Telemetry for the First 10 Activation Events: A Founder’s Map to Measure What Actually Predicts Paid Conversion

Below is the minimal, product-agnostic set of the first 10 activation events every microfeature or mini‑product should emit. Use snake_case event names and keep property names stable (string or number type enforced). These events are the backbone for funnels, path analysis, and cohorting to paid conversion.

Instrument exactly these events (use these canonical names), and attach the listed properties. If a property is optional, mark it with (opt). Use the same property types everywhere — inconsistent types are the most common source of analysis errors.

These events are chosen to capture the three signals that predict conversion in most microproducts: intent (actions attempted), success (actions completed), and stickiness (repeat / settings / reactivation). Funnels built from these events let you test whether a demo action actually leads to conversion — not just clicks.

A simple funnel example: signup_completed → first_view → primary_action_attempted → primary_action_succeeded → paid_conversion. Track conversion lift by running A/Bs where you modify the step that most often drops users and measure lift on paid_conversion. Make sure paid_conversion is a server-confirmed event (not only client fire-and-forget).

Pick and enforce one naming convention (we recommend object_action in snake_case) and a schema policy for properties: types, required vs optional, max cardinality for string properties. Document the taxonomy centrally and enforce it in PRs and CI. Platforms like Amplitude and Mixpanel provide guides and governance tooling to keep taxonomy healthy.

Avoid ambiguous event names, avoid user-visible IDs in event names, and treat event names as stable contracts. When you need to change an event, version it (e.g., primary_action_succeeded_v2) and keep the old name alive until you’ve migrated downstream analyses.

Start without sampling for your activation funnel while you iterate — the first 1‑5k users need full fidelity. After stabilization, apply deterministic sampling (e.g., hash(user_id) % 100 == n) for high-volume non-funnel events. Never sample core funnel events (signup_completed, primary_action_succeeded, paid_conversion).

Implement dynamic sampling tiers: full-fidelity for funnel/paid cohorts; 1–10% sampled for background telemetry and diagnostic events; adaptive sampling for noisy events (increase sample when entropy is unchanged). Ensure your sampling is reversible in analysis — store sample_rate as an event property so analyses can up‑weight or model for the sampling scheme.

Treat event telemetry as personal data where it can be linked to an identifiable user. Implement consent gating: for EU users, refuse non-essential analytics until explicit consent. Capture consent events: analytics_consent_given and analytics_consent_revoked with properties: user_id (opt), consent_version, timestamp. These consent events are critical for audits and for driving conditional telemetry.

Never send raw PII (emails, full names, credit card fragments) in event properties. Use stable user_id or pseudonymous identifiers, and if you must retain sensitive states (plan tier, hashed email for dedupe), store hashes or enums and document why and where they are used. When possible, route personally identifying conversion confirmations (paid_conversion) through the server and keep client fires only as signals (then reconcile server-side).