Measurement Product

Analytics Pipeline & Live Measurement

How this portfolio uses GA4, event design, BigQuery-ready modeling, and dashboarding to turn website behavior into decision-ready insight.

Pipeline status Instrumentation ready

GA4 custom events are live in the site code and structured for downstream analysis.

Add `dashboard_url` in `hugo.toml` to turn this into a live dashboard launch point.

Analytics Pipeline & Live Measurement

This portfolio is instrumented as a lightweight analytics product, not just a static website.

The goal is simple: use real visitor behavior to demonstrate how I design measurement plans, structure event data, and turn usage patterns into decision-ready dashboards.

In Progress

In Progress

Turning live GA4 data into a recruiter-ready dashboard

The measurement layer is now live on the production site and collecting real visitor behavior in GA4. The next phase is building the reporting layer: defining custom dimensions and conversions, connecting GA4 export to BigQuery, and publishing a Looker Studio dashboard that surfaces engagement, project interest, and conversion behavior.

Current focus: validate event quality, model recruiter-intent KPIs, and launch the first live dashboard view.

Measurement Layer

GA4 event design focused on meaningful behavior

I track the actions that actually signal intent: project clicks, resume downloads, contact actions, navigation behavior, scroll depth, outbound links, and project-filter usage.

Modeling Layer

BigQuery-ready event structure for analysis

The event schema is designed so it can flow cleanly into BigQuery for SQL-based KPI modeling, session analysis, and funnel views without rebuilding the taxonomy later.

Insight Layer

Dashboard storytelling built around conversion and interest

The dashboard plan focuses on which pages attract attention, which projects drive the strongest engagement, and how often visitors convert into resume downloads or contact actions.

Event Taxonomy

Core events

portfolio_page_view, portfolio_navigation_click, portfolio_cta_click, portfolio_project_click, portfolio_filter_select, portfolio_card_impression, portfolio_scroll_depth

Conversion events

portfolio_file_download, portfolio_contact_click, portfolio_outbound_click

Shared parameters

page_type, content_group, cta_location, item_name, item_category, destination_type, link_text, link_url

What This Lets Me Analyze

Acquisition to interest

Which landing pages create the most project exploration?

Measure project-click rate by landing page, source, device, and page type.

Interest to conversion

Which content actually drives resume downloads or contact intent?

Compare CTA click-through and conversion behavior across the home, about, analytics, and project pages.

Content strategy

Which project tracks attract the strongest engagement?

Use project-card impressions, filter usage, and project-click rates to understand what visitors value most.

Dashboard Blueprint

  1. Executive overview with users, engaged sessions, resume downloads, contact clicks, and project-click conversion rate.
  2. Content performance view split by page type, landing page, scroll depth, and CTA performance.
  3. Project interest view showing impressions, click-through rate, live-demo interest, and track-level engagement.
  4. Conversion view for resume, LinkedIn, GitHub, and email actions.
  5. Source and device view to compare how recruiters, hiring managers, and organic traffic behave differently.

Pipeline Architecture

Portfolio website
  -> GA4 event collection
  -> GA4 custom dimensions + conversions
  -> BigQuery export
  -> SQL views / KPI modeling
  -> Looker Studio live dashboard

Why It Matters

This setup turns my own website into a living analytics sandbox.

It shows that I can do more than install a tag: I can define success events, design a reusable event model, prepare the data for warehouse analysis, and build a dashboard that surfaces real business questions from live traffic.