) reports show all users against their age (days since installation on the x-axis), giving all users the same day 0.
An audience might include a variety of user ages, for instance, if your audience includes all active users over the last 7d, your audience may include a user that installed a year ago. A relative report would include this year-old user on the same day-0 as a user who installed yesterday. Therefore you can further restrict install dates when building a relative report using “Restrict user install dates”.
“Days in Lifecycle” limits how many days make up the x-axis in your chart.
To ensure accurate lifetime reporting, only users that have an install date within the dynamic report bounds will be included in this report.
Choosing more “Days in Lifecycle” than exists in an audience will result in a reporting error.
In this relative report, we see the number of daily active users against their age (days since install on the x-axis)
Since, for most, this is a unique report we’ve listed out some common use-cases to give examples:
Total revenue earned on day N (as a % of whole) against an audience
User’s average impression consumption by days active