Outfit of the day
OOTD (Outfit of the Day) is a mobile app designed to help users, especially teenagers make confident, stress-free outfit choices for everyday wear or special occasions. The app offers personalized recommendations based on user preferences, current trends, and contextual factors like weather or event type. This project was part of an Interaction Design course at RIT, where we applied a user-centered approach, including contextual inquiry, to understand real behaviors and design a meaningful solution.
My role
UX Designer (Part of a course in RIT)
2 months
Rochester, New York
Fashion
Information and Interaction Design
Challenge
Choosing what to wear can be a daily source of stress for teenagers. With fast-changing fashion trends and growing social pressure to “look right,” many feel overwhelmed by the process. OOTD aims to simplify outfit selection through personalized, trend-aware recommendations that make getting dressed easier, faster, and more fun.
Results
The OOTD prototype received enthusiastic feedback from teens, with users appreciating its simplicity, trend alignment, and playful tone.
85%
of users said the app helped reduce decision fatigue during outfit selection
5/5
Average satisfaction score in a follow-up survey post usability testing
95%
Users preferred OOTD over scrolling through Instagram or Pinterest for outfit ideas
Project Focus
A study suggests that women spend 17 minutes a day debating what outfit to wear. Whereas, men spend up to 13 minutes rifling through their wardrobes. This equates to four days a year for women and three days for men wasted. Also color sets up the mood for an individual one for a particular day. So clothing actually impacts how well a given day will be.
Stakeholders
Fashion Designers
Retail Clothing Stores
Consumers
Approach
Before beginning user research, we conducted a background study on global color and outfit trends. We explored platforms like Pinterest, Instagram, and selected fashion websites to understand what styles people are currently drawn to.
For our initial research, we focused on RIT students to learn how they choose their outfits and combine colors. Using contextual inquiry and interviews, we gathered insights into their daily dressing habits and decision-making.
We synthesized the data using an affinity diagram to uncover common behaviors and patterns. These findings helped us create a representative persona and refine our problem statement to better address user needs and motivations.
Building on these insights, we brainstormed ideas and created an interactive prototype to validate our assumptions and reflect on the effectiveness of our design process.
Target Audience
For the first phase, our target audience would be college students around Rochester.
Further, we will expand our target audience to teenagers from different demography
Contextual Inquiry
For this phase, we spoke with 8 participants through one-on-one contextual interviews. We first spent time crafting the right questions and finding people who could share honest, everyday experiences around choosing outfits. These casual but focused conversations gave us a deeper look into the habits, frustrations, and thought processes behind what people wear and why.
Affinity Mapping
We mapped out our interview insights as digital post-it notes in Miro. These were first grouped into local affinities based on common themes, and then organized into a high-level affinity diagram with three structured layers to reveal broader patterns and connections.
User Personas
Based on insights from the contextual inquiry, we identified four distinct user groups and crafted personas to represent each of them.
By digging into their motivations and frustrations with current outfit-planning tools, we uncovered key opportunities to design more personalized, user-centered experiences.
Jane Rosy, Primary Persona
"I waste a lot of time in selecting clothes, I wish there was a tool that does this for me."
Jack, Secondary Persona 1
"I need a way to get feedback for my choices of clothes so that I will be more confident about what I’m wearing."
Kim, Secondary Persona 2
"I want to be more organized with my wardrobe arrangement and want to be better planned on what I'm going to wear for an event"
Charles, Anti Persona
"I do not find it productive to spend so much time selecting clothes, I wear whatever is clean in my wardrobe."
Selection of Platform
All participants shared that they prefer a mobile-first solution for outfit recommendations, and their reasoning was clear. The decision-making process usually begins right after a shower, standing in front of the wardrobe, when their phone is the closest and most convenient tool at hand. Given this behavior, we chose to design a mobile-first application that fits seamlessly into their daily routine.
Designs
User Flows
The user flow is divided into two core sections:
Onboarding Flow
Guides new users through setting preferences, answering key style questions, and setting up their profile for personalized recommendations.Home Screen Flow
The home screen branches into three key features:Recommendation Screen
Offers daily outfit suggestions with customizable color and style options.Community Screen
A social space where users can post their outfits, view friends’ styles, and give or receive feedback.Profile Screen
Displays usage insights like frequently worn colors, outfit trends, and saved looks for quick access.
Conclusion
Future Directions
As we explored user needs and behaviors, several opportunities for future enhancement emerged -
Smart Wardrobe Integration:
Introducing a smart device or sensor within wardrobes could allow automatic detection and syncing of available clothing items with the app. This would remove the need for users to manually upload or tag their outfits, making the experience more seamless and accurate.Retail Inventory-Driven Suggestions:
From a stakeholder and business perspective, integrating live retail inventories would allow the app to suggest outfits based on new arrivals or trending styles in stores. This could also be paired with virtual try-ons or previews to educate users on how different styles might look on them.Social Media Connectivity:
Enabling integration with platforms like Instagram or Snapchat would allow users to share their looks, follow trends, and build community engagement. It would also give the app insight into broader fashion preferences and evolving styles, making recommendations even smarter.
Reflections from This Project
This project gave us hands-on experience with a full user-centered design cycle, and several key learnings stood out:
Contextual Inquiry in Action:
We learned how to observe and engage users in their real environments, which helped us uncover insights we wouldn’t have found through surveys alone. It taught us how powerful it is to listen, observe, and interpret everyday routines.Affinity Mapping for Synthesis:
Organizing large volumes of data into structured affinity diagrams helped us clearly identify patterns and relationships. It made the process of finding insights feel more collaborative and manageable.Prototyping for Validation:
Building both low- and high-fidelity prototypes helped us iterate quickly and test our assumptions. It gave us a deeper understanding of how users interact with visual design and flow and how small tweaks can make a big impact.Collaborative Teamwork:
Working together remotely and using collaborative tools like Miro and Figma helped us stay aligned. We learned how to divide tasks effectively, give and receive constructive feedback, and create a unified product vision.