Reframing Search & Discovery for Enterprise Retail
Defining enterprise discovery strategy across complex commerce systems
L.L.Bean needed to strengthen high-intent discovery across mobile and desktop commerce. Existing search and navigation patterns were driving conversion, but lacked alignment with contextual user behavior.
Through behavioral research and competitive benchmarking, I defined enterprise discovery principles that guided iteration across search architecture and filtering systems. Subsequent testing informed performance-optimized direction.
This work aligned discovery systems with real-world decision behavior, improving clarity across browsing, directed search, and cross-category exploration. It introduced a governing framework that shaped ongoing search and navigation investments.
My Role
• Led enterprise discovery strategy across digital commerce
• Defined behavioral principles for search and navigation architecture
• Partnered with designers and researchers to operationalize insight
• Aligned Product, UX, and Engineering on iterative direction
• Shaped roadmap priorities through executive recommendations
Home Depot
Bed Bath & Beyond
Bed Bath & Beyond
Users were asked to find an outdoor heater on Home Depot (competitor retailer of outdoor furniture). Predictive inline completion reinforced directed intent. Suggested categories clarified scope before result navigation.
Bed Bath & Beyond
Bed Bath & Beyond
Bed Bath & Beyond
On Bed Bath & Beyond (competitor retailer of home goods), users searched for a queen sheet set. Search suggestions surfaced attribute-level refinement early, supporting exploratory comparison behavior.
Sorel
Bed Bath & Beyond
Sorel
Sorel is a footwear competitor of LLB. Here, users searched for winter boots. No predictive modeling on this site. nstead, it relied on result listing. Increased cognitive load during ambiguous search queries.
Translating Behavioral Observation into System Direction
Across twelve in-depth qualitative sessions, we analyzed mobile search behavior to uncover how intent shifts across context, familiarity, and purchase readiness. Rather than optimizing isolated interactions, we mapped the underlying mental models driving search reformulation, refinement, and abandonment.
Using session recordings, participant quotes, and interaction flows, we synthesized a behavioral framework illustrating how discovery patterns vary by use case and user type. This revealed a critical insight: context—not query structure—is the primary driver of mobile search behavior.
That framing informed architectural recommendations prioritizing intent clarity, predictive scaffolding, and adaptive refinement.
Driving Performance-Validated Direction
Following the research readout, I partnered with UX and Product leadership to translate behavioral findings into testable redesign concepts. We structured an A/B experimentation plan to validate predictive modeling, refinement patterns, and intent signaling within the mobile search experience.
Testing outcomes informed the final production implementation, aligning search architecture with observed user behavior.