Analytics Metrics

Metric Overview


SessionsNotification via SDK of app being launched (session_begin).ExactCount(filterset AND event_name = _SessionBegin)
Revenue_per_installTotal revenue from Purchase events divided by total count of installs.ExactRevenue / New_users
Active_usersThe count of distinct users that had a _sessionbegin or completed any named/integrated event (e.g., register, view, add to cart, purchase) during the specified time range.HLL estimateTotal_users – New_users
Total_users (or Users)Distinct count of all devices within the filter set (installs, session, events).HLL estimateHLL(filterset)
EventsNon-distinct count of all completed events (NOT installs, clicks, or sessions).ExactTotal – (Sessions + Clicks + Installs)
TotalEverything within filter set (includes installs, clicks, sessions).ExactCount(filterset)
RevenueThe sum of the revenue from Purchase events.ExactDimension_Sum(filterset AND event_name IN revenue_events)
Events_per_userTotal events (from above) divided by total users (from above).HLL estimateEvents / Total_users
ClicksThe number of clicks during the selected timeframe.ExactCount(filterset AND event_name = _Click)
New_usersThe number of installs during the selected timeframe.ExactCount(filterset AND event_name = _Install)
RPUThe total revenue/total_users for the user specified date range.HLL estimateRevenue / Total_users
LTVBy day aggregation of revenue for a defined install cohort.HLL estimateDimension_Sum(filterset AND attribution_date IN cohort)
RetainedEssentially the same as Active_users from above, however applied to a defined install cohort date range.HLL estimateHLL(filterset AND attribution_date IN cohort)
RPISame as above.HLL estimateHLL(filterset AND attribution_date IN cohort)
Average Session CountThe average number of Sessions by a particular user.HLL estimateSessions / Total_users
FunnelRetained users throughout a series of events.KMV estimateKMV(filterset AND event_name = X) INTERSECT KMV(filterset AND event_name = Y) INTERSECT …

NOTE: HLL & KVM Estimates – Visualization of data is based on Theta Sketches (streaming algorithms) to render in real-time and is 97.9% accurate. 100% accuracy available in reporting, postbacks and APIs.


Last Modified: Jan 17, 2024 at 3:48 pm