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Pet Scout

UX CASE STUDY

PURPOSE

Master Thesis Project

MY ROLE

Lead UX Designer & UX Researcher

DURATION

16 months

YEAR

2023

Project Overview

About

PetScout is a user-centred platform that centralizes pet adoption across Europe. It simplifies the search process, improves matching between pets and adopters, and creates an intuitive experience to increase successful adoptions.

This study resulted in a published scientific paper.

Problem Statement

Users struggle to adopt pets because platforms are fragmented, pet information is incomplete or inconsistent, and adoption steps are unclear and slow.

Objective

Design a user-friendly platform that centralizes pet adoption, provides reliable information, simplifies the process, and helps users find the right pet quickly.

Approach

User-centred design thinking process combining qualitative & quantitative research.

We began by speaking directly with adopters through interviews and survey to understand their experiences.

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We translated research into personas, empathy maps, and job stories to define the problem clearly. Users needed fast search, reliable pet information, and a simple way to make confident adoption decisions.

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We used “How Might We” questions and Crazy 8 exercises to explore ideas. Features were prioritized using MoSCoW, focusing on filtering, matching, rich pet profiles, and a streamlined inquiry process.

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Low- and high-fidelity prototypes were created for mobile and desktop, evolving from wireframes to final mockups. Designs were iterated based on testing to improve usability and clarity.

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Both low- and high-fidelity prototypes were tested using the moderated usability testing with the thinking-aloud method and SUS questionnaire to evaluate and refine the design.

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User Research

Understanding users, their needs, and pain points.

We conducted four semi-structured interviews with dog owners to understand their experiences with pet adoption. Topics included:

  • Adoption process and challenges
  • Use of technology
  • Experience with adoption platforms
  • Personal background and context

An online survey with 39 responses provided quantitative insights into user behavior and preferences. It included:

  • Demographics and general adoption information
  • Pet search locations and duration
  • Frequency of platform visits and device usage
  • Importance of pet characteristics and platform features
  • Mix of open, multiple-choice, and Likert scale questions

We conducted four semi-structured interviews with dog owners to understand their experiences with pet adoption. Topics included:

  • Adoption process and challenges
  • Use of technology
  • Experience with adoption platforms
  • Personal background and context

An online survey with 39 responses provided quantitative insights into user behavior and preferences. It included:

  • Demographics and general adoption information
  • Pet search locations and duration
  • Frequency of platform visits and device usage
  • Importance of pet characteristics and platform features
  • Mix of open, multiple-choice, and Likert scale questions
KEY FINDINGS

Users mainly search for pets on mobile devices, but fragmented platforms and inconsistent information make the slow adoption process harder.

 
REFLECTIONS

The research showed that the main challenges are driven by poor user experience rather than a lack of solutions.

Understanding real user frustrations helped focus the project on simplifying the process and improving information clarity.

Problem Framing

Turning insights into clarity.

Finding and adopting a pet online is slow, fragmented, and frustrating due to scattered information, poor platform usability, and lack of reliable, detailed pet data.

  • Users must search across multiple websites, shelters, and social media platforms
  • Information about pets is often incomplete, inconsistent, or hard to trust
  • The process becomes long, complex, and overwhelming
  • Poor digital experiences can even discourage adoption altogether

Three personas were created based on user interviews, capturing users’ traits, goals, frustrations, and real quotes.

They helped represent different user types and their needs.

For example, Martin is a young professional who feels lonely and wants to quickly find a suitable dog without wasting time across multiple platforms.

Empathy maps visualized what the users say, think, do, and feel during the adoption process. For Martin, this highlights his frustration with long, fregmented searches and his desire for simple, efficient way to find a dog. 

Job stories described user motivations in context. For example, Martin wants to eliminate irrelevant options and make faster decisions when searching for a dog to adopt.

User scenarios illustrated how users interact with the platform when searching, comparing, and inquiring about pets. For Martin, this includes:

  1. Discovering the app
  2. Filtering dogs based on his needs
  3. Setting alerts when no suitable match is found

Finding and adopting a pet online is slow, fragmented, and frustrating due to scattered information, poor platform usability, and lack of reliable, detailed pet data.

  • Users must search across multiple websites, shelters, and social media platforms
  • Information about pets is often incomplete, inconsistent, or hard to trust
  • The process becomes long, complex, and overwhelming
  • Poor digital experiences can even discourage adoption altogether

Three personas were created based on user interviews, capturing users’ traits, goals, frustrations, and real quotes.

They helped represent different user types and their needs.

For example, Martin is a young professional who feels lonely and wants to quickly find a suitable dog without wasting time across multiple platforms.

Empathy maps visualized what the users say, think, do, and feel during the adoption process. For Martin, this highlights his frustration with long, fregmented searches and his desire for simple, efficient way to find a dog. 

Job stories described user motivations in context. For example, Martin wants to eliminate irrelevant options and make faster decisions when searching for a dog to adopt.

User scenarios illustrated how users interact with the platform when searching, comparing, and inquiring about pets. For Martin, this includes:

  1. Discovering the app
  2. Filtering dogs based on his needs
  3. Setting alerts when no suitable match is found

I gathered all the discovered information and presented it in the form of the following statement:

“Our users are European adults looking to adopt a dog from a shelter or rescue organization. We will improve their pet adoption and searching experience. Which, in terms a 5-year old would understand, is helping them find the best dog fast and easy. Currently, these users struggle because of:

  • too many different platforms to look through
  • searching takes a lot of time
  • some platforms don’t look serious or are too hard to use

Ideation

From ideas to iterations.

Based on user research, features were prioritized using MoSCoW analysis.

Core requirements focused on:

  • search and filter
  • detailed pet profiles
  • direct inquires

The platform was designed mobile-first, with desktop support.

The information architecture was designed to structure content and navigation clearly across mobile and desktop.

Below you can see the overall mobile app structure as well as detailed breakdown of the pet profile, ensuring users can easily find information and complete key tasks.

Prototyping & Iterative Design

From wireframes to final experience.

We translated the generated ideas into mobile and desktop wireframes to define structure and key user flows. The process was iterative, focusing on creating a simple and user-friendly experience across both platforms.

Mobile App

Website

Logo Design & App Icon

The logo combines a search icon with subtle shapes of a dog and a cat, representing both discovery and pet adoption. This concept is also reflected in the app icon, ensuring recognizability across platforms. The name Pet Scout reinforces the idea of searching and finding the right match.

Color Palette

Green was chosen as the primary color to convey trust, growth, and compassion, paired with white for clarity and simplicity. Accent colors were defined to communicate system states, such as success, warning, and error.

Typography

A clean sans-serif typeface (DM Sans) was selected for its readability and modern feel, supporting a clear and accessible user experience.

Grid & Spacing

A structured grid system ensures consistency across devices: a 4-column grid for mobile and a 12-column grid for desktop. An 8-point spacing system was used throughout to maintain visual rhythm and alignment.

High-fidelity mockups were created to translate the final design into a polished and realistic interface. They showcase key user flows such as searching, filtering, exploring pet profiles, and starting an inquiry, while applying the defined visual identity across mobile and desktop.

 

Clickable vertical prototypes were created in both low- and high-fidelity for mobile and desktop.

The prototype below includes integrated testing instructions, guiding users through tasks directly within the flow and supporting remote usability testing.

We translated the generated ideas into mobile and desktop wireframes to define structure and key user flows. The process was iterative, focusing on creating a simple and user-friendly experience across both platforms.

Mobile App

Website

Logo Design & App Icon

The logo combines a search icon with subtle shapes of a dog and a cat, representing both discovery and pet adoption. This concept is also reflected in the app icon, ensuring recognizability across platforms. The name Pet Scout reinforces the idea of searching and finding the right match.

Color Palette

Green was chosen as the primary color to convey trust, growth, and compassion, paired with white for clarity and simplicity. Accent colors were defined to communicate system states, such as success, warning, and error.

Typography

A clean sans-serif typeface (DM Sans) was selected for its readability and modern feel, supporting a clear and accessible user experience.

Grid & Spacing

A structured grid system ensures consistency across devices: a 4-column grid for mobile and a 12-column grid for desktop. An 8-point spacing system was used throughout to maintain visual rhythm and alignment.

High-fidelity mockups were created to translate the final design into a polished and realistic interface. They showcase key user flows such as searching, filtering, exploring pet profiles, and starting an inquiry, while applying the defined visual identity across mobile and desktop.

Clickable vertical prototypes were created in both low- and high-fidelity for mobile and desktop.

The prototype below includes integrated testing instructions, guiding users through tasks directly within the flow and supporting remote usability testing.

Usability Testing

Tested, refined, validated.

Early testing revealed issues such as confusion with the filter icon and unclear interactions in the pet inquiry form. These insights were used to refine flows and improve usability.

The refined design delivered a clear and intuitive experience across mobile and desktop. Users were able to navigate easily, find relevant information, and complete key actions without friction.

Users described the platform as intuitive, easy to use, modern, attractive and well-structured. The filtering system and detailed pet profiles were especially valued.

Minor improvements included clarifying required form inputs, improving onboarding guidance, and refining small visual and performance details.

  • Task Completion Rate: 100 %
  • Task Completion Time (avg): 166 s
  • SUS Score: 97

Early testing revealed issues such as confusion with the filter icon and unclear interactions in the pet inquiry form. These insights were used to refine flows and improve usability.

The refined design delivered a clear and intuitive experience across mobile and desktop. Users were able to navigate easily, find relevant information, and complete key actions without friction.

Users described the platform as intuitive, easy to use, modern, attractive and well-structured. The filtering system and detailed pet profiles were especially valued.

Minor improvements included clarifying required form inputs, improving onboarding guidance, and refining small visual and performance details.

  • Task Completion Rate: 100 %
  • Task Completion Time (avg): 166 s
  • SUS Score: 97
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Conclusion

Reflections

Testing confirmed that the platform is intuitive, user-friendly, and visually appealing, with only minor areas for improvement. It also showed strong impact of user-centred design and iterative testing on the final solution.

Next Steps

Future work includes expanding research with more diverse users and shelters, improving features such as tailored adoption forms and personalization, and developing the prototype into a fully functional platform.

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