Product-Led SEO Architecture for Scalable Revenue Growth
Executive Summary
Designed and implemented a product-led SEO strategy that transformed Fabric.com’s dynamically generated category pages from an indexing liability into a scalable organic growth engine. By partnering directly with software engineering to automate page-level SEO metadata based on real-user intent, the initiative unlocked significant organic visibility and revenue growth without manual content overhead.
Context
Company: Fabric.com (an Amazon subsidiary at the time)
Industry: E-commerce / Retail
Role: SEO Channel Manager
Environment: Large product catalog, dynamic category architecture, high SKU velocity
Fabric.com’s merchandising system generated product category pages dynamically based on tags and filters applied by the merchandising team. This architecture supported merchandising flexibility but introduced severe limitations for organic search performance.
The Strategic Problem
Fabric.com did not have a content problem. It had an architecture-to-growth mismatch.
Key constraints:
Category pages were generated on-the-fly, with no backend support for SEO-critical elements such as:
Meta titles and descriptions
H1s and page-level copy
Page URLs were driven by combinations of tags and filters, creating:
Near-infinite page permutations
Crawl inefficiency and index bloat risk
Manual optimization was impossible at scale given SKU volume and filter combinations
From a growth strategy perspective, the core question was:
How do we allow search engines to understand and value dynamically generated category pages without introducing manual content debt or restricting merchandising flexibility?
Objectives
Enable SEO value on dynamic category pages without manual intervention
Align organic search visibility with real customer intent
Prevent index bloat while improving crawl efficiency
Drive incremental organic traffic and revenue at scale
Build a solution that engineering and merchandising could sustain
Strategy & Approach
1. Reframed SEO as a Product Architecture Problem
Rather than treating this as an SEO copy or tagging issue, I framed it as a product and systems design problem:
Search engines could not interpret the intent of dynamically generated pages
Customers were expressing clear intent through filters (e.g., color, material)
The system already knew what the page represented. It simply wasn’t exposing it
This reframing shifted the solution from manual optimization to automation.
2. Partnered Directly With Engineering to Automate Page Semantics
I worked closely with a software engineer to design an automated solution where:
Each time a category page was generated, backend logic:
Parsed the active tags and filters
Generated SEO-relevant page elements dynamically
Page elements populated included:
Meta title
Meta description
H1 and supporting page descriptors
Example: If a customer filtered for red cotton fabric, the system automatically generated page elements aligned to “Red Cotton Fabric,” creating a search-intelligible category page without manual setup.
3. Preserved Merchandising Flexibility While Enabling Scale
A key design constraint was not breaking existing workflows:
Merchandising retained full control over tags and filters
No manual SEO rulesets were required
The system scaled naturally as new products, colors, or materials were introduced
SEO became an emergent property of the platform, not an add-on process.
Results
Enabled indexable, intent-aligned category pages at scale
Significantly increased organic visibility across long-tail, high-intent queries
Contributed to a 35% increase in SEO-driven revenue
Improved crawl efficiency and reduced the risk of low-value index bloat
Created a durable SEO advantage competitors could not easily replicate
Strategic Impact
SEO shifted from content-heavy execution to system-level leverage
Organic growth scaled without proportional increases in manual effort
Engineering and marketing collaborated around shared outcomes rather than tickets
The platform itself became a growth asset