REWARE, Clothing Sustainability App — Encouraging Sustainable Behaviors


BACKGROUND Shortened lifecycles of apparel create enormous textile waste. Clothing waste is an environmental crisis that impacts us all — fast fashion companies being one of the largest contributors to that waste. Clothing from these companies often fall short in quality which shortens the lifecycle of apparel.

Unwanted clothing is typically discarded instead of repurposed or recycled.

Academic project, 16 Weeks (August 2021 - December 2021)
THE PROJECT How might we promote forming sustainable clothing habits? Working with a team of 11 students varying in different skillsets that range from programming to designing, we designed an application that helps limit clothing waste contribution by promoting increased clothing lifecycles and rewarding personal sustainable accomplishments. By tackling the problem this way, it encourages users to utilize their current wardrobe which indirectly affects shopping and clothing consumption.

Team: A mix of ux design, graphic design, and engineering students

Tools: Figma, Miro, Canva

Theoretical research

CURRENT STATE An estimated 13 million tons of waste generated was from clothing and footwear. - Environmental Protection Agency (EPA), 2018 Statistic
COMPETITIVE/COMPARATIVE ANALYSIS Direct competitors Our direct competitors included those that are currently working towards reducing small scale environmental impacts in their own ways.

Weaknesses of competitors stemmed from the inability to incentivize users to start changing behaviors.
COMPETITIVE/COMPARATIVE ANALYSIS Indirect competitors Our indirect competitors focused on gamification and data visualization. Apps like Forest and Duolingo excelled in gamification while built in applications on the iPhone like the Health app and Screen Time were great examples of visualization.
PSYCHOLOGY - POSITIVE BEHAVIOR REINFORCEMENT Motivation vs. Gamification In order to create anything that revolved around changing people’s behavior, we needed to understand why people do things and specifically how they form habits. This realization steered our research into human motivation and ways of influencing a person’s behavior.

People most of the time don't want to do things when they are told to, especially when it involves a boring task. In order to find ways and techniques to make people engage more with everyday tasks, we focused on researching gamification.

Research methods

TARGET AUDIENCE Who are we targeting? Our target audience is young adults between the ages of 17 and 22 years old who want to reduce their clothing waste, but have low disposable income. This requires them to choose to buy a lower quantity OR a lower quality. The ability to buy more for less and tendency to follow trends makes them an easy target for fast fashion companies.
METHOD #1 Archetypes Brainstorming archetypes for this project allowed us to continue to understand who we would be focusing on and designing for. During this process we took into consideration what we already know, and often took inspiration from those in the class. This lead us to create four archetypes based on varying levels of knowledge towards being environmentally friendly; the advocate, busy-bee, unaware, and misinformed. We focused on the advocate and busy-bee specifically, using them as our model participant for interviews.
METHOD #2 Surveys We were fortunate enough to reach 18 participants through the use of many online platforms as well as knowing people who fit the demographic and characteristics of our target audience. Among the responses we received on our survey, some of the key insights included:
METHOD #3 Interview: SME We learned a different perspective when talking to Idacavage, a pundit on fashion history and material culture, that fast fashion is not a concept many are familiar with or understands. That some may even hold fashion at different priority levels or have completely different motivations.
METHOD #4 Interview: Target audience & Affinity diagram We interviewed a total of 15 individuals ages 18 to 24 asking them various questions broken up by demographics, behavior, motivation, societal, and sustainability topics. From the answers, we were able to gather important insights that influenced the ReWare design.

Highlighted data gathered:
  • High interest/involvement with secondhand shopping
  • Large social impact for fashion inspiration
  • Donating clothes was the most popular method of clothing disposal followed by up-cycling

Solution thinking

PROBLEM STATEMENT Young consumers have a basic understanding of sustainability, but not enough to create impactful changes.
METHOD #1 Brainstorming Brainstorming lead us to a breakdown of three categories; social/informative, gaming, and a virtual closet. We took close consideration into what was already out there, as well as wants, needs, and suggestions based off of responses from our interviews.

We took bits and pieces of each category to bring into our final solution; chalking it up to an additional three categories of gamification, machine learning, and social or private sharing. These were the three categories that we would ultimately focus on.
JOBS-TO-BE-DONE Empowering users to make informed, sustainable choices through a sense of personal accomplishment.
OUR DESIGN APPROACH REWARE, Clothing sustainability app We decided to turn our attention to what an individual already owns in their closet. By learning how to utilize what's currently in your closet, it would inadvertently affect shopping and clothing consumption habits.

Our solution flourished into ReWare, a way for users to track and visualize what outfits they wear over any amount of time. Users document their outfits via photos, whilst a form of machine learning will tell them information based on their what they wear.

App features

FEATURE #1 Gamification Users may choose to participate in daily, weekly, biweekly, or monthly challenges that all pertain to utilizing their closet. By completing these tasks, users may be rewarded with achievements, badges, or points to redeem in a form of virtual shop.
  • A personal customizable avatar, reward system, and form of challenges/tasks are just some of the gamification features that can be included in the application.
FEATURE #2 Machine learning ReWare strives to be a fun way to document your outfits, while simultaneously learning about your own clothing consumption habits.
  • Manually inputting information deemed to be too daunting and tedious, but an AI algorithm that feeds off of information the user provides is a much more digestible task.
  • Machine learning will help alleviate the stress of inputting copious amounts of information.
  • A powerful tool to help users understand the scope of their impact.
FEATURE #3 Social sharing A key component to this app will be some form of photo sharing. From our user interviews we found that many take inspiration from social media, whether it be friends, celebrities, or fashion pages.
  • By allowing users to post their photos to some sort of feed, they can quickly visualize information showing what they are wearing frequently, as well as gather inspiration from friends.
FEATURE #4 Private logging Photos will automatically be uploaded to a private feed, in which users will have quick access to all the outfits they have uploaded to the app. From there, they can choose to make certain outfits public for everyone to see, and encourage others to gain inspiration from one another.


WHAT I LEARNED... ‍Extending my stay in the problem space ‍With the topic of sustainability pertaining to clothing waste being so dense, the importance of “not leaving” the problem space until you can confidently grasp the subject matter is key. Eagerly, but prematurely moving into the solution phase can actually do more harm than good.
‍Editing out ‍There were so many ways we could have approached mitigating personal clothing waste. So many of the earlier concept iterations became products that tried to do too much. Acknowledging and learning when to appropriately edit out features made for simple, yet purposeful experiences.
NEXT PROJECT: ‍Costco Data Reports