🎯 Business Context & Objective

Health Supply 770 (HS770) operates an e-commerce platform for healthcare and pharmaceutical supplies, where website performance directly impacts product demand, revenue, and operational efficiency. As traffic sources expanded and product catalog complexity increased, relying on surface-level GA4 reports was no longer sufficient to understand user behavior, conversion efficiency, or revenue leakage. The objective of this project was to build a structured website analytics workflow that enables consistent performance monitoring, deeper behavioral analysis, and decision-ready reporting for growth, product, and marketing teams.

Data & Tracking Foundation (GA4 Assessment)

HS770’s website activity is tracked using Google Analytics 4 (GA4), providing event-level data on user interactions such as page views, product interactions, add-to-cart actions, and purchases. While GA4 offers high-level visibility into traffic and conversions, its standard interface limits flexibility when analyzing funnel drop-offs, comparing acquisition quality across channels, or enforcing consistent metric definitions over time.

As an initial step, GA4 was used to validate overall traffic patterns and confirm tracking coverage, after which deeper analysis was required to accurately evaluate engagement quality, conversion behavior, and revenue performance at a granular level.

High-level GA4 reporting used to validate traffic and conversion tracking before deeper analysis.

Data Modeling & Metric Definition (BigQuery)

To move beyond GA4’s surface-level reporting, GA4 event data was queried directly in BigQuery and structured to support performance and funnel analysis at a granular level.

• Performance metric modeling: Conversion rate and purchase activity were derived from raw GA4 events by explicitly defining visitors, purchases, and transactions in SQL. This approach ensured that conversion metrics accurately reflected the actual business logic instead of GA4’s default abstractions.
• Funnel and drop-off analysis: Custom funnel logic was built to track user progression across key steps (product view, add to cart, checkout, purchase), allowing precise identification of step-level drop-offs that are not fully exposed in GA4’s interface.
• Analytics foundation for Looker: These modeled outputs served as validated data sources for Looker reporting, ensuring that dashboards were driven by consistent, query-level metric definitions rather than recalculated logic at the visualization layer.

Supporting SQL queries for metric definition and funnel analysis are included for reference. 🔗View BigQuery SQL logic

Performance Analysis & Monitoring (Looker)

This step shows how the modeled BigQuery data was used in practice to monitor website performance and surface actionable patterns. Looker was used as the reporting and exploration layer, with all core metrics already defined upstream.

• Acquisition Quality & Engagement Performance: Rather than evaluating channels based on traffic volume alone, acquisition performance was analyzed by comparing sessions alongside engagement quality indicators such as engagement rate and average session duration. This made it possible to distinguish channels that drove meaningful user interaction from those that generated visits with limited downstream value.
• Product & Conversion Inefficiency Analysis: To identify revenue leakage beyond high-level conversion rates, product-level behavior was analyzed by isolating items that were frequently viewed and added to cart but not purchased. This view helped surface products with strong intent signals but weak purchase completion, pointing to potential issues related to pricing clarity, product information, or checkout friction rather than lack of demand.

The complete Looker performance dashboard used in this analysis is available here.
🔗View full Looker dashboard (PDF)