product-analytics-setup
Product Analytics Setup
A senior PM and analyst's playbook for instrumenting product analytics correctly the first time.
Most product analytics setups are some combination of inherited mistakes, dashboard sprawl, and events nobody trusts. The team launches a new feature; instrumentation gets bolted on under deadline pressure; naming drifts; properties are inconsistent; six months later nobody can answer simple questions because the answer depends on which event you trust.
This skill is the discipline that prevents that. It assumes you have answered the strategic questions about what to measure (see analytics-strategy). It assumes you have a tool connected (Mixpanel, Heap, PostHog, Amplitude, or warehouse-native via BigQuery, Snowflake, or dbt). The hard part is the systematic execution: naming conventions, property design, schema versioning, funnel construction, cohort definitions, retention measurement.
When to use this skill: setting up product analytics from scratch, auditing an existing instrumentation, fixing a "we have data but cannot trust it" problem, or designing instrumentation for a new feature.
What this skill is for
This skill spans instrumentation execution. It does not cover measurement strategy (use analytics-strategy), experimentation result interpretation (use experimentation-analytics), paid media analytics (use ads-performance-analytics), or platform decisions (use experimentation-platform-orchestrator). Pair this skill with the relevant integrations microsite for your specific tool.
The clean distinction from analytics-strategy. That skill (Growth category) is strategic: what to measure and why, KPI hierarchy, dashboard architecture, attribution models. This skill (Product category) is execution: how to actually instrument the product correctly. The two compose. Read analytics-strategy first to decide what matters; read this skill to instrument it.
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