Following the collection of data using the above methods, we consolidated our data into an affinity map, from which we were able to extrapolate several research findings and insights. These research findings informed our creation of storyboards and generation of design concepts.
Literature Review
To get a better understanding of the context we were designing for, the team began with a comprehensive literature review. The goal of our literature review was to define user group characteristics, understand common issues faced by pets and owners post-adoption, and identify current practices within the pet adoption and adjustment space.
Competitive Analysis
As part of our secondary research, we also looked at existing technologies, products, and processes on the market that support both pet adoption and adjustment. We identified four commonly used tools and technologies used by new and prospective pet owners, including matching programs such as Meet Your Match, behavioral management products, PetFinder, and GoodPup, a dogtraining application. We chose to conduct a competitive analysis to determine these existing solutions' strengths and weaknesses, and identify opportunities for improvement. From our analysis we learned:
- While MeetYourMatch is designed to match owners and pets based on compatibility, the lack of experience makes many first-time adopters unlikely to meet the adoption criteria for shelters
- Many existing tools fail to consider both owner and pets' lifestyles in showing adoptable animals
- Training services and in app one-on-one expert consultations are often costly
- There are a vast amount of behavioral aid tools on the market with varying amounts of succes for different pets
- Most existing services fail to consider pets' unique quirks, and how this impacts the their needs.
Survey
The primary purpose of our survey was to garner a high-level understanding of our users’ pain points and needs through the adoption process and adjustment phase. We focused our survey questions on users’ current habits, attitudes, and behaviors around pet ownership. This would allow us to identify the services pet owners use to adopt, challenges first-time and existing pet owners face, and the hurdles prospective pet owners face in choosing to adopt.
Our final survey in Qualtrics consisted of 31 questions total, with different logic flows for existing and prospective pet owners. We distributed the survey across Slack channels and reddit threads. We received a total of 27 responses, out of which 25 were current/previous pet owners and 2 were prospective pet owners.
Findings
Of the pet owners surveyed, we found:
Semi-Structured Interviews
Following the collection of qualitative and quantitative data from our survey, we chose to conduct semi-structured interviews to dive deeper into our users' attitudes, beliefs, and emotions towards pet ownership.
In total, we recruited 5 participants - four pet owners and a veterinarian - for hour long interviews. We asked questions in a semi-structured format, allowing for flexibility in asking follow-up questions as needed. We based the interview questions on the trends and patterns observed in the survey responses. Our interview questions were exploratory, following up on the insights and experiences relevant to our problem space.
Data Analysis
To organize and understand the data collected from our user interviews, we chose to create an affinity map. By visualizing the information hierarchy, affinity maps facilitate the generation of new insights and highlight emerging patterns and themes in the data. The process of collaboratively immersing ourselves in the data makes affinity modeling a strong tool to build design ideas off of each other and understand overarching themes in our data.
With the 5 interviews we conducted, we were able to transcribe audio recordings and utilize notes to generate 150 interpretation notes. Over several hours, we then worked to group our interpretation notes based on the general topics and behaviors.
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Based on the groupings in our affinity map and data collected from our survey, we generated the following insights.