Analytics Metrics

Metric Overview


Metric Description Exactness Formula
Sessions Notification via SDK of app being launched (session_begin). Exact Count(filterset AND event_name = _SessionBegin)
Revenue_per_install Total revenue from Purchase events divided by total count of installs. Exact Revenue / New_users
Active_users The 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 estimate Total_users – New_users
Total_users (or Users) Distinct count of all devices within the filter set (installs, session, events). HLL estimate HLL(filterset)
Events Non-distinct count of all completed events (NOT installs, clicks, or sessions). Exact Total – (Sessions + Clicks + Installs)
Total Everything within filter set (includes installs, clicks, sessions). Exact Count(filterset)
Revenue The sum of the revenue from Purchase events. Exact Dimension_Sum(filterset AND event_name IN revenue_events)
Events_per_user Total events (from above) divided by total users (from above). HLL estimate Events / Total_users
Clicks The number of clicks during the selected timeframe. Exact Count(filterset AND event_name = _Click)
New_users The number of installs during the selected timeframe. Exact Count(filterset AND event_name = _Install)
RPU The total revenue/total_users for the user specified date range. HLL estimate Revenue / Total_users
LTV By day aggregation of revenue for a defined install cohort. HLL estimate Dimension_Sum(filterset AND attribution_date IN cohort)
Retained Essentially the same as Active_users from above, however applied to a defined install cohort date range. HLL estimate HLL(filterset AND attribution_date IN cohort)
RPI Same as above. HLL estimate HLL(filterset AND attribution_date IN cohort)
Average Session Time Session duration is derived from the uptime which is received in the _sessionend event. Exact Event_Uptime(filterset AND event_name = _SessionEnd)
Average Session Count The average number of Sessions by a particular user. HLL estimate Sessions / Total_users
Funnel Retained users throughout a series of events. KMV estimate KMV(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: Oct 9, 2017 at 1:08 pm