SHOP USED PDP COMPONENT

Before component design began, I consumed a foundational audience research report produced by Patagonia's Insights & Analytics team — combining an influence mapping framework, digital audience data via SparkToro, and a Worn Wear Messaging Survey. This research shaped what the component needed to communicate, not just what it needed to show.

A key insight from the audience profile: the Worn Wear audience shares a similar digital behavior profile to the main brand, but with a higher affinity for discounts, value, and sustainable fashion. They do brand, price, and product discovery across specialty retail sites, gear review sites, ethical shopping apps, and third-party resellers — meaning many potential Worn Wear customers were already comparing Patagonia gear against eBay and Poshmark listings before ever encountering Worn Wear directly.

Insights also identified Worn Wear as a potential acquisition engine for Patagonia — broadening the customer base where price is a barrier and appealing to younger customers excited to participate in the circular economy. Worn Wear's customer base skews younger — over 80% of under-35's shop used clothing at least a few times a year — and repeat Worn Wear customers deliver comparable lifetime value to main brand repeat customers, with 29% of repeat WW customers having 10+ lifetime orders vs. 18% of mainline customers. This was as much an acquisition and retention play as a circularity one.

One of the most significant design challenges wasn't visual — it was logical. The component needed to handle every possible inventory state gracefully: a customer who hadn't selected a size yet, one who had an exact used match, one whose size had no exact match but similar products existed, and one browsing a category where used inventory wasn't available at all. Each state required its own distinct UI, copy, and behavior.

The matching hierarchy was built on a working assumption — that customers would prioritize size over color — which shaped the cascade: the component always attempts the most specific match first, stepping down to broader alternatives rather than disappearing when inventory isn't ideal. The usability study later confirmed this assumption and solidified the logic before development handoff.

The matching logic for each state was defined precisely:

A planned usability study was scoped around three core goals:

• Understand customer response to matching scenarios — perfect vs. similar — to support used recommendation logic
• Understand how new vs. returning customer types engage with the component
• Understand user purchasing behavior to inform UI for future integration work

I designed and ran an unmoderated usability study with 10 participants — an even split of customers familiar and unfamiliar with Worn Wear. The test moved through four stages: first impressions on the PDP with the component, exploration of new and used options side by side, comparison questions, and open feedback. Synthesis surfaced four general themes:

Adding ‘Select Your Size’ to Used Tab: When a customer lands on the Used Tab in the PDP without selecting a size, the component was updated to actively prompt size selection. Because size is the most critical factor in surfacing a relevant used result, nudging customers to select their size earlier improves the quality of the used recommendation they see and reduces time spent in the lower-fidelity Similar Products state.

This change reflected the core insight directly: customers are open to similar items, but only if size is available. Getting size selection to happen earlier in the browsing flow meant more customers would see a higher-quality match — or know sooner that one wasn't available.

With the logic confirmed and the one design iteration resolved, I developed full hi-fi widget specifications for mobile and desktop — every component state, match scenario, copy need, and marketing tile variation documented. Spec files were organized around the size-selection decision point, with color-coded scenario branches making handoff clear for Engineering and Trove.