The following document provides insights into Kochava’s Analytics Metrics which tracks app performance, user engagement, and event data. Learn how to access detailed reports and metrics via API for improved decision-making.
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 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.
