Overview
OOTD (Outfit Of The Day) is a one-stop-shop mobile application for providing personalized recommendations for colors and outfit styles to wear for regular day-to-day use or for special occasions.
Problem Statement
"Picking out outfits can be an overwhelming task for teenagers on a daily basis, the trends keep changing and it can be difficult for them to keep up."​​​​​​​
Course
Information and Interaction Design
Tools Used
Figma
Miro
Otter.io
Team
4 UX Designers
My Responsibility
Conducting Contextual Inquiries
Creating Personas
Low and High Fidelity Prototyping
Milestone 1: Project Focus
Timeline: Sep 5, 2021, to Sep 9, 2021
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
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Fashion Designers
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Retail Clothing Stores
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Consumers
Approach
1. Before getting into the actual research phase, we decided to set up the team principle and guidelines. Then we are planning to do an extensive background study on the colors trends that people are following around the globe. For that, we are using Pinterest, Instagram, and a limited set of popular clothing websites.
2. In the initial user research, we would like to start with the students of RIT to understand how they pick their outfits and the dress color combinations they are familiar with through contextual inquiry. From the interviews, we plan to obtain categorized information on users' shopping and dressing behaviors by drawing out the affinity diagram.
3. Through the affinity diagram, we draw similar behaviors and characteristics to create a persona and with that, we decided to refine our problem statement to address the needs, goals, and points for the persona.
4. We also plan to brainstorm various ideas and design an interactive prototype to validate our assumptions and self-evaluate the process we took.
Target Audience
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For the first phase, our target audience would be college students around Rochester.
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Further, we will expand our target audience to teenagers from different demography
Milestone 2: Contextual Inquiry
Timeline: Sep 20, 2021 to Oct 01, 2021
In this milestone, we did contextual inquiry with a total of 8 participants: 8 individual interviews. The process started with a discussion about preparing the questionnaire and identifying the participant lists. Then we scheduled interviews with the participants where we asked them our questions.​
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We interpreted the interviews into post-it notes using Miro. We grouped them based on various factors into local affinities and eventually translated them into a high-affinity diagram with 3 levels.
Local Affinity
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Affinity Diagram
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Interpretation
We gained a lot of insights from the interviews, the group discussion, and the interpretation session. The interviews brought up many new user pain points which helped us find out ways to improve the overall user experience like asking questions to the user to gain data and provide a more personalized experience to the user. We might have overlooked some aspects that affect the selection of outfits like the weather which was the most common aspect for the participants. Some insights varied greatly among the participants. For example, purchasing pattern: some participants prioritized brand, some prioritized design, for some it was the color, while some prioritized price/budget.
Milestone 3: Personas
Timeline: Sep 20, 2021 to Oct 01, 2021
Based on the results of the contextual inquiry, we identified four main groups of users and created a persona to reflect each of their personalities.
Understanding the user’s motivations and their problems with their existing apps helped us identify opportunities and create user-centered designs.
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"I waste a lot of time in selecting clothes, I wish there was a tool that does this for me."
Jane Doe, Primary Persona
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"I need a way to get feedback for my choices of clothes so that I will be more confident about what I’m wearing."
Jack, Secondary Persona 1
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"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"
D. Kim, Secondary Persona 2
"I do not find it productive to spend so much time selecting clothes, I wear whatever is clean in my wardrobe."
Charles, Anti Persona
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Selection of Platform
All of the participants that we interviewed said that they prefer a mobile-first application for recommending outfits based on the following reasons,
The decision-making process for selecting an outfit starts when they come out after a bath in front of their wardrobe.
In such conditions, the nearest and easiest platform accessible to use is their smartphone.
Hence, we decided to design a mobile-first application instead of any other platform.
Milestone 4: Low-fidelity prototype
Timeline: Oct 23, 2021, to Oct 29, 2021
User Flows
The user flow is divided into 2 main parts :
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Onboarding user flows: Contains the onboarding process of the user.
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Home Screen user flow: The home screen user flow offers 3 main functionalities,
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Recommendation Screen: Displays the color and outfit options with customization features.​
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Community Screen: Provides socializing option where users can see their friend's outfits and post their outfits as well as to give and receive feedback on their posted outfits.
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Profile Screen: Shows statistical analysis of the most worn colors and outfits.
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Low Fidelity Prototype
Milestone 5: High-fidelity prototype
Timeline: Nov 15, 2021 to Nov 29, 2021
Interactive Prototype
Prototype Recording
Future Directions
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We can add a smart device to the wardrobe which will connect to the app so that the users do not have to manually add their outfits.
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From a stakeholder perspective, the outfit suggestion could be based on the new models that arrived in the store and educate the user on how the style will look on them.
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Also, integration with social media platforms could be a game-changer as it can help the community to understand the clothing trends better and make better choices.
Reflections from this project
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We learned how to conduct a contextual inquiry.
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Creating affinity diagrams to find relationships between concepts and ideas from a large number of data.
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Using prototyping tools effectively to create low-fidelity and high-fidelity prototypes.
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Working in a team effectively using team collaboration features on various platforms.