Data spanning 5+ years (Google Search Console + internal data sources)
Analysis across marketing channels and key site sections
Constraint
Required a consolidated view across all marketing channels
Needed deeper analysis for SEO and Paid Search
Had to ensure comparability across multiple years of historical data
Complexity
Different attribution models (last-touch vs. multi-touch)
Data split across multiple tools with different logic and reliability
Existing reporting difficult to interpret and not actionable
Result
Unified performance measurement system
Clear visibility into traffic, sign-ups, and conversions
A reporting structure that could be understood and used across teams
Foundation for ongoing analysis and decision-making
The challenge
Following shifts in performance patterns, it became difficult to clearly understand how different parts of the main website and marketing channels were contributing to business outcomes.
The issue wasn’t just performance—it was lack of clarity and usability.
Traffic data in the internal reporting system was not reliable enough for SEO analysis
Conversion data followed different attribution logic over time
Existing reporting outputs were complex and difficult to interpret
Metrics could lead to misleading conclusions when viewed in isolation
👉 Before any conclusions could be drawn, performance data needed to be translated into a system that was both reliable and easy to understand.
My role
I worked cross-functionally with SEO, paid search, and content & growth stakeholders to:
👉 Design and build a system that unified traffic and conversion data into a consistent, interpretable view of performance.
Defined how different data sources should be used
Structured performance data into meaningful site sections
Built logic to ensure consistency across time and channels
Translated complex data into outputs that could be understood and used by different teams
Enabled ongoing analysis beyond the initial investigation
The system (core work)
1. Establish a consistent measurement framework
Used Google Search Console (GSC) as the primary source for traffic data, based on its reliability for SEO
Used QuickSight (last-touch) for conversion data to maintain comparability with historical data and budget assumptions
Defined a unified framework across both datasets
Multi-touch attribution was not used due to limited historical coverage, making long-term comparisons unreliable.
👉 This ensured performance could be analyzed consistently over time.
2. Reconstruct the site structure for analysis
Extended the URL mapping system to structure ~4,500 URLs
Mapped evolving URLs (including multiple versions of feature pages) to consistent entities
Grouped pages into key sections: homepage, pricing, feature pages (tracked at feature level despite URL changes), blog, and supporting content
👉 Enabled performance analysis across time, even as URLs and structures changed.
3. Consolidate data into a unified system
Combined GSC and QuickSight data in Google Sheets
Processed large datasets across traffic and conversion layers
Used formulas and pivot tables to automate aggregation and reduce manual error
👉 Created a single, consistent source of truth.
4. Build interpretable performance metrics
Defined metrics beyond raw data: conversion rate, new account rate, relationships between traffic and conversions, average clicks per blog post, etc.
Ensured metrics were interpreted in context
A higher conversion rate did not always indicate improved performance—in some cases, it reflected declining traffic rather than stronger conversion behavior.
5. Enable multi-dimensional analysis
Built views across all marketing channels, SEO and paid channels, and branded vs. non-branded traffic (GSC-based approximation)
👉 This made it possible to identify where performance diverged across the funnel.
6. Design scalable reporting and visualization
Created dashboards for:
Monthly, quarterly, and yearly performance
Traffic, sign-ups, and conversions
Changes over time (MoM, QoQ, YoY)
Built visualizations to highlight:
Performance trends
Gaps between traffic and conversion outcomes
Areas where performance did not align with expectations
👉 The system was designed to be updated regularly and used beyond a one-time analysis.
Execution highlights
Integrated multiple data sources
Structured thousands of URLs into meaningful categories
Built scalable dashboards for ongoing performance tracking
Extended the system to include additional marketing channels (e.g., affiliates, social) as needed
Enabled cross-functional analysis across SEO, paid search, and other marketing teams
Impact
This work didn’t just produce a report. It created clarity in a system that was previously difficult to interpret and act on.
It made it possible to:
Understand how traffic translated into sign-ups and paid conversions
Identify where performance declined across key site sections
Avoid misleading interpretations of isolated metrics
Align analysis across channels and teams
👉 It transformed disconnected data into a usable system for decision-making.
Let’s work together!
If you’re working with scattered data, conflicting metrics, or unclear performance signals—I can help you design a system that turns it into something actionable.
