UniStyle

An accessible multi-touchpoint service that provides a convenient and affordable way for students to find affordable clothing, become more confident, and foster social interaction. Developed in collaboration with other UX designers.

Timeframe: 9 weeks

Tools used: Figma, Voiceflow, Airtable

Situation: As students begin living independently and joining the workforce, they require access to affordable clothing and a way to build social and professional confidence. This particularly impacts students with disabilities, as they face additional barriers to employment.

Concept: UniStyle is a hybrid physical and digital service designed to help students with their social confidence and wardrobe. It includes an online forum dedicated to styling and fashion advice, a clothing sales platform with both digital and in-person touchpoints, and an AI-powered conversational agent that serves as a styling assistant.

Research and Ideation

After several brainstorming and feedback sessions, we decided on the concept of an on-campus secondhand clothing donation point with a companion website, where students could reserve clothing to be picked up and participant in an online forum. This concept allowed for synergy between physical and digital touchpoints. It also met our criteria of being convenient, affordable and socially engaging, which addressed the needs of our student personas.

Brainstorming notes for our chosen service concept, including notes on accessibility.

Prototyping

To fully visualise the scope of our service, we first developed two storyboards depicting how our personas would interact with the service to address their needs.

Storyboard illustrating how our persona Ashwin uses the service to obtain secondhand clothing suitable for an upcoming job interview.

Storyboard illustrating how our persona Leila uses the service to donate unused clothing and improve her social confidence.

To test the in-person component of our service, we used Lego blocks to create a model of the on-campus clothing donation point and conducted a walkthrough of the service, with participants playing the role of potential users. Participants were briefed on our user personas and disabilities they had in order to test the service's accessibility. These walkthroughs provided us with valuable insights on how to operate the clothing exchange model. We initially used a points-based purchasing system to limit the amount of clothing students could receive in a week. However, after receiving feedback, we changed to a simpler currency-based system that would provide more of an incentive to donate clothing to the service.

We also developed a customer journey map in tandem with our roleplaying exercises, which provided a clear overview of our design and highlighted areas of improvement in the customer experience.

I created several iterations of wireframes for UniStyle's companion website using Figma, making changes in response to user feedback. The A11y toolkit was used to annotate accessibility features, and a mobile-first philosophy design allowed us to scale up for desktop screens.

Low-fidelity wireframe of the companion site with accessibility features annotated.

Initially, we received user feedback that the site navigation was unintuitive. To address this, several sections of the website were renamed, such as the "Community" section being changed to "Forum" to align with external mental models. The layout was also optimised for mobile navigation.

Conversational Design

We used Voiceflow to create an AI-powered conversational agent for our service. It was designed to act as a "styling assistant" for students, capable of generating a personalised lookbook and recommending items based on user preferences. We conducted a survey to determine what factors users would value when finding clothing that suits their needs (such as gender presentation and colour). The survey also asked about participant expectations when interacting with conversational agents. Overall, respondents preferred conversational agents that provided instant and accurate responses that eliminated the need for human interaction.

A sample user flow for the conversational agent.

Connecting the conversational agent to an Airtable database via API calls allowed us to store user preferences and quickly search for suitable clothing.