ReFine

Design Challenge

Many people own clothing they've only worn once but don't want to sell or donate. At the same time, others want stylish outfits for special events without buying new ones. Our challenge was to design a trustworthy, easy-to-use peer-to-peer clothing rental experience that promotes sustainability and community connection.

Research Insights

• Trust & Transparency: Renters want clear information about size, condition, and delivery; sellers want control and reliable transactions.
• Convenience: Users value simple listing and rental flows with minimal friction.
• Community: Verified profiles and shared experiences help foster credibility and repeat usage.

Key Features

ReFine supports both sides of the rental marketplace with features designed for trust, convenience, and community.

Iteration & Evaluation

We conducted heuristic evaluations and usability tests to refine our prototype:

• Added "undo" and confirmation pop-ups for key actions to prevent user errors.
• Improved visibility of filters and date selection feedback for better clarity.
• Simplified layouts and standardized font sizes for readability.
• Prioritized trust-building features like reviews and community posts over non-critical elements.

Design Evolution

Our design evolved from paper sketches → wireframes → digital prototype in Figma. Two major changes shaped the final version:

1. Community section revamp: Added interactive posts, polls, and user profiles to boost engagement and transparency.
2. Enhanced search and filters: Improved discoverability with detailed options for condition, size, and brand.

Throughout the process, we focused on making actions reversible, feedback immediate, and navigation predictable — small design choices that build trust in a peer-to-peer marketplace.

Impact & Next Steps

ReFine makes renting easier than buying for one-time events, which means less clothing waste and more community connection around fashion. If I were to continue this project, I'd focus on accessibility improvements (screen reader support, better color contrast), more detailed onboarding for first-time renters, and automated suggestions when errors happen (like "Did you mean size M instead of XL?").