Data is only as valuable as the decisions it informs. My approach to analytics centers on moving past "vanity metrics" (likes and raw sessions) to focus on revenue attribution and user intent. By building custom measurement frameworks, I translate complex dataset behaviors into clear, actionable roadmaps for stakeholders.
Google Analytics 4 (GA4) | BigQuery | SQL | Google Tag Manager (GTM) | Google Search Console | KPI Tracking
Visualization & Reporting
Looker Studio | Tableau | Microsoft Excel (Advanced) | Pivot Table Architecting | Canva (Storytelling) | Data Cleaning
Data Science Tools
Python (Pandas & NumPy) | Jupyter Notebooks | API Data Extraction | Statistical Significance Testing
Data Analytics Deep-Dives
Advanced GA4 Configuration
Standard GA4 "out of the box" only tracks the basics. I specialize in building custom measurement frameworks that capture the full nuances of a B2B user journey. I don't rely on "enhanced measurements." I implement server-side and client-side tagging via Google Tag Manager (GTM) to track high-intent actions like "PDF Whitepaper Downloads", "Pricing Page Dwell Time", and "Form Field Interactions." This transforms GA4 from a traffic counter into a lead scoring engine, allowing the sales team to prioritize prospects who have interacted with high-value content.
Conversion Rate Optimization (CRO)
Traffic is a vanity metric; conversions are a sanity metric. I use data to identify where the leaks are in the marketing funnel. I combine quantitative data (GA4 drop-off rates) with qualitative data (clarity heatmaps). If a page has a 90% bounce rate, I don't just guess why - I watch the session recordings to see if a "sticky header" is obscuring the CTA or if a mobile validation error is frustrating the user.
By increasing a conversion rate from 1% to 1.5%, I effectively grow the company's revenue by 50% without spending an extra dollar on advertising or SEO.
Multi-Touch Attribution & Revenue Mapping
In B2B SaaS, the sale doesn't happen on the first click. It's a 6-month journey. I build models that credit the assist. I move beyond last click models (which usually over credit paid ads) to data-driven attribution. I analyze the linear and first interaction paths to show how a blog post from three months ago actually warmed up the lead for the final sales demo.
This protects the SEO budget by providing its long-term ROI and helps the C-Suite understand the true customer acquisition cost (CAC) across the entire ecosystem.
Executive Dashboarding
Stakeholders don't have time to dig through GA4. I build automated dashboards that translate technical data into "boardroom language." I use BigQuery to join SEO data (GSC) with revenue data (CRM/Salesforce/et al). This allows me to create a "north star" dashboard that shows exactly how many dollars of pipeline were generated by specific organic content clusters.
This creates transparency and alignment. When the CEO can see the direct line between a technical SEO fix and a revenue spike, it builds the trust necessary to scale the department.
The Data Talks
I build custom analytical workflows to move beyond surface-level reporting, uncovering the hidden patterns in search data that drive scalable revenue growth.
Python Content Decay Analysis (Pandas)
import pandas as pd
# Load Google Search Console data (Comparison Period)
df = pd.read_csv('gsc_export_comparison.csv')
# Calculate the Traffic Delta (%)
df['Traffic_Change'] = ((df['Clicks_New'] - df['Clicks_Old']) / df['Clicks_Old']) * 100
# Identify "High-Priority Decay" (Pages down > 20% with > 500 impressions)
decay_alert = df[(df['Traffic_Change'] < -20) & (df['Impressions'] > 500)]
print(f"🚨 Found {len(decay_alert)} pages requiring urgent content refresh.")
print(decay_alert[['Page', 'Traffic_Change']].head())
