Hi, I'm Marc Woo.

Product Designer shaping the AI-native discovery and commerce system.

I specialize in search and complex navigation, helping millions of users find exactly what they need.

Fan Experience and Loyalty Strategy

Dick's Sporting Goods﹒2025

I architected scalable experience frameworks for global events, bridging future-state discovery concepts with data-driven loyalty optimizations that transform seasonal engagement into long-term brand equity.

2026 World Cup: Fan Experience Vision

Delivered a modular "North Star" for global sporting events.

I designed a flexible interface logic that leverages real-time tournament data to transform seasonal fan excitement into personalized commerce hubs for events like the World Cup and Super Bowl.

Loyalty Visibility

Drove user sign-in by surfacing loyalty rewards.

I optimized global navigation to highlight available rewards and account status, creating a "Value Visibility" loop that nudges sign-ins and deepens loyalty engagement.

Search and Navigation Architecture

Dick's Sporting Goods﹒2022 – Present

I re-architected the foundational logic for product discovery, transforming a fragmented legacy schema into a single, adaptive system. This design foundation reduced technical debt and enabled intent-based retrieval for millions of queries.

Mobile Menu Optimization

Cut exit rates 30% by minimizing decision fatigue.

I overhauled mobile wayfinding, effectively reducing cognitive load while streamlining the internal taxonomy management process.

Semantic Search Experience

Translated complex ML signals into human-readable discovery results.

I designed a visual framework for Vector Search, translating complex ML predictions into transparent, trustworthy results, even for vague customer queries.

Global Navigation Architecture

Eliminated layout shifts (CLS) to prioritize content discoverability.

I optimized the global header to solve layout instability and compressed navigation height by 35%, ensuring content visibility to build trust within the first millisecond of interaction.

Browse Ecosystem and Decision Support

Dick's Sporting Goods﹒2022 – Present

I restructured the core browse framework to resolve "choice paralysis." By organizing complex inventory into comparable patterns, I accelerated the customer journey from discovery to conversion.

Active Filtering Feedback

Drove 18% lift in filter engagement via instant visual feedback.

I implemented state-change cues to validate user intent, reducing session abandonment during deep browsing.

Compare Tool

Generated a $1.28M lift in profit/visit via a 0-to-1 implementation.

I normalized complex technical data into scannable, side-by-side patterns, keeping the discovery loop internal and reducing external "tab-switching.

AI-Native Content Architecture

Defined the content structure required for high-accuracy AI retrieval.

I optimized content hierarchies to ensure product data is structured for AI retrieval (GEO) while providing high-value, in-flow education for customers.

AI-Integrated Discovery Strategy

Dick's Sporting Goods﹒2025 – Present

I defined the strategic vision for shifting the core discovery loop from reactive keyword matching to anticipatory intent prediction. This 0→1 roadmap later presented to Investor Relations as a key driver for future revenue growth.

Context-Aware UI Construction

Synthesized high-level intent into dynamic layouts.

I designed a system that utilizes implicit signals—location, time, and history—to proactively reconstruct the UI and surface relevant content, significantly reducing manual search friction.

LLM-Powered Decision Support

Aggregated fragmented metadata into a cohesive summary.

I leveraged AI to synthesize thousands of data points–Expert Tips, Inventory, Customer Reviews) into clear highlights, This progressive refinement loop builds decision confidence by simplifying complex product data.

Geospatial Health Mapping for IoT System

Novosselov Research Group﹒2021 – 2022

Designed the companion interface for AeroSpec, a portable IoT sensor system that transforms raw environmental telemetry into intuitive, actionable health signals. This architecture allows users to discover hyper-local safety trends through high-fidelity geospatial visualizations.

Longitudinal Validation

De-risking engineering investment through rigorous consumer science.

Validated the data model via 54 surveys and 3 diary studies before code commitment. This ensured the interface solved real-world respiratory needs rather than just displaying available telemetry.

Privacy-First Spatial Indexing

Abstracting individual data points into community-level safety signals.

Architected a hexagonal grid system leveraging Uber’s H3 spatial index to solve the "utility vs. privacy" trade-off. This implementation enables high-resolution community mapping without compromising the precise location of individual contributors.

Interactive Discovery Logic

Prioritizing immediate safety signals over raw data

Utilized progressive disclosure to prevent data overwhelm. The interface highlights critical "Safe/Unsafe" signals first, revealing granular pollutant metrics only upon explicit user interaction.