Acceptance‑Test Telemetry Cookbook: Event Names, Schemas & Sample Queries to Prove Retention Before You Build

For prebuild experiments you want three things from telemetry: (1) a reliable activation event that represents the user’s first meaningful value, (2) a Day‑N retention signal that’s simple to compute, and (3) a minimal schema that developers can implement quickly without leaking business logic into event names. Keep the tracking plan tiny — 6–10 events at most — and push contextual variation into properties, not event names.

This approach follows tracking‑plan best practices used by product teams: standardized Object‑Action naming, minimal required properties (user_id, anonymous_id, timestamp, platform), and a single source of truth for event semantics so analysis and engineering don’t drift over time.

Below is a compact tracking plan designed for rapid implementation. Each event is necessary to answer the two acceptance questions: did users reach activation, and are they back at Day‑7? Keep property lists minimal to avoid long implementation cycles.

Implement these fields on every Track event: user_id (nullable), anonymous_id, timestamp (ISO8601), event_name, platform (web|ios|android), product_version, and experiment_variant (if applicable). Additional event‑specific properties are listed per event.

Use a cohort approach: assign users to a signup_date cohort and check whether they emit the Activation event within N days, and whether they emit CoreAction within day windows for retention. Below are two compact queries you can paste into BigQuery or your warehouse.

The SQL assumes an events table with columns: user_id, event_name, event_timestamp (UTC), and signup_date (date of first Signup). For non‑identified users, use anonymous_id and backfill when they first identify.

Set pragmatic, product‑specific thresholds before running the experiment. As a rule of thumb, many consumer apps aim for 20–40% activation within the target window and non‑viral PLG tools often look for 20–30% Day‑7 retention among activated users. These numbers are heuristics; use them as a starting point, not gospel.

Interpret thresholds in context: a low activation rate with high Day‑7 among activated users suggests onboarding problems; high activation but low Day‑7 suggests shallow perceived value. Use these signals to decide whether to iterate on onboarding, the core value, or stop development.

Hand these artifacts to your contractor: (A) the canonical event list, (B) minimal JSON schema examples for every event, and (C) the SQL/Segment queries above. Require end‑to‑end tests: a script that simulates signups and activation events and asserts the cohort queries return expected counts. This turns telemetry into an executable acceptance test.

Finally, instrument monitoring: alert if event volume drops or naming mismatches appear. Track a small set of dashboards (cohort size, activation rate, Day‑7 retention) and check them daily during the experiment window. AppWispr recommends keeping the feedback loop tight — measure, decide, and act within one 7‑day cycle.