Overview 

WEEI operates in a high-traffic sports media environment where users move quickly between live audio, team coverage, shows, personalities, and breaking news.

I led a structured experimentation program to evaluate and optimize navigation and information architecture based on user intent and observed engagement behavior. The work focused on reducing discovery friction, improving pathways to priority content, and creating a more data-driven framework for ongoing audience experience optimization.

The Challenge

WEEI’s navigation had evolved organically over time, creating a structure that reflected internal content organization more than the way users actually moved through the experience. 

Key challenges included: 

• Discovery friction, users had to navigate across multiple pathways to find live audio, team coverage, shows, personalities, and editorial content; misalignment with user intent, navigation labels, hierarchy, and category groupings did not consistently reflect how audiences explored sports content; 
• Underutilized high-value pathways, priority destinations such as Listen Live, show pages, and team hubs were not always surfaced as clearly or prominently as user behavior suggested; 
• Limited decision evidence, navigation decisions needed a stronger behavioral and testing framework rather than relying primarily on assumptions. 

The challenge was therefore broader than redesigning a menu.

The Approach

The initiative combined behavioral analysis, information architecture refinement, and structured experimentation to identify navigation changes that could measurably improve content discovery.

1. User-Intent & Behavioral Analysis

• Analyzed engagement patterns to understand how audiences moved between live audio, team coverage, shows, personalities, and editorial content
• Identified high-intent user journeys and priority destinations across the experience
• Evaluated where existing navigation created friction or unnecessary steps in content discovery

2. Navigation & Information Architecture Optimization

• Reprioritized high-value destinations such as Listen Live, show pages, and team hubs
• Simplified category groupings and navigation hierarchy
• Refined labels and pathways to better align with audience intent and browsing behavior
• Developed navigation variants designed to improve clarity without disrupting brand or editorial structure

3. Experimentation Framework

• Translated behavioral insights into testable navigation and information architecture hypotheses
• Developed control and treatment experiences for structured A/B testing
• Defined success criteria around navigation engagement, downstream content discovery, and recirculation
• Established a repeatable framework for using experimentation to guide future discovery decisions

Testing & Measurement

The navigation changes were evaluated through structured A/B testing, with traffic split between the existing experience and the optimized variant.

Performance was measured across three primary dimensions:

1. Navigation Engagement

• Click-through rate across primary navigation elements
• Engagement with priority destinations such as Listen Live, show pages, and team hubs
• Comparative performance between control and treatment experiences

2. Downstream Content Discovery

• Engagement with editorial and audio content reached through navigation
• Movement from top-level navigation into deeper content areas
• Recirculation behavior across the platform

3. Experiment Validation

• Performance was monitored against predefined success criteria
• Results were evaluated for statistical significance before determining the winning experience
• Findings were used to validate which navigation and information architecture changes should be adopted more broadly

Results

The optimized navigation experience delivered statistically significant improvements in audience engagement and content discovery.

• 20% increase in navigation CTR, demonstrating stronger engagement with the redesigned navigation experience
• Increased interaction with priority editorial and audio content areas
• Improved pathways into deeper content, supporting stronger discovery and recirculation across the platform
• Validated a repeatable experimentation framework for using audience behavior to guide future navigation and information architecture decisions

The results demonstrated that targeted, evidence-based information architecture changes could materially improve content discovery and audience engagement at scale.

Key Takeaway

Effective navigation is not just a design decision—it is a measurable discovery system. By combining behavioral insight, information architecture, and structured experimentation, WEEI created a more evidence-based approach to improving how audiences find and engage with content.