Mobile App
Swipe Buy — A Human-Centred Thrift Shopping App
Project Overview
Swipe Buy is a concept mobile app designed to make second-hand fashion discovery more engaging and enjoyable for young consumers. The project tackles a real usability gap in the thrift shopping market — existing platforms like Vinted and Depop rely on cluttered, grid-based layouts that overwhelm users and turn browsing into a chore. By borrowing the swipe interaction model popularised by dating apps like Tinder, Swipe Buy transforms product discovery into a fast, fun, and low-effort experience where users swipe right to save an item and left to pass.
The design followed the ISO 9241-210 Human-Centred Design framework throughout — from literature review and user research, through to wireframing, high-fidelity prototyping, and heuristic evaluation. Every decision was grounded in real user needs, validated by a survey of 29 participants and refined through expert review.
The Problem
The second-hand fashion market is growing rapidly, yet the apps designed to serve it are failing their users. Platforms like Vinted and Depop rely on dense, grid-based layouts and complex filtering systems that prioritise search efficiency over the user's emotional journey. The result is decision fatigue — browsing feels like a chore rather than an enjoyable activity.
A survey of 29 users confirmed this gap clearly. 62.1% identified difficulty filtering items effectively as their biggest frustration when browsing without a specific item in mind, and 17.2% described current apps as cluttered and overwhelming. Meanwhile, 65.5% said their primary motivation for thrift shopping was to find unique items and express their personal style — an experience that existing platforms consistently fail to deliver.
There was clearly an opportunity to design something different: an app that makes discovery feel effortless, personal, and genuinely fun.
The Solution
Swipe Buy reimagines thrift shopping by applying the swipe interaction model — familiar from dating apps like Tinder — to second-hand fashion discovery. Instead of scrolling through overwhelming grids, users are presented with one product card at a time. A swipe right saves the item; a swipe left passes it. This binary decision model reduces cognitive load, makes browsing feel instinctive, and turns product discovery into an enjoyable, almost addictive experience.
The app combines this gamified interaction with an algorithm-driven personalised feed, capturing style preferences during onboarding and continuously adapting based on swiping behaviour. Robust inline filtering — by brand, price, size, and condition — is accessible without ever leaving the swipe screen, keeping users in flow at all times
User Research
The research phase combined a review of existing academic literature with primary user research to validate the core concept and uncover real user needs before any design decisions were made.
Literature Review
Three key themes shaped the academic foundation of this project:
The rise of sustainable fashion — The second-hand apparel market is not just growing, it is evolving into a form of value-added consumption, driven by young consumers motivated by personal expression and environmental responsibility, not just price.
Hedonic benefits as a success factor — Research shows that successful mobile apps must deliver entertainment and enjoyment alongside practical value. Apps that neglect the user's emotional journey risk disengagement and abandonment.
Swipe logic as an engagement driver — The swipe mechanic, popularised by Tinder, transforms complex decision-making into a fast-paced, rewarding binary action. Applying this proven logic to product discovery was the central design hypothesis of this project.
Also, a comparison of 2 thrift apps (Vinted and Depop) alongside one of the most famous dating apps, “Tinder”. The main goal is to find feature opportunities for the “Swipe & Buy” app.
User Survey
To validate these assumptions with real data, an anonymous online questionnaire was conducted with 29 participants from the target demographic. Four key findings emerged:
79.2% had purchased second-hand clothing before, confirming an active and comfortable market
65.5% cited finding unique items and expressing personal style as their primary motivation — outranking saving money
72.4% found the "Tinder for clothes" concept Very or Somewhat appealing
75.9% identified good filtering options as the most valuable feature, followed by a personalised feed (69%) and high-quality photos (55.2%)
These findings directly informed the app's core features and design priorities, ensuring every decision was grounded in evidence rather than assumption.
User Profile and Personas
Two personas were then developed to represent the two ends of this audience:
User Profile
Before designing any screens, a clear picture of the target user was built from the survey data and literature findings. The core audience is 18–30 year olds, primarily based in the UK, who access the internet almost exclusively via smartphone. They are highly familiar with swipe-based and social apps like Instagram, TikTok, Tinder, and Hinge — meaning the swipe mechanic requires zero learning curve. They browse for clothes as a leisure activity, not a task, and are driven by discovery and style expression as much as by price.
User Personas
Task Models
Two realistic task scenarios were created, one for each persona, to ground the design in concrete, everyday situations:
Chloe's Scenario — On her lunch break, she opens Swipe Buy with no specific item in mind. She simply wants to browse, discover, and save anything that catches her eye by swiping right.
Ben's Scenario — Between university lectures, he needs to find and buy a warm winter jumper from a known brand, within budget, as quickly as possible.
These scenarios ensured every screen and interaction was designed around a real use case, not a hypothetical one.
Task Scenario 1 - Chloe
Task Scenario 2 - Ben
Conceptual Model
The conceptual model maps the main objects in the Swipe Buy system and how they interact with each other
User Requirements
Before moving into design, all requirements were documented across three categories to ensure the final product was both user-centred and technically grounded.
Usability Requirements
Technical and Environmental Requirements
Functional Requirements
Design Process
With research and requirements in place, the design phase moved through three progressive stages — from rough sketches to a fully realised, clickable prototype.
Stage 1 — Sketches (Lo-Fi)
The process began with hand-drawn sketches to rapidly explore layout ideas and interaction patterns before committing to any digital work. This stage was intentionally rough — the goal was to generate and discard ideas quickly, exploring multiple solutions for the onboarding flow, the home swipe screen, item details, and the liked items list.
Stage 2 — Wireframes (Lo-Fi)
Sketches were then translated into clean digital wireframes for all key screens. Each screen was designed with a clear, focused purpose
Stage 3 — High-Fidelity Design
The wireframes were brought to life with the full visual language of the Swipe Buy brand — colour, typography, spacing, imagery, and component states all applied consistently across every screen. Two key cognitive principles shaped the layout decisions throughout:
Hick's Law — Each screen is limited to a small number of actions to avoid overwhelming the user's decision-making
Fitts' Law — Core interactive elements are large, clearly labelled, and placed within easy reach of the thumb
The result is a high-fidelity prototype covering all primary flows — onboarding, swiping, saving, filtering, messaging, selling, and checkout — built entirely in Figma and ready for evaluation.
Information Architecture
The Information Architecture (IA) defines how all screens, features, and content are organised within the app — ensuring nothing is buried, every section is reachable intuitively, and the overall structure supports the user's mental model. The IA covers the full app from onboarding through to profile management, grouping screens into clear navigational clusters around the five main tab bar sections: Home, Browse, Sell, Liked Items, and Profile.
User Flows
Three key user flows were mapped to validate that each core journey was clear, efficient, and free of unnecessary steps:
1. Registering & Onboarding Flow
A new user moves from the welcome screen through account creation, the style quiz, and location and fit preferences — arriving at their first personalised product feed ready to swipe.
2. Swiping & Buying Flow
From the home feed, the user browses and swipes, taps into a seller's storefront, sends a message, manages their liked items list, proceeds to checkout, tracks their order, and optionally leaves a review.
3. Adding an Item Flow
A seller uploads photos, fills in item details (title, category, brand, size, condition, price), previews the listing, and publishes it for sale — with the option to save as a draft or remove the item at any point.
Design System
Colour Palette
Effects & Icons
Components
Typography
Spacing Rules
Heuristic Evaluation
With the high-fidelity prototype complete, a heuristic evaluation was conducted to identify usability issues before any user testing took place. This method was chosen because it is widely used in both industry and academia for rapid, structured expert review — catching problems early, when they are cheapest to fix.
Framework & Severity Scale
All four evaluators assessed the prototype against Nielsen's 10 Usability Heuristics, covering:
Visibility of system status
Match between system and the real world
User control and freedom
Consistency and standards
Error prevention
Recognition rather than recall
Flexibility and efficiency of use
Aesthetic and minimalist design
Help users recognise, diagnose, and recover from errors
Help and documentation
Key Findings
The overall usability baseline was strong, with many aspects consistently rated as 0 — no issue — across all four evaluators. However, three recurring problems emerged:
No Help & Support access — Users had no way to access help from within the app, violating the Help and Documentation heuristic
Forced onboarding — No option to skip the style quiz, potentially frustrating users who want to browse immediately
Unclear payment error states — A single generic error screen failed to communicate what had gone wrong or how to resolve it
Design Changes Made
Each finding was directly addressed in a revised iteration of the prototype:
Added a Help & Support button to the profile screen header
Added a "Skip this step" option on relevant onboarding screens
Created 3 distinct error screens tailored to the most common payment failure scenarios
Added required field indicators on the item listing form
Reflections & Next Steps
Swipe Buy successfully demonstrates that applying a human-centred design process from the very start — grounded in real research and refined through structured evaluation — produces a product that genuinely solves a user problem. The swipe mechanic proved to be far more than a novelty; validated by both academic literature and primary user research, it addresses a real emotional gap in the current thrift shopping experience.
The heuristic evaluation was a particularly valuable stage. Having four independent evaluators review the prototype surfaced issues that would have been easy to overlook — and catching them at this stage, before user testing, meant they could be resolved quickly and cheaply. It reinforced a key lesson: expert review and user testing are not interchangeable — they complement each other and both are necessary.
Short-Term Recommendations
Conduct moderated usability testing with real target users from the 18–30 demographic to validate that the expert findings reflect actual user behaviour
Implement and test the revised onboarding skip flow and payment error screens with participants
Carry out a full accessibility audit to ensure WCAG AA compliance across all screens and interaction states
Long-Term Recommendations
Integrate product analytics to track drop-off points, swipe-to-save conversion rates, and purchasing behaviour post-launch
Establish a continuous evaluation cycle combining expert reviews, moderated user testing, A/B testing, and data analysis to keep the product iterating as it scales
Explore the social and community features (Could Have requirements) that were intentionally deferred from the MVP — such as sharing liked items and following sellers — as natural next steps once the core experience is stable
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