Acloset
UI/UX App Feature Expansion
Inspiration & Objective：
Enhancing Acloset with a 'Search with Camera' Feature
For this 'Add Feature' project, I chose to enhance Acloset, a wardrobe management app celebrated for its AI-driven capabilities, by proposing a 'Search with Camera' feature. The project stemmed from two weeks of intensive app usage, allowing me to fully engage with its functionalities and identify key areas ripe for innovation. The primary goal was to simplify how users could add items to their wardrobe using camera technology, thus enriching the user experience with an intuitive and innovative solution."
Feature Walkthrough: Using 'Search with Camera' in Acloset
Add Clothing
Search: Find Similar Styles
Edit & Save
Design Process Overview
The development of Acloset's 'Search with Camera' feature followed a streamlined three-step design process, focusing on creating a user-centered enhancement.
1. Research
Gathering insights through surveys and interviews to understand user needs.
2. Design
Creating initial sketches and digital prototypes with design tools like Figma.
3. Interaction Flow
Testing prototypes with users to refine usability and finalize the design.
Research
Market Research: Comparative Analysis
I conducted market research by comparing features and user experiences of Gladwell, Purple, and Whering apps. This analysis helped identify unique opportunities for Acloset to enhance its wardrobe management capabilities.
The analysis highlighted the need for enhanced usability, personalization, and sustainability in wardrobe management apps.
User-Friendly Interface: Essential for easy navigation and engagement.
Flexible Payment Options: Cater to diverse user preferences.
AI for Personalized Recommendations: Enhance value with tailored suggestions.
Social Sharing: Encourage interaction through outfit or purchase sharing.
Simplified Upload Process: Streamline adding clothes to the app.
Market Differentiation: Identify unique selling points for competitive advantage.
Key takeaways include:
Opportunities for Acloset:
Personalized Style Quiz: Discover users' fashion preferences for tailored recommendations.
Virtual Try-On: Premium feature for a digital fitting experience, linking to the metaverse.
Wear Count Tracker: Promote sustainable fashion by tracking clothing usage.
User Interview
For Acloset's feature enhancement, user interviews were conducted to gather insights on user interactions and preferences with wardrobe management apps, focusing on identifying pain points and desired features.
Survey findings revealed a strong user interest in a "visual try-on" feature, yet highlighted challenges in uploading clothing images. Consequently, prioritization was given to enhancing the image recognition feature based on user feedback and internal discussions.
Survey Insights
Affinity Mapping
From user research to affinity mapping, we learned users pick outfits differently and find photo uploads challenging. This insight is guiding us to focus on image recognition, simplifying how users add clothes, and ensuring our app caters to everyone's needs more effectively.
User Persona
After synthesizing insights from user research and affinity mapping, we crafted personas to represent our app's diverse user base. These personas help us tailor the development of features like image recognition, ensuring the app meets the varied needs and preferences of our users effectively.
Feature Prioritization
Using personas shaped from user research and affinity mapping, I set feature priorities: image recognition is essential and comes first, visual try-on is next as a nice-to-have, and the recommendation to buy feature can follow later, ensuring a strategic, user-driven development path.
Must have
Image Recognition Feature：
Increases the efficiency of image uploading for users.
Reduces chances of users abandoning the upload due to dissatisfaction with their captured image.
Automated image recognition ensures a more professional and consistent user experience.
The image recognition function lays a solid foundation for the upcoming "visual try-on" feature by providing clearer, more professional images for a more realistic try-on effect.
Nice to have
The visual try-on feature allows users to virtually try on outfits on a digital platform, offering an experience that closely mirrors real-life fitting, thus simplifying and enhancing the decision-making process. Concurrently, image recognition technology underpins this process, automatically identifying and categorizing uploaded clothing images, ensuring the virtual try-on effect is more authentic and accurate, thereby elevating the user experience.
Visual try-on Feature：
Can come later
The "Recommendation to Buy" feature offers personalized clothing suggestions by deeply analyzing the contents of a user's closet and their fashion preferences, coupled with current fashion trends and inventory from partnered shopping platforms. Not only does this elevate the user's shopping experience by catering to their evolving fashion needs, but it also presents a potential revenue stream for Acloset and strengthens its collaboration with shopping platforms.
Recommendation to Buy feature：
Design
User Flow
Transitioning from the groundwork laid by user research, affinity mapping, and feature prioritization, I next focused on the design phase, starting with the User Flow. This step is crucial for visualizing how users will interact with the new image recognition feature, guiding them seamlessly from discovery to action within the app.
Low Fidelity Wireframe
Following the user flow outline, I moved on to developing Low Fidelity Wireframes. These initial sketches provided a visual representation of the app's layout and the image recognition feature, allowing for early testing of concepts and the overall user journey.
High Fidelity Wireframe
After refining the Low Fidelity Wireframes, I progressed to creating High Fidelity Wireframes. This phase involved adding detailed design elements, such as color schemes and typography, to bring the user interface closer to the final product, ensuring a more accurate representation of the user experience.
So, the focus of this feature update is the integration of a "Search with Camera" functionality into the existing clothes-finding feature, allowing users to quickly find similar or identical clothes online by taking a photo.
Interactive Flow
Usability Testing
With the High Fidelity Wireframes in place, I transitioned to defining the Interaction Flow and planning for Usability Testing.
Next up, I focused on the interactive parts of the design, like how things move on the screen and what happens when you tap buttons. Then, it was time to see what real users thought about it all, to make sure everything worked smoothly and made sense to them.
The Acloset usability test focuses on using Maze to assess how easily users can navigate to and utilize the "search with camera" feature to add and save clothing items, gathering feedback to refine the app experience.
Testing Results
User testing of Acloset's "search with camera" feature resulted in positive feedback, with an 83.3% success rate in task completion, and an average user rating of 9.2.
Participants: 6 users tested, representing a mix of Acloset familiar and new users.
Task 1: Some users encountered initial navigation challenges despite guidance arrows.
Task 2: Majority of users successfully utilized the "search with camera" feature, with minor issues reported by one participant during image selection.
User Feedback: Users praised the helpfulness of the camera feature and the app's overall user-friendly interface.
Key Points:
Overall, the testing suggests successful implementation and positive user reception of the "search with camera" feature, enhancing the efficiency of wardrobe management in Acloset.
Reflection
Reflecting on the process of adding a feature to Acloset, I learned the importance of flexibility and responsiveness to user feedback, prioritizing improvements in photo uploading over a visual try-on feature, ultimately leading to a more user-centered product and personal growth as a UX designer.