GFS Search

GFS Search Redesign for Faster Ordering

Gordan Food service (B2B eCommerce)

Improving product discovery and ordering efficiency through smarter autocomplete, filtering, and scalable search patterns.

Role: Senior Product Designer + Implementation Engineer

Timeline: 2024

Platform: Web + Mobile

Collaborators: Product Managers, Engineers, Ordering Team

Overview

Search is one of the highest-traffic features in Gordon Food Service’s B2B ordering platform. However, the original experience lacked intelligence, consistency, and flexibility, leading to missed product discovery, slower ordering, and user frustration.

The experience struggled with autocomplete relevance, filtering behaviors, and responsive usability across high-frequency ordering workflows.

I led the redesign of search across web and app, introducing smarter autocomplete, improved filtering, and reusable search components integrated into the GFS Design Library.

Problem

The legacy search experience suffered from:

• Low relevance and slow discovery

• High abandonment rates

• No support for typos, synonyms, or SKU variations

• Inconsistent UI and behaviors across devices

• Poor grouping logic and unclear hierarchy

Analytics, Baymard audit findings, and user interviews confirmed that power users — high-volume buyers — needed a much more efficient search experience to support repeat ordering.

Approach

The redesign process focused on identifying friction across the broader search ecosystem through research, workflow analysis, heuristic evaluation, and iterative exploration. I combined Baymard findings, support tickets, session recordings, and competitor analysis to better understand how users searched, recovered from errors, and navigated high-frequency ordering workflows.

Research & Audit

• Reviewed support tickets and session recordings

• Conducted Baymard-based heuristic evaluation

• Analyzed competitor flows (Sysco, Amazon Business, Grainger)

Scenario Planning

Mapped real ordering behaviors to ensure search could handle:

• Suggestive queries

• Exact matches

• No-results scenarios

• SKU-only inputs

Interaction Strategy

Explored better autocomplete and recovery experiences focused on reducing friction during high frequency ordering workflows.

Search Workflow & System Mapping

Search interactions were mapped across desktop and mobile experiences to better understand autocomplete behaviors, exact-match flows, recovery states, and repeat-order patterns. This helped identify high-impact discovery improvements and align search interactions across multiple ordering entry points.

Core Search Behaviors

Product Suggestion

Designed for users who rely on common or repeat orders.

Behavior: smart suggestions, predictive typing, and personalized ranking.

Exact Match

Prioritizes fast navigation to a known SKU or product title.

Behavior: direct PDP navigation and grouped variations.

Autocomplete by Product ID

For power users who memorize SKUs.

Behavior: immediate ID recognition and keyboard-first interaction.

No Results Found

Redesigned to prevent dead ends.

Behavior: fallback suggestions, typo handling, category pivots, and clear recovery paths.

Iteration & State Exploration

Explored autocomplete layouts and interaction states to improve scanability, product discovery, and query refinement across desktop and mobile ordering workflows. Iterations focused on reducing dead ends, improving recovery paths, and strengthening hierarchy across high-frequency search experiences.

No Results Page — Iteration Work

What we had

Basic tips and category links. Feedback showed the experience still felt like a dead end.