Rescue Me
Role
Research, UX Design, UI Design
Date
May 2018
Client
Proof of Concept
Delivered
Research Artifacts, UX Design, UI Design
Project Overview
In my spare time I foster and fospice, foster hospice, dogs for animal rescue organizations in New York City. Hundreds of rescue dogs are looking for forever homes across the city. I spent some time investigating how to better match up rescue dogs with potential adopters. Here was my goal:
Design an experience that will help connect people looking for a new pet with the right companion for them.
Help an adopter find a pet which matches their lifestyle, considering factors including breed, gender, age, temperament, and health status.
Simplify the process of finding and adopting a dog.
Description
Research
I'm a rescue foster mom and recently looked at adopting a rescue puppy so I had a few assumptions and questions I needed to validate going into the project. I identified 5 individuals who had either been through the adoption process or are going through it now to interview. I also identified the stakeholders as those individuals who volunteer for and run rescues, I interviewed 3 stakeholders.
Rescue Volunteer (Stakeholder) Questions:
What do you look for in adoption applications?
How do you find and match dogs with adopters?
Why would you ever reject an adopter?
How is finding a foster home different then finding a permanent adopter?
Adopter (End User) Questions:
What was the process for adopting your dog?
How did you find the place where you wanted to adopt?
What factors were important to you as you researched?
What tools did you use?
What was the application process like?
How many dogs did you apply for before you successfully adopted?
How much time did you spend looking for a dog?
High Level Takeaways Adopters:
Tools: The majority of adopters use Petfinder and Google to find adoptable dogs.
Constraints: Adopters may have inflexible constraints such as size, activity level, etc.
Competition: Adopting dogs is competitive in NYC. Many adopters have similar constraints.
Trust: Adopters need to trust the rescue they are adopting from. Transparency is important.
Application: The adoption process is time consuming. There is a separate application for every rescue.
Matchmaker: New adopters need help finding a dog, using services or the rescue to provide dog matchmaking.
High Level Takeaways Rescue Volunteers:
Application: Volunteers need to review separate applications for foster and adoption.
Constraints: What adopters say they want and what they actually want may differ. Volunteers need to be able to differ between constraints that are non-negotiable and those that are flexible.
Foster: Fosters are happy to take in a dog in the short term they would not adopt in the long term.
Flexibility: Adopters end up adopting dogs outside of their original search. Flexibility is important to the process.
From the interviews I created research artifacts including personas and a journey map. I identified three personas: the new dog owner, the experienced owner and the rescue volunteer. I created one journey map that focuses completely on the end user (adopter) journey, I found that the two adopter personas had a similar enough journey to combine into one.
Reframing the Problem
Adopting a dog is complex and time-consuming, it often involves applying multiple times.
Big Ideas
From research I moved into the ideation phase. I quickly brainstormed some ideas and then organized those ideas around the problem they were solving.
Doggie Matchmaker
Problem: Finding the right dog is time consuming
Solution: Use machine learning to read the adopters application and show relevant dogs. System learns as users look at dogs.
Requires Validation: AI developers will need to donate their time. Possible corporate sponsorship.
One Form to Rule Them All
Problem: Separate applications for every rescue
Solution: One application for all rescues
Requires Validation: We can work with the rescues to standardize the application
A Serendipitous Dogscovery
Problem: Sometimes adopters want a certain kind of dog but end up adopting something different.
Solution: Users can filter for flexibility. Users can see a Wild Card selection of dogs that are outside of their original search constraints but still matched to some of their interests.
Requires Validation: Can we motivate users to look outside their constraints
A Doggie Dog World
Problem: Searching for a dog is competitive with a lot of adopters applying for the same dog.
Solution: Adopters see how many people have already applied for a specific dog. Adopters can see maps and a calendar of adoption events.
Requires Validation: System will need to account for individuals who applied for a dog directly with the rescue.
Superhero Search
Problem: New dog adopters struggle to connect with rescue organizations.
Solution: Adopters can connect directly with rescues through the site. Adopters can filter their search by rescue so they can adopt from rescues they know and trust.
Requires Validation: Allow users to volunteer with a rescue without leaving the site.
Technology
Let's use machine learning to do the heavy lifting for us. Rescue dog adoption includes a great deal of unstructured data points - the adopters preferences and constraints, dog breed characteristics, and adoptable dogs. The system can be trained on dog breed characteristics and traits from sources such as Wikipedia and Dogtime. Once a user's application is submitted, natural language processing and sentiment analysis can cross-reference the application with adoptable dogs and dog breed traits to display curated and personalized results.
User Flow
I went through several iterations of the user flow and information architecture.
Wireframes
I went through several rounds of wireframe iterations, testing with users each step of the way.
Here are a few of the first low-fidelity wireframes.
Final Wireframes
I tested the initial wireframes with end users, gathered feedback and created the final wireframes. In this scenario the end user, Rebecca finds out about Rescue Me through Social Media. Rebecca is interested in adopting a dog and creates a profile through Facebook. Rebecca fills out the rescue application, she's a working professional who lives alone in an apartment with no backyard. Rebecca is presented with her curated dog results, adult Shepherd mixes that are house-trained. Rebecca explores the advanced filtering section. She filters for a more active dog and is shown a warning that this type of dog is contrary to the information she provided in her rescue application, knowing that she will have a more difficult time getting approved for a more active dog she clears her filters. Rebecca likes the looks of Mr Wimble and views his profile in more detail. Rebecca sees that he is up for adoption through Muddy Paws rescue and looks over their information in more detail. Rebecca likes what she sees with Muddy Paws and Mr Wimble and submits her application, she's aware that several other people have applied for Mr Wimble and wants to get her application in ASAP. She sees that Mr Wimble will be at an event at Union Square and decides to go see him while she waits to hear from Muddy Paws about her application. Rebecca meets and falls in love with Mr Wimble at the adoption event. Soon after the event Rebecca receives an email that she has a new message on Rescue Me. She logs into the site and checks her messages. Rebecca sees that her application for Mr Wimble was approved! Rebecca successfully adopts Mr Wimble and immediately goes to pick him up. Rebecca is elated and recommends Rescue Me to all her friends and co-workers looking for dogs.
Next Steps
The next steps for this site are to create prototypes and high fidelity mock ups and recruit AI developers to begin building the site. The site will also need to be tested more extensively with end users. A native mobile application needs to be designed and built once the MVP is validated.
Future Considerations:
Post-adoption: What is the post-adoption experience
Continuous Engagement - How do we encourage users to return to the app once they've successfully adopted a dog
Application - Can we work with rescues to check the application as soon as the user fills it out. Then when a user submits the application it has already been pre-approved.
Scale - We'll need to scale to other cities.