Mobile prototype of the Spot M.D. experience. Prototype created in Figma for www.getsunday.com
Spot M.D.
Image Upload Tool for Brown Lawn Spots Diagnosis
This clever tool allows users with lawns to quickly diagnose what is causing pesky brown spots in their lawn. Using machine learning, this live tool is generating immediate value to users, while also encouraging them to purchase Lawn Plans from GetSunday.com
Who was this for?
This feature was designed and built for GetSunday.com, a company focused on bringing people back into the yard with subscription based programs for lawn and pests.
As a full-time Product Designer for Sunday, I collaborated with the following teams for this project:
Product
Marketing
Sunday Labs
Copywriting & Graphic Design
Engineering
Customer Success
Problem
Homeowners often struggle to identify the causes of brown spots on their lawns, leading to ineffective treatments and frustration. There is a need for an intuitive and user-friendly tool that can provide accurate diagnoses and solutions.
Goal
Design and develop Spot M.D., a desktop and mobile web application that allows users to upload images of brown spots on their lawns, receive accurate diagnoses, and obtain relevant solutions for each diagnosis.
Purpose
Empower homeowners to easily identify and address issues causing brown spots on their lawns, and to lead users to purchase a custom lawn plan from GetSunday.com
Deliverables
Desktop and mobile web application with image upload functionality and diagnostic results.
User journey that guides users seamlessly from image capture to diagnosis.
High-fidelity mockups and prototypes for user testing and final implementation.
Role & Responsibilities
My Roles: Lead Designer, Prototyper, UX Researcher
Conducting user research to understand pain points and needs.
Creating user personas and a detailed user journey map.
Designing initial concepts, wireframes, and low-fidelity prototypes.
Developing high-fidelity mockups and interactive prototypes.
Conducting usability testing to gather user feedback and refine designs.
Iterating based on usability test results and finalizing designs.
Collaborating with developers to ensure accurate implementation.
It started with identifying users’ painpoints
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"I just want to know WHY these brown spots on my lawn keep popping up."
Real GetSunday.com Subscriber
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"If there was a tool where I could take a picture and allows me to upload that and diagnose the issue -- that would really be great."
Real GetSunday.com Subscriber
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“If a tool can tell me how to fix the brown spots on my lawn, that would be really awesome.”
Real GetSunday.com Subscriber
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"What if I had multiple brownspots on my lawn -- would the tool diagnose different issues, or just one?"
Real GetSunday.com Subscriber
Initial User Research Summary
User research revealed that homeowners struggle with identifying lawn issues, leading to ineffective treatments and frustration. They desired a simple and quick solution that could provide accurate diagnoses and actionable steps.
I conducted 5 user interviews to understand GetSunday.com subscriber’s painpoints around brown spots in their lawns.
After understanding their issues and desires for an image upload tool, we were able to conceptualize a solution using a machine learning tool developed by the Sunday Labs team. We also know that over 80% of Sunday users access the website through their mobile device. Therefore a mobile first design was our focus.
With the initial functioning image upload tool working, and knowing the goals of the users, I was able to develop an an initial User Flow for Spot M.D.
Interview Tool: UserInterviews.com
Developing a simple User Flow helped to understanding what wire framing would need to be done to execute on the goals of the tool.
Initial Mobile Wireframes for Spot M.D.
Initial Desktop Wireframes for Spot M.D.
Initial Wireframing
After understanding the Personas, Goals of the users, and User Journey, I started to do initial wireframing (with Mobile First, of course!)
Used FPO (For Placement Only) Copy to understand the page structure. Our copywriter, Jenni Hanley provided copy for the tool after I had an idea for the content placement.
I worked closely with our Sunday Labs team to understand the capabilities of what the Machine Learning tool could accomplish.
After having initial mobile wireframes, I completed desktop wireframes as well.
These wireframes were iterated upon to ensure a seamless and intuitive experience for users.
Exploration design with For Placement Only copy
Mobile experience designed and prototyped
Desktop experience designed and prototyped Wireframes for Spot M.D.
Design
Once I had the initial Wireframes, I presented to my team of Engineers, Customer Specialists, Marketing Directors and Product Managers. This helped me refine the experience to meet not only the User’s goals, but also the Business goals to drive more users to GetSunday.com Custom Lawn Engine.
In the Design phase, I:
Added final copy from our Copywriter
Ensured users could diagnose photos from their phone, and upload images on the Desktop experience
Worked with a graphic designer to add custom vector art to the experience
Added branded color
Prototyped experience
Prototypes
Creating prototypes for both mobile and desktop on Figma not only gave clarity to the Engineering team of how to implement the designs, it also provided assets to the company as a whole to communicate the benefits of the feature before it was released.
Mobile prototype of the Spot M.D. experience. Shows user taking a photo, email capture, then brings them to the Diagnosis page — which leads to a funnel where users can purchase a lawn plan — After we’ve provided some upfront value to the user.
Desktop Spot M.D. experience prototype
Prototyping for multiple viewports
Creating prototypes in both mobile and desktop help expose the differences in the experiences.
In this case, the mobile and desktop experiences were different in a key way — The intended experience for the Spot M.D. tool is the mobile experience. This is because the user can quickly use their phone to take a picture of the brown spot on their lawn, and receive a diagnosis right where they’re standing.
For Desktop, the user must have the photo they intend to use already on their desktop. If they do have that, they can easily upload the picture for diagnosis. Otherwise, we encourage Desktop users to use the QR code to use the tool on their smart phone.
Usability testing and iterations
After the tool was live, I conduct further usability testing with potential customers. I interviewed 5 people who reviewed the experience.
Key findings:
It works!! The tool was capable of accurately diagnosing user’s brown spots on their lawns with all 5 users I interviewed.
Users were frustrated with having to provide an email address, mainly due to previously known issues with Sunday’s user login experience involving a magic-link to reset their passwords.
There were no notes around the UX or UI of the experience. However, 40% of users interviewed said that the “What you can do” was not that helpful. This was noted for the Sunday Labs and Copywriting teams to provide further value to customers with more actionable tips for users.
Engineering pushes to go-live.
Now what?
QA:
After rigorous QA (Quality Assurance) by myself and the Project Manager (Dakota-Rae Westveer) , we make suggestions for minor fixes for the developers. The feature then went from the GE environment to the live website.
Communication to Sunday Teams:
Once the tool was live, the use-cases and goals of the tool were reviewed by the Marketing team. They then conducted a marketing campaign to do a “soft launch” of the tool through an email link, offering value to our customers and potential customers while guiding them to shop further at GetSunday.com.
The CTA for the Spot M.D. tool is to push users to the Custom Lawn Page, shown above.
What I Learned / Key Takeaways
Empathy-Driven Design: This project reinforced the importance of deeply understanding users' needs and pain points. By conducting user interviews and synthesizing feedback, I learned how to prioritize solutions that address real-world challenges faced by medical professionals and patients alike.
Designing for Accessibility and Inclusivity: Spot MD required creating user experiences that were not only functional but also accessible for diverse audiences. This experience deepened my commitment to ensuring designs meet WCAG standards and prioritize usability for all users, regardless of ability.
Balancing Complexity with Usability: Designing for the medical field meant handling intricate workflows and large datasets while delivering a clear, intuitive interface. I honed my ability to simplify complex processes into straightforward user journeys without sacrificing functionality.
Product Successes: While Spot MD was only recently released to the public, early results show encouraging conversion rates. With the primary goal of guiding users to shop our custom lawn plan, 27% of users who used the tool clicked on the desired call-to-action (CTA). Beyond driving potential customers into the lawn funnel, the tool added significant value to users through branded features, marking a measurable product success.
Effective Collaboration Across Disciplines: Partnering with engineers, product managers, and stakeholders, I enhanced my skills in communicating design rationale and gathering input from cross-functional teams to ensure alignment and deliver a cohesive product.
Iterative Problem-Solving: Through usability testing and iterative design improvements, I saw the value of continually refining features based on user feedback to create a more seamless and satisfying experience.
What’s next?
This was phase 1 of our image upload tools for Sunday customers. Not only will we tweak this tool to meet demands for our business needs, but Sunday is also developing other image upload tools, such as Grass ID and Weed ID. These tools will provide value to customers and prospective customers, while guiding them to shop with GetSunday.com.
Phase 2 of the Spot M.D. Tool:
For users that already have a custom Lawn Plan through GetSunday.com, recommend specific products to them based on their user data, and through the diagnosis of the tool.
For example, if a user has a Lawn Plan already, and is diagnosed with “Drought” as their issue, recommend our “Heat Defense” lawn product to protect against drought conditions.
Integrate other image upload tools, and potential rebrand all diagnosis tools as something else.
Further user testing and iterations.