Founder Analytics Plan: 5 KPIs, Event Names & Instrumentation Spec to Ship with Any App Brief
Written by AppWispr editorial
Return to blogFOUNDER ANALYTICS PLAN: 5 KPIS, EVENT NAMES & INSTRUMENTATION SPEC TO SHIP WITH ANY APP BRIEF
Ship product instrumentation that engineers can implement and analysts can trust. This one‑page analytics pack gives founders a battle‑tested set of five core KPIs, a one‑page event naming table, SQL‑friendly sample event schemas, and an acceptance checklist so your team instruments useful data before launch.
Section 1
Pick 5 core KPIs that align to your North Star
Founders need a compact set of KPIs that map directly to product actions your team can measure and improve. Start with one North Star and five supporting KPIs that cover acquisition, activation, retention, revenue and referral — the AARRR lens keeps the list tight and actionable.
For each KPI define: a human description, formal SQL definition, update cadence (daily/weekly), and a counter‑metric that flags bad optimization (e.g., use rate vs. support tickets). This prevents chasing vanity numbers and keeps engineers clear on which events matter.
- North Star: the single metric most predictive of long‑term value (e.g., Weekly Core Actions per Active User).
- KPI 1 — Acquisition: New users who reach first key step (SQL: count(distinct user_id) where event = 'signup' AND user_property.source != 'test').
- KPI 2 — Activation: % of new users who complete 'first key action' within 7 days.
- KPI 3 — Retention: D7 return rate for users who activated in period.
- KPI 4 — Revenue: Free→paid conversion within 30 days or ARPU, defined clearly per billing period.
- KPI 5 — Referral/Network: Invite acceptance rate or invites per activated user.
Sources used in this section
Section 2
One‑page event naming table every brief should include
Inconsistent names are the fastest route to broken reports. Use a predictable, query‑friendly convention: <domain>_<entity>_<action> (e.g., app_user_signup, product_item_viewed). Avoid encoding IDs or state into names — put those in event properties.
Add a short description column, who owns the event, required properties, and enforcement status. Keep the table to one page so engineers and PMs can review it during planning and standups; this reduces rework and instrumentation debt.
- Naming convention examples: app_user_signup, app_onboard_completed, billing_subscription_started.
- Required properties for user‑level events: user_id, anon_id, created_at, platform, source. For product events: item_id, item_type, price (if relevant).
- Governance: each event must list owner (PM/engineer), schema version, and test plan.
Section 3
SQL‑friendly event schemas (copyable examples)
Design events so they map cleanly into analytics tables. Each tracked event should have: event_name, event_ts (ISO8601), user_id (nullable), anon_id, properties (JSON), sdk_version, platform, environment. This model keeps event tables queryable in SQL warehouses without extra joins.
Include canonical property names and types in the spec. Example: event 'app_purchase' → properties: { item_id: string, item_category: string, price_cents: integer, currency: string, coupon_code: string|null }. Specify which properties are indexed or flattened for frequent queries.
- Minimal event table columns: event_name, event_ts, user_id, anon_id, session_id, properties_json, platform, sdk_version, environment.
- Flatten properties you query often (e.g., is_paid_user boolean, plan_tier string) to avoid repeated JSON parsing in big queries.
- Version the schema (schema_v) and require backward‑compatible changes for minor version increments.
Section 4
Acceptance checklist engineers can run before merge
Turn instrumention into a short QA checklist that gates merges for release builds. The checklist should confirm event names match the one‑page table, required properties appear with correct types, timestamps are correct, and the SDK reports to the staging dataset/stream.
Automate the checklist where possible. Add unit tests for event payloads, lightweight end‑to‑end checks that assert events arrive in the staging pipeline, and a sample SQL query that reproduces the KPI for one synthetic test user.
- Checklist items: event emitted in staging, event name matches spec, required properties present and typed, event timestamp within expected window, GDPR/consent flag honored.
- Automation pointers: schema tests (CI), e2e event smoke test, sample KPI query that returns the test user.
- Sign‑off: engineering owner, analytics/PM owner, date, and a brief test log attached to the PR.
Section 5
How to include this pack in your app brief and avoid common failure modes
Embed the one‑page pack in every product brief (one file, three sections: KPIs, event naming table, acceptance checklist). Make instrumentation a deliverable with acceptance criteria rather than a polite request — that changes prioritization during sprints.
Watch for common mistakes: ad hoc event names created by feature teams, failing to document property types, and skipping staging validation. Fix these with a small governance loop: weekly instrumentation review, a shared tracking plan, and a single owner for analytics definitions.
- Deliverable: 'Analytics pack' attached to PR describing KPIs, events, sample schema and checklist.
- Governance: weekly 15‑minute tracking review + a living tracking plan file in the repo/docs.
- Countermeasures: require schema tests, enforce naming in linters or SDK wrappers, and use staging pipelines for validation.
FAQ
Common follow-up questions
Which single North Star should I pick for an early consumer app?
Choose the metric that best captures users receiving the core value. For a messaging app it might be Weekly Active Users who send at least one message; for a marketplace it might be completed transactions per buyer per month. Your North Star should be directly influenced by product work and predictive of retention.
How many events are too many?
Start small: instrument the events that map to your five KPIs and the key funnel steps. Add events only when they answer a decision. Excess events add noise and maintenance cost; prefer property‑rich events over many narrowly named events.
Should I store all event properties as JSON or flatten them?
Store a properties JSON for flexibility, but flatten any fields you query frequently (e.g., is_paid_user, plan_tier, item_category). Flattening reduces repeated JSON parsing and speeds up SQL queries.
How do I ensure privacy and consent in the spec?
Include consent flags (analytics_consent boolean) and environment tags in each event. Design the pipeline to drop or anonymize PII when consent is absent, and document which properties are PII so engineers can avoid sending them.
Sources
Research used in this article
Each generated article keeps its own linked source list so the underlying reporting is visible and easy to verify.
IdeaPlan
The Product Analytics Handbook - Full Guide
https://www.ideaplan.io/analytics-guide/read
Grain Analytics
Event Naming Convention - Grain Analytics
https://docs.grainql.com/core/event-naming
KPI Tree
AARRR Pirate Metrics and Metric Trees
https://kpitree.co/guides/frameworks/pirate-metrics-aarrr
OpsBlu
Simple Analytics Event Tracking Setup | OpsBlu Docs
https://opsblu.com/documentation/analytics-platforms/simple-analytics/setup-and-implementation/event-tracking/
David Wells
Using event naming conventions to keep analytics data clean.
https://davidwells.io/blog/clean-analytics
Next step
Turn the idea into a build-ready plan.
AppWispr takes the research and packages it into a product brief, mockups, screenshots, and launch copy you can use right away.