When decluttering, most people don’t know where to start. Selling channels vary by item type and condition: buyout, consignment, C2C, or donation, but the rules, fees, and contacts are scattered across platforms, leaving users to figure it out by trial and error.

Clearspace 是一款協助使用者決定二手物品最佳處理方式的 App 概念設計。面對「要如何處理？」的問題，市面上沒有一個平台能一次解答，不同品項、狀況與時效性，適合的管道都不同。Clearspace 推薦最適合的處理方式：現金收購、寄賣、C2C 平台或捐贈，讓使用者起心動念想要處理二手物品時，可以簡易開開始！
目標：幫助使用者快速找到適合的二手物品處理管道，降低資訊蒐集與決策成本。
工具：Figma｜時程：約 4 週｜職責：獨立 UX/UI 設計師，負責端到端設計流程
涵蓋內容：使用者訪談・問題定義・資訊架構・低保真至高保真原型・可用性測試・設計迭代

Problem Overview

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User research

Summary

A survey of 10 respondents (all women) revealed that decluttering is mainly triggered by limited space. Clothing and home appliances were the most common categories. Respondents responded positively to a “routing assistant” concept, with credibility signals and estimated payout being the strongest motivators for action. Key friction points included listing creation, packing, and the overall effort of end-to-end selling.

The most frequently cited item categories

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Obstacles participants face when selling second-hand items

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Information and features that build trust and drive action

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Pain points

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Persona: Ya-Ting

User Journey

Persona: Ya-Ling is struggles with organising and administrative tasks and is willing to pay for a service that saves her time.

Stage	User goal	Key actions	Pain points	Opportunities / UX implications
1. Trigger & motivation	Clear space quickly with minimal hassle	Notices clutter; decides to declutter (year-end/seasonal change/space limits)	Overwhelm; procrastination	Offer a “quick start” flow and estimated effort/time per route
2. Decide whether it’s worth acting	Avoid wasted time and disappointment	Mentally weighs value vs. effort; considers selling vs. donating	Uncertainty about resale value; fear it won’t sell	Provide a fast “worth it?” assessment and expected proceeds range
3. Explore routes	Choose a route that feels safe and reliable	Browses platforms/shops; compares options	Too many fragmented sources; unclear eligibility/requirements	Route recommendation based on item type, condition, and deadline
4. Build trust & confirm eligibility	Feel confident before committing	Checks reviews, shop legitimacy, requirements, and constraints	Low trust; unclear rules; hidden costs	Emphasise credibility signals; transparent eligibility and terms
5. Prepare item(s)	Get ready with minimal effort	Gathers accessories/receipts/packaging; takes photos	Preparation is tedious; not sure what’s needed	Auto-generated prep checklist; photo guidelines; “what to bring” list
6. Execute the route	Complete disposal with minimal interaction	Lists item(s) or books a buyback/consignment/donation	Listing writing is burdensome; messaging/negotiation is draining	Template-based listings; assisted replies; optional “done-for-you” service
7. Handover & completion	Finish smoothly and avoid issues	Ships/meet-ups; shop drop-off; donation handover	Logistics coordination; cancellations; returns	Clear handover instructions; appointment booking; status updates
8. Outcome & reflection	Feel satisfied and reduce future friction	Receives money / confirmation; closes task	If outcome feels “unfair” (too low) users regret	Set expectations upfront; offer alternative routes (e.g., donate if buyback is too low)

Design decisions / Iterations

The results page required careful information prioritisation, so I separated ‘must-have’ details from ‘nice-to-have’ content to keep the UI scannable.
This helped prevent cognitive overload and reduced drop-off during decision-making.

Starting the Design

Colours Research

However, this product is not intended to be “another selling app.”
Instead, it helps users find the right way to deal with their second-hand items. Therefore, I chose a colour palette that communicates professionalism, reliability, and a sense of technology.

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High-fidelity prototype

The successful shop-matching journey

After analysing the questionnaire results, I did desk research and explored ideas with AI. In this case study, I focused on solving the top pain points and addressing what participants cared about most.

For the ‘matched shop found’ flow, I made these key design decisions:

1. Add location context and highlight nearby shops.
   I show which shops are close to the user, and include a short ‘Now buying’ information to clarify eligibility and improve trust. This helps users who feel it takes too much effort to get started (for example, browsing multiple channels or contacting shops one by one).
2. Provide more detail on the selected shop page.
   Once a user taps a shop, they can see clearer buying requirements and contact information, so they can take the next step with confidence.

I also adjusted the homepage to keep the experience focused:

• Reduced the sponsored area so users’ attention stays on the main task (searching).
• Added “Recent results” so users can continue where they left off.
• Added location to make recommendations feel more relevant and actionable.

The ’no suitable shop found‘ journey

If no matching shop is found, the experience falls back to a dedicated Donation page. Based on the survey, donating was the most common current behaviour (50%), so I treated donation as a primary alternative rather than an edge case. The Donation page follows the same layout structure as the shop detail page, and provides clear, practical information (such as accepted items, condition requirements, and delivery options) to help users take action quickly.

Lastly, I added a Saved page so users can bookmark shops they may want to use again, or keep for future reference. This supports repeat decisions: next time they need to sell items, they could start from these saved options instead of searching from scratch.

I also included a Profile page for key settings and support, such as location access (on/off), notification preferences (for saved shop updates), FAQs, and other basic account options.

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Going forward

Takeaways

• Trust matters more than ‘max price’. The survey suggests most users are willing to wait longer and accept a lower payout, as long as the shop feels reliable and the process is predictable.
• Reducing uncertainty is the core value. Users hesitate mainly because it feels time-consuming and unclear where to start. Clear eligibility and practical shop details help them act faster.
• A low-effort start is essential. Keeping the homepage minimal (search-first, with location and recent results) supports quick decision-making without overwhelming users.
• Donation is not just a fallback. Since donating is a common behaviour, a Donation page makes the “no match found” flow feel helpful rather than like a dead end.
• Information density needs structure. Using short features information on the results list and moving details into the shop page keeps the UI readable while still supporting informed decisions.

Next Steps

• Standardise shop data fields (eligibility, requirements, appraisal method, payout rules, contact links).
• Define rules for ‘Now buying’ updates to reduce outdated eligibility information.
• Expand and standardise donation listings (accepted items, condition, delivery methods).
• Track key metrics (detail views, contact/nav clicks, save rate, donation conversion).
• Pilot one category first (e.g., cameras & electronics) before scaling to more categories.

Thanks for your visiting.
Hoping you enjoy this journey!