H&M - Find Your Style
(About)
Client:
H&M
Industry:
Fashion / Retail
Role:
User Researcher, UX & UI Designer
Duration:
2 months
Client:
H&M
Industry:
Fashion / Retail
Role:
User Researcher, UX & UI Designer
Duration:
2 months
Client:
H&M
Industry:
Fashion / Retail
Role:
User Researcher, UX & UI Designer
Duration:
2 months
(Process)
(01)
Overview
Find Your Style is an in-store, swipe-based experience for H&M that helps shoppers quickly discover their personal style, favorite items, and build a shareable wish list that hands off seamlessly to mobile for online purchase. My role spanned user research, workshop facilitation, interaction & UI design. I partnered closely with H&M stakeholders and Phygrid engineers to align business goals (engagement & conversion) with a playful, low-friction customer journey. We initially targeted men’s outfits to validate the concept with a cohort more inclined to buy complete looks, then prepared the system for category expansion.
(01)
Overview
Find Your Style is an in-store, swipe-based experience for H&M that helps shoppers quickly discover their personal style, favorite items, and build a shareable wish list that hands off seamlessly to mobile for online purchase. My role spanned user research, workshop facilitation, interaction & UI design. I partnered closely with H&M stakeholders and Phygrid engineers to align business goals (engagement & conversion) with a playful, low-friction customer journey. We initially targeted men’s outfits to validate the concept with a cohort more inclined to buy complete looks, then prepared the system for category expansion.
(01)
Overview
Find Your Style is an in-store, swipe-based experience for H&M that helps shoppers quickly discover their personal style, favorite items, and build a shareable wish list that hands off seamlessly to mobile for online purchase. My role spanned user research, workshop facilitation, interaction & UI design. I partnered closely with H&M stakeholders and Phygrid engineers to align business goals (engagement & conversion) with a playful, low-friction customer journey. We initially targeted men’s outfits to validate the concept with a cohort more inclined to buy complete looks, then prepared the system for category expansion.
(02)
Challenge
In-store digital touchpoints often attract initial curiosity but fail to sustain engagement or drive next actions. We needed to design an interface that: - Onboards instantly across a mixed audience (younger visitors who understand swipe conventions vs. older visitors who don’t), - Feels fun yet purposeful so it doesn’t become a gimmick, - Bridges physical→digital with a reliable handoff to mobile, - Works within store constraints (lighting, glare, quick sessions, variable foot traffic) and a content pipeline that can refresh outfits without breaking the UX.
(02)
Challenge
In-store digital touchpoints often attract initial curiosity but fail to sustain engagement or drive next actions. We needed to design an interface that: - Onboards instantly across a mixed audience (younger visitors who understand swipe conventions vs. older visitors who don’t), - Feels fun yet purposeful so it doesn’t become a gimmick, - Bridges physical→digital with a reliable handoff to mobile, - Works within store constraints (lighting, glare, quick sessions, variable foot traffic) and a content pipeline that can refresh outfits without breaking the UX.
(02)
Challenge
In-store digital touchpoints often attract initial curiosity but fail to sustain engagement or drive next actions. We needed to design an interface that: - Onboards instantly across a mixed audience (younger visitors who understand swipe conventions vs. older visitors who don’t), - Feels fun yet purposeful so it doesn’t become a gimmick, - Bridges physical→digital with a reliable handoff to mobile, - Works within store constraints (lighting, glare, quick sessions, variable foot traffic) and a content pipeline that can refresh outfits without breaking the UX.
(03)
Solution
We took a research-first approach: discovery workshops with H&M, field observations in store, and internal user tests to shape a simple, swipe-first journey. Key elements: - Behavior model → swipe exploration. Quick “Tinder-like” decisions lower cognitive load and surface preferences early. - Micro-onboarding & nudges. Short helper text + subtle animations teach the gesture; this addressed the older segment who needed a cue. - Style persona framing. Behind the scenes, liked outfits map to lightweight style archetypes that make feedback feel personal rather than algorithmic. - Favorites & list building. Users can shortlist items and generate a shopping list that transitions to their phone via QR or link. - Omni-channel bridge. The mobile handoff supports later purchase and keeps momentum beyond the store visit. - Design for store reality. High-contrast UI, touch targets for quick taps, minimal steps, and motion timing tuned for “glanceable” moments.
(03)
Solution
We took a research-first approach: discovery workshops with H&M, field observations in store, and internal user tests to shape a simple, swipe-first journey. Key elements: - Behavior model → swipe exploration. Quick “Tinder-like” decisions lower cognitive load and surface preferences early. - Micro-onboarding & nudges. Short helper text + subtle animations teach the gesture; this addressed the older segment who needed a cue. - Style persona framing. Behind the scenes, liked outfits map to lightweight style archetypes that make feedback feel personal rather than algorithmic. - Favorites & list building. Users can shortlist items and generate a shopping list that transitions to their phone via QR or link. - Omni-channel bridge. The mobile handoff supports later purchase and keeps momentum beyond the store visit. - Design for store reality. High-contrast UI, touch targets for quick taps, minimal steps, and motion timing tuned for “glanceable” moments.
(03)
Solution
We took a research-first approach: discovery workshops with H&M, field observations in store, and internal user tests to shape a simple, swipe-first journey. Key elements: - Behavior model → swipe exploration. Quick “Tinder-like” decisions lower cognitive load and surface preferences early. - Micro-onboarding & nudges. Short helper text + subtle animations teach the gesture; this addressed the older segment who needed a cue. - Style persona framing. Behind the scenes, liked outfits map to lightweight style archetypes that make feedback feel personal rather than algorithmic. - Favorites & list building. Users can shortlist items and generate a shopping list that transitions to their phone via QR or link. - Omni-channel bridge. The mobile handoff supports later purchase and keeps momentum beyond the store visit. - Design for store reality. High-contrast UI, touch targets for quick taps, minimal steps, and motion timing tuned for “glanceable” moments.
(04)
Result & Conclusion
The pilot launched successfully and remains live, with H&M’s analytics showing sustained engagement and healthy completion through the style flow. Meaningful product interest emerged across favorite items and mobile handoffs — customers arrived better prepared, with a clearer sense of what they liked. Store staff reported smoother conversations and increased purchase intent, confirming the value of linking playful discovery with informed decision-making. The concept proved adaptable across categories, and its underlying UX pattern — fast visual scanning, low-friction input, and contextual handoff — continues to guide future retail experiments at H&M.
(04)
Result & Conclusion
The pilot launched successfully and remains live, with H&M’s analytics showing sustained engagement and healthy completion through the style flow. Meaningful product interest emerged across favorite items and mobile handoffs — customers arrived better prepared, with a clearer sense of what they liked. Store staff reported smoother conversations and increased purchase intent, confirming the value of linking playful discovery with informed decision-making. The concept proved adaptable across categories, and its underlying UX pattern — fast visual scanning, low-friction input, and contextual handoff — continues to guide future retail experiments at H&M.
(04)
Result & Conclusion
The pilot launched successfully and remains live, with H&M’s analytics showing sustained engagement and healthy completion through the style flow. Meaningful product interest emerged across favorite items and mobile handoffs — customers arrived better prepared, with a clearer sense of what they liked. Store staff reported smoother conversations and increased purchase intent, confirming the value of linking playful discovery with informed decision-making. The concept proved adaptable across categories, and its underlying UX pattern — fast visual scanning, low-friction input, and contextual handoff — continues to guide future retail experiments at H&M.
