Data Analytics Tracking

What Problem It Solves

The usual analytics failure is not missing data; it is too many events that cannot answer concrete questions: Which CTA drives signups? Where does onboarding drop off? Which campaign deserves more spend? This skill reframes tracking as a decision-first workflow: clarify key conversions, existing instrumentation, the tech stack, and privacy constraints before choosing events.

How It Works

It proceeds with an engineering-oriented flow:

• Initial assessment: review product and marketing context, current tracking, tooling, stack, and compliance needs, then ask only for missing details.
• Event design: derive events from decisions, use object-action naming such as cta_clicked or form_submitted, and put context in properties instead of event names.
• Property governance: standardize page, user, campaign, and product attributes, avoid duplicating automatic fields, and keep PII out of analytics properties.
• Implementation path: cover GA4 custom events, GTM Tags, Triggers, Variables, and data layer patterns, plus utm_source, utm_medium, and campaign naming rules.
• Validation: check triggers, property values, duplicate events, mobile behavior, and conversion recording, with debugging via DebugView, GTM preview, and variable path review.

Boundaries

This is best for web marketing sites and product apps, especially with GA4, GTM, Mixpanel, Amplitude, PostHog, or Segment. It does not replace warehouse modeling, SQL analysis, or experimental statistics. Where EU, UK, or Canadian consent, IP anonymization, retention, and deletion requirements apply, the output must be adapted to the applicable legal and platform context.