Case Study 2: Sizing as a Trust System

Insights that shaped size architecture, TrueSize adoption, and model imagery strategy

We tested how shoppers actually choose sizes and interpret inclusive signals. Participants overwhelmingly shop by specific size, not range, and many described “Plus” as segregated or unnecessary.

The work produced clear product direction for sizing labels, filters, and fit-assist moments that the team could implement across experiences.

My Role

Set inclusive sizing strategy

Codified sizing principles

Directed prototype iteration Aligned cross-functional delivery

Drove roadmap decisions

Key findings

1. Shoppers primarily filter by specific size, often selecting two adjacent sizes.

2. “Plus” as a label was frequently perceived as segregating and sometimes offensive; it added little value when numeric/alpha sizing was clear.

3. Adaptive model photos increased perceived fit confidence by showing bodies closer to the selected size.

4. TrueSize / Which Size Fits Me was noticeable and compelling; participants expected it as an emerging pattern.

5. Shoppers actively cross-reference multiple fit signals (size chart, reviews, imagery, tools) before committing, indicating low baseline trust in a single source of truth.

Recommendations

Unify sizing architecture; remove segmented size gates.

Reduce interpretation friction through dual alpha + numeric labeling.

Make fit confidence visible and default, not secondary.

Embedding Inclusive Fit into the Core Experience

Inclusive fit should be embedded—not segmented.

We defined a unified sizing architecture that integrates inclusive sizing into core filters, PDP interactions, and fit-assist tools. By aligning representation, labeling, and confidence signals within a single system, the experience supports diverse shoppers without fragmenting the brand.

Signal: Inclusive sizing is visible and unified at the point of discovery — no segmentation, no hidden pathways.

Interpret: Shoppers understand size relevance through dual labeling (alpha + numeric) and adaptive model imagery.

Validate: Fit confidence tools (TrueSize, reviews, fit feedback) are surfaced at decision inflection points.

Commit: Size selection feels low-risk due to consistent signals across filters, PLP, and PDP.

Reinforce: Post-selection messaging sustains confidence and reduces return anxiety.