Connecting shelters and adopters to find the perfect match for every lifestyle
According to the ASPCA, 6.5 million animals enter shelters each year. 1.5 million of those animals are euthanized, with Georgia being one of the largest contributors. The problem has become so great in the region that many Georgian shelters send excess animals to other regions. In 2016, the Georgian legislature named the "adoptable dog" as the official state animal. Being a group of animals lovers, we sought to develop a solution to assist and promote these animals to families.
Floofy is a digital platform that seeks to unite wanting adopters with adoptable cats and dogs through meaningful attributes with the assistance of local shelters and rescues.
In this five-month project, I served as a UI/UX designer and User Researcher.
Going in to the research process, we had some initial assumptions as to why families choose to purchase an animal from a breeder rather than to adopt one from a shelter. When people adopt from a breeder, we believed they were looking for: a pure breed, a kitten or puppy, and quality assurance.
Before we dove into a solution, we conducted desktop research on the currently available systems at shelters and rescues. This would catch us up on what is already being done on the surface and identify obvious opportunities for improvement. For the intents of the project, we decided to focus on shelters in the Atlanta metropolitan area first and later scale it up if found to be viable.
Early Contextual Interviews
After identifying our preliminary adoption centers and categories, I connected with a volunteer at a local rescue to conduct a contextual interview. I attended a weekend adoption event run by Animal Action Rescue and observed a volunteer promoting the organization and available dogs outside of a local PetsMart. While there, I asked a series of questions based on actions and thoughts of the volunteer and browsers. While freeform, this observation assisted in building a rapid overview of concerns held by adoption center volunteers as well as potential adopters.
We crafted a survey on Qualtrics and that was disseminated on local subreddits, r/atlanta, and r/georgiatech. Although these populations are limited in diversity, they offered an efficient method for gathering high-level data. Although unpolished and broad, our survey revealed a number pain points and considerations people make when they are looking to adopt or choose not to adopt. The survey ended with a prompt for participants to submit their email should they be willing to be interviewed in-depth at a later date.
After identifying general leads, I established contact with leadership at the Atlanta Humane Society (AHS) and FurKids Animal Rescue and Shelters. I led interviews with a fellow researcher taking notes. Our participants helped us understand their daily routines and how they manage their relations with interested adopters. The intent of these was to identify pain points on the shelter side and see if they can be connected with adopter concerns.
Our team reached out to survey participants who had adopted an animal in the Atlanta metropolitan area within the past 12 months. Subjects expressed concern about difficulty they had when applying for an adoption in both intrusiveness and repetition. They also spent months looking for the right dog and eventually took a hiatus from the fatigue of filling out and submitting applications when they discovered their first choice was already taken. Another adopter shared some regret with a recent dog they rescued. The person had limited exposure to and information about the animal before they adopted them, and the animal's temperament was different than expected.
What it Offers
After interviewing, our research team sat down together to share and discuss findings. We noted every key finding, pain point, and desire onto a whiteboard and began to build traits to describe our users. From these traits, we were able to craft a diverse range of users who would benefit from our design.
After interviewing local shelters and adopters, our research team sat down together to share and discuss findings. We noted every key finding, pain point, and desire onto a whiteboard and began to build traits to describe our users. From these traits, we were able to craft a diverse range of users who would benefit from our design. These traits can be seen in the table below:
Wants a pet for her kids like when she was growing up
Concerned about how an animal will interact with her kids
Hindered by time needed to care for pet and service at the shelter
Recent College Graduate
Wants companionship to help with loneliness away from home
Concerned about the cost and time needed for his pet
Hindered by small living space and rental restrictions
First-Time Pet Owner
Wants to adopt (not shop) from a shelter she heard about online
Concerned about roommate's opinion
Hindered by confusing adoption process
Wants to use his experience rescue an animal in need
Concerned about an animal's health needs
Hindered by the compatibility and number of current pets
Wants a pet as a companion for his partner
Concerned about his own pet allergies
Hindered by finding and training puppy
Upon sussing out our pain points and users, the team separated to sketch initial ideas. These ideas were created under the pretense of no budget, resource restrictions, or otherwise to promote ingenuity.
After sketching, the team reconvened to share and expand upon out ideas. We grouped concepts based on the pain points addressed or media used. The team researched and discussed the feasibility of different media and content and ultimately decided to draft low resolution wireframes for three concepts:
Survey that helps users be matched with a specific animal in a shelter, not just a breed
Promotes discovery of mixed breeds, unique personalities, and special needs
Guide that educates novice owners on responsibilities, resources and prerequisites for pet ownership
Survey to recommend specific needs for each animal
Online store that allows users to preview and set items in their homes
Products for pets and owners alike
This concept derived from users’ confusion and uncertainty about how to find the correct pet. There are many pet matching sites on the market, but we identified some missed opportunities. From our interview with the Atlanta Humane Society, we learned that many shelters use a program called PetPoint. This program is a database that shelters use to log information about a pet such as its identification number, appearance, bite history, and owner history.
Some of this information is used by PetPoint to upload a profile in pet finding site, Petango. While this site features the latest information about an animal, its survey to help find matching pets for owners is wanting. Many of the questions are esoteric personality questions that do not make it clear to adopters what it is leading to. When a user completes their survey, they are then matched with ~2,000 animals with match percentages assigned to each one. A number that high is too overwhelming for someone to take action on and properly vet. The match percentages seem arbitrary and only serve to keep a person from looking at animals that they only have a 40% match with. These survey and result themes are common amongst pet finders. They are flawed, but the desire to filter through animals is there. Another strong point to utilize a survey is that is can mitigate breed bias and help users discover mixed breeds that are more common in shelters.
Our solution would curate questions to be more clearly about the desired pet than how the owner would describe themselves. Many questions can also be created that could help users realize concerns or desires that they had not considered and better inform them for ownership. Another key goal would be to try to limit the number of animals that appear in a result. This would make results easier to parse as well as make the user feel a stronger sense of “destiny” with the select few that appear. This limitation could be achieved by reducing the available range of the search or by parsing the results down to specific shelters.
From our research, we saw a recurring theme of frustration with the actual adoption process. Many shelters require potential adopters to provide documentation for their housing, current pets, and finances. This can feel intrusive or overwhelming for some adopters. With a pet preparedness application, we can educate adopters on necessary information. They can then use a checklist to track their tasks and use it to share their eligibility to shelters. Speaking with the Atlanta Humane Society, the application takes about 1.5 hours in-store to process. One hour of this is spent waiting to speak with an adoption counselor.
Seeing an opportunity for the preparedness app to help an owner after adoption, we wanted to include features that promoted the continuing education and management. By entering information about an animal, a list can be generated with specific items for that animal. For example, a first-time dog owner can adopt a puppy with heartworms and be given a list to obtain a kennel and leash to manage its condition and to find a vet to schedule upcoming treatments.
AR Pet Shop
When considering a new animal, many people were concerned about how much space they would take up or how to plan out their area in their home. This is why we are looking into AR shopping as a potential solution. Pets such as retired greyhounds require large beds to accommodate their body structure and adopters need to make sure they can fit such things into their apartment or house.
This concept would enable users would be able to preview a bed in their home via AR. With the latest toolkits, users can scan a room to create a grid to fit items in a space at scale. Creating scenes can build excitement for and desire for products and users can share their dream scenes with others. If a user finds an item they like, then they can add it to their cart for purchasing.
Rapid Intercept Testing
To narrow our direction, the team intercepted participants for feedback at Tech Square in Atlanta. We equipped ourselves with wireframes depicting an overview of each solution for initial talking points along with a more detailed guide to walk participants through the concept. While the AR applications were interesting, overall they were perceived as novelties over tools. Based on our tests, the Pet Matcher received the highest praise. It also proved to be a differentiator while still having a practical use that many felt comfortable accessing. The format lended itself to scalable features such as information on breeds, shelters, shelter interaction, and light commerce.
As we continued to interview, it became evident that our design would have to serve two sides of the process: adopters and shelters. Once an adopter has discovered a potential pet, our design would provide a seamless connection with the shelter caring for them. Conversely, the shelter should be supported with information on an adopter’s lifestyle and capability to care for a pet, enabling the shelter to better consult an adopter when initially approached. Shelters and rescues had shared difficulties with inventory and managing communication with interested parties, so the design could serve holistically within one platform.
To better understand the processes and interactions happening between the users and the system, a flow chart was developed. From this, we elaborated on every touch point the user experiences, whether that be user-to-user or user-to-system. This chart also acknowledges the scope this project has reached and allowed us to capture any possible edge cases that needed to be considered. As evident in the flow, pictured below, this two-fold project is connecting two different but equally important users while also allowing each user to talk to the system independently and uniquely.
Our class hosted a peer review session to gather insight and informed analysis from other teams and faculty. In addition to their feedback, we recorded notes and artifacts based on the comments received as well as what we observed on our own. Some of these review points were quickly implemented before testing.
To adequately measure our progress, we divided the testing into two parts: the adopter side and the shelter side. For the adopter side, we searched for participants who have had an animal before. For the shelter side, I reached out to the Atlanta Humane Society to share the prototype with employees of varying tasks and levels.
For each platform, we created goals and tasks that, when performed by our participants, allowed us to measure click counts, errors, time taken, and success rates. These metrics would help look at the KPIs of the designs from a quantitative perspective.
In addition to the recorded units, each task included follow-up questions. Finally, after all tasks were completed, each participant was asked additional holistic questions. At the conclusion of the activity, participants were asked to complete a modified SUS evaluation.
After distilling our findings from our participants, it became evident that we accomplished the goal of providing more educational content for adopters to reference before adopting a pet. In addition, participants indicated that the information provided about the shelters proved very helpful and helped addressed the previously expressed concerns about shelter requirement confusion. We also received feedback from some of the participants about confusion in navigating the tabs.
Participants responded in an overall positive manner. They mentioned that this system could be beneficial to their organization, and would help to eliminate some of their frustrations. We discussed some of the key issues the shelters face, and participants indicated that this would alleviate some of the tasks that rob them of valuable time. However, participants did mention that they wished to make a more personal connection for potential adopters.
Participants expressed confusion when presented with the task functionality. While they felt this could be highly beneficial, they did mention that there would be some areas of tension with the application. For instance, shelters typically do not take appointments. The shelters operate on a first come, first serve basis. The matching and scheduling functionality would need to align to the shelters’ operational process.
In the course of testing our designs, we learned a great deal about our process. When we began testing, we immediately became very conscious of problems that arose with the prototype itself. During the first test of the adopter’s prototype, we realized the prototype was very linear and testers did not have much flexibility to explore. The original objective was to keep the prototype lean so that we did not over invest time creating unnecessary screens or links. However, this lean thinking hindered us once users realized that few of the buttons were actually linked. They became less likely to click around and search for the solution to their assigned task.
Additionally, after a task was assigned, participants would often click on secondary links rather than the primary paths that we had erroneously assumed they would use. This caused confusion particularly because we requested that our users alert us when they believed they had achieved the task. Unfortunately, participants still struggled to arrive at the screen we intended them to navigate to, because their preferred path was not currently linked in the prototype. They were not incorrect, but we inadvertently made them believe they were
I also had difficulty determining which errors were true errors that needed to be updated to be tested, and which mistakes should remain in order to maintain a consistent testing experience. When we received feedback from participants, I became inspired to add new pages, features, and categories, but we reluctantly chose to waiting until after the tests were conducted and synthesized so as to not compromise the validity of the study.
In the future, we would need to set aside more time to “playtest” our prototype and preclude surprises before testing began.