Scaling Performance
Transforming a Complex, Decentralized Web Ecosystem into a Measurable, Continuously Optimized System
The Situation
Northrop Grumman’s website operates at enterprise scale:
17,000+ daily users across global audiences
High-stakes content supporting defense programs, government stakeholders, and media
Decentralized publishing across divisions with competing priorities
Like many large organizations, the system had evolved organically:
Content structured around internal divisions, not user needs
Inconsistent UX patterns across high-value pages
Limited visibility into where users experienced friction
Optimization efforts were inconsistent and difficult to scale
For flagship programs like Integrated Battle Command System (IBCS), this created real risk: High bounce rates, dead clicks, and fragmented messaging on pages tied directly to brand credibility.
High bounce rates, dead clicks, and fragmented messaging on pages tied directly to brand credibility.
My Role
As part of the enterprise web team, I lead efforts to:
Align content, UX, and analytics into a continuous optimization model
Identify and resolve friction across high-impact pages
Establish governance frameworks that enable scalable improvement
Translate behavioral data into repeatable design and content decisions
What I Built
1. A Data-Informed Optimization Model
Shifted from reactive updates to continuous, behavior-driven optimization:
Implemented tools including GA4, Crazy Egg, Similarweb, and SiteImprove
Identified friction points such as rage clicks, dead clicks, and drop-off patterns
Translated analytics into specific UX and content changes
Result: Optimization became systematic and measurable, not subjective.
2. High-Impact Page Transformation (IBCS Case)
Led full redesign of a flagship program page:
Simplified dense technical content into scannable, structured layouts
Improved accessibility and functionality (video, CTA hierarchy)
Reframed messaging to align with audience expectations and brand standards
Result: A high-risk page became a model for performance and clarity
3. Scalable UX and Content Patterns
Moved from one-off fixes to repeatable design systems:
Standardized component usage (accordions, hierarchy, CTA placement)
Introduced consistent content structures for complex technical topics
Designed layouts that supported both executive scanning and deep technical exploration
Increased on-page engagement 2x through UX improvements and structured content models
Result: Teams could scale improvements without reinventing the approach
4. Governance That Enables Decentralized Publishing
Built systems that balance autonomy with consistency:
Established content and UX standards across divisions
Created workflows that align stakeholders without slowing production
Enabled teams to publish with greater confidence and fewer errors
Result: A decentralized environment became coordinated without becoming rigid
5. AI-Assisted Efficiency and Discoverability
Integrated emerging AI workflows into operations:
Introduced AI-assisted tagging and metadata processes
Improved content discoverability through structured data and taxonomy alignment
Reduced manual effort while increasing consistency across the ecosystem
Result: Content became more discoverable, scalable, and future-ready
Led enterprise IA overhaul, shifting to customer-focused architecture improving usability and content findability
Outcomes
81.5% reduction in rage clicks
71.4% reduction in dead clicks
36.6% decrease in bounce rate on high-value pages
Improved speed, clarity, and discoverability across key program pages
Optimization model adopted across additional high-priority initiatives
Established governance frameworks supporting enterprise-wide publishing
Why This Matters
At enterprise scale, the challenge isn’t creating content.
It’s maintaining performance across a system that never stops changing.
This work demonstrates how:
Behavioral data can drive meaningful UX improvements
Governance enables scale without limiting teams
Optimization must be continuous, not campaign-based
Enterprise websites require systems, not one-off redesigns
I approach optimization as a systems challenge, designing structures that support continuous improvement over time.