LaundroMate

Prototype link (Please submit a link to a playable prototype, not a link to your design file) Link
Describe your project (max 150 words)

Doing laundry in college can be a frustrating game of chance and patience. It's all about timing your visit to the laundry room perfectly, praying for an available machine that cleans well, and then keeping an eagle eye on your clothes to prevent them from being moved to a dirty container. This is where LaundroMate comes in. It streamlines and centralizes the laundry process with real-time machine availability, load progress tracking, maintenance updates, informational hub with AI chatbot-integration, and reminder notifications. Wondering the most optimal time for laundry or forgot to transfer your load? LaundroMate's gentle reminders will ensure you're on top of your laundry game. With LaundroMate, you're not just washing clothes; you're joining a network of students who support and respect each other's time and laundry needs.
Describe your research process and findings. If you conducted any surveys or interviews, please include the survey form and/or interview questions here. If you conducted secondary research by pulling from online sources, please include a link to your sources. (Max 500 words)

To ensure we had an in-depth understanding of our problem space, we combined a variety of general and specific research methods including a user survey, user interviews, secondary research, and competitive analysis. We surveyed 109 undergraduates who live at different US college campuses, prioritizing quantitative data about general laundry issues and their extent. Some notable questions included the following:

-When you first came to college, did you know how to do your laundry? -If applicable, how effective is your current method of communicating with other laundry users?

We also included opportunities to measure qualitative data, providing optional short answer questions for respondents to explain their answer choices. From this survey, a key insight we learned was 10% of respondents didn’t know how to do laundry before college. Specifically, one user noted “every dorm’s machines work differently…which you don’t know until you’ve used it and find out from experience”. To address this finding, we built a chatbot that serves as a personalized, expedited resource for users of all experience levels. We explained more findings and the corresponding design choice they informed in the next section and our demo. Shifting towards general qualitative data, we conducted 6 user interviews with students at different universities and a stakeholder interview with a student maintenance representative (SMR), as we wanted to apply a more specific lens to our research methods, hoping to learn more about maintenance. This stakeholder interview was highly relevant to our user group. SMRs represent a unique position, as they’re a student but also work closely with maintenance staff to communicate utility updates to students, emphasizing their relevance to our user group. Some important questions we asked during these interviews include the following:

-What helps you keep track of the status of your laundry? -What do you like about the current way laundry maintenance is handled?

Furthermore, we conducted secondary research of university articles and concluded our research with a specific research lens on a competitive analysis of current college laundry apps. Across these methods, we noted multiple common findings. First, users often expressed worry about strangers touching their clothes or placing them in dirty storage. A more specific finding we identified, from secondary research and SMR interviews, was that machines often break due to misuse, especially by students who try to reduce costs by overloading machines (Dehmel). After solidifying a well-rounded knowledge of our problem space, we concluded our research with a specific, narrow method of competitive analysis. Many interviewees at other universities shared their experiences using current college laundry apps such as Speed Queen and Wash Connect Campus. These users enjoyed the simple interface and the convenience of real-time updates on available machines. However, we noticed these apps didn’t cater towards the unique diversity of college populations as they lacked accessible alternative options. Taking advantage of these usability gaps in existing apps and leveraging our robust research on college laundry, we integrated these insights into our designs, creating an accessible product that satisfies previously unfulfilled needs.

Survey: https://docs.google.com/forms/d/1c3egrh705iKA5DvMg393xFqgMDpSTPgC96vJF_Gigmg/viewanalytics Interviews: https://docs.google.com/document/d/1IXQMmLqUVsSmLF5dqG0d9K62d6e-8W0BJvGE-_-QkFA/edit?usp=sharing

Describe your most important design decisions. What research findings and/or user testing results led you to make these decisions? (Max 500 words)

After collecting 109 survey responses, conducting 6 interviews, and reading detailed research of laundry experiences at other universities, we synthesized our user research into core insights that informed our design. Our integration of user insights into our design resulted in a successful high-fidelity prototype with a System Usability Score (SUS) of 88.2, demonstrating our design is highly usable by industry standards. One of the core insights that heavily drove our design was users’ desire for a more effective communication method on laundry statuses. Notably, 45% of survey respondents stated that their current method of laundry communication was inefficient. Other frequent pain points included frustrations towards other users who failed to communicate moving others’ clothes and uncomfortable feelings when using non-anonymous communication. Since this problem space was the most common concern, we focused on creating features that would mitigate this pain point. While our initial ideations included a chat feature, after analysis of our findings, we concluded a chat would perpetuate current communication issues. Instead, we implemented automated, anonymous notifications for when a user moves someone else’s load. This feature is highly innovative; current college laundry apps don’t include similar features addressing this pain point. Thus, our app successfully satisfies a previously unfulfilled user need. Based on our quantitative research, we identified machine availability as another crucial frustration users expressed. Specifically, we found that about 40% of survey respondents do not often have available machines. Other stakeholders such as student representatives that work with campus utility departments connected this to improper usage of machines, which further exacerbate this issue and put significant burdens on these student representatives. A dorm senator from Dartmouth emphasized that “the faulty washers and dryers pose issues to his senatorial duties…one resident called him to complain about spending $20 on laundry, after a dryer failed to dry her clothing after four cycles.” (Dehmel). With these major pain points in mind, we implemented features that resolve these issues and cater towards the unique diversity of college populations. First, we implemented a live view of the laundry room displaying the availability of machines through an interactive visual map and a detailed numerical list view. Users may toggle between these views, choosing the representation that satisfies their needs. Importantly, we integrated other accessible and inclusive features such as the ability to customize the timing of notifications to fit personal needs (e.g receiving a notification 5 minutes before laundry is done), a dark mode and light mode toggle, and readable and highly visible text that complies with WCAG, emphasizing LaundroMate’s high accessibility. This further demonstrates LaundroMate’s innovation as many other current laundry apps lack accessible designs, a crucial insight to incorporate when designing for vastly diverse college populations. We also prioritized a simple user interface, as many users enjoyed this design on current laundry apps. We carefully built each of LaundroMate’s features based on our extensive user research, integrating innovative features that address previously unfulfilled user needs of automated, improved communication and accessible, highly inclusive features.