Design Trustworthy Analytics Tracking & Measurement Strategies

This skill positions the agent as an expert in analytics implementation and measurement design, with the goal of producing tracking that yields trustworthy, decision-ready signals for marketing, product, and growth teams. Before any tracking is added or changed, it runs a required Phase 0 step: a diagnostic "Measurement Readiness & Signal Quality Index," scored 0-100 across six weighted categories - Decision Alignment (25), Event Model Clarity (20), Data Accuracy & Integrity (20), Conversion Definition Quality (15), Attribution & Context (10), and Governance & Maintenance (10). The resulting score maps to a readiness band: 85-100 is "Measurement-Ready" (safe to optimize and experiment), 70-84 is "Usable with Gaps" (fix issues before major decisions), 55-69 is "Unreliable" (data cannot be trusted yet), and below 55 is "Broken" (do not act on the data - stop and recommend remediation instead).

Once scored, Phase 1 gathers business context (what decisions the data will inform, who consumes it, what actions follow), current state (tools in use such as GA4, GTM, Mixpanel, or Amplitude, existing events/conversions, and known issues), and technical/compliance context (tech stack, ownership of tracking, and privacy or regulatory constraints).

The skill then applies a set of non-negotiable principles - track for decisions, not curiosity; start from the question you need answered and work backward to the event that proves it; events should represent meaningful state changes (intent, completion, commitment) rather than cosmetic clicks or UI noise; and data quality beats volume. It defines an event taxonomy across four categories (Navigation/Exposure, Intent Signals, Completion Signals, System/State Changes), a naming convention (object_action[_context], lowercase, underscored), and rules for event properties (include page/section, user type, and method context; avoid PII, free-text fields, and duplicated auto-properties). It also lays out conversion strategy (a conversion must represent real value, completed intent, and irreversible progress - not a page view or button click), GA4/GTM implementation guidance, UTM and attribution discipline, required validation steps (real-time verification, duplicate detection, cross-browser and mobile testing, consent-state testing), common failure modes (double firing, missing properties, broken attribution, PII leakage, inflated conversions), and privacy/compliance requirements (consent, data minimization, deletion support, retention review).

Use this skill when auditing, designing, or improving an analytics tracking setup that needs to produce reliable, decision-grade data - for example before a GA4/GTM implementation, or when existing tracking is suspected of being noisy or untrustworthy. Do not use it to simply track everything possible or to optimize dashboards on top of unvalidated instrumentation; the skill explicitly refuses to treat GA4 numbers as truth until they've been validated, and instructs stopping to recommend remediation first if the readiness score comes back "Broken."

Inputs: the business decisions the data should support, the tools currently in use, existing events/conversions and known data-quality issues, and technical/compliance constraints (tech stack, ownership, privacy requirements). Output: a Measurement Strategy Summary (readiness index score, verdict, key risks, and remediation order), a Tracking Plan table (event, description, properties, trigger, decision supported), a Conversions table (conversion, event, counting rule, used by), and Implementation Notes covering tool-specific setup, ownership, and validation steps.

Oriented around GA4 and Google Tag Manager, with references to other analytics tools such as Mixpanel and Amplitude. It links to related skills - page-cro (uses this data for optimization), ab-test-setup (requires clean conversions), seo-audit (organic performance analysis), and programmatic-seo (scale requires reliable signals).

Marketers, product managers, and growth practitioners who need to design or audit an analytics measurement strategy - defining events, conversions, and attribution rules - before trusting the resulting data for decisions.