MoodBoard
Our moodboard helped set the visual style for Petica. We chose soft colors, friendly shapes, and warm imagery to make the platform feel welcoming and trustworthy. It gave us a clear direction for the look and feel of the website and app.
As one of the team members of UnExpetica, I contributed significantly to the Petica Project, focusing primarily on UI/UX design and presentations. Initially, I was involved in the design and presentation aspects of the project, but as we reached the midway point, I stepped into a more leadership role, helping guide the team towards successful project completion. In my role as a UI/UX Designer & Front-end Developer, I collaborated with the team to take Petica from concept to an interactive prototype. I contributed to the design process using Figma, working alongside the team to iterate through user testing and expert feedback. The project was built using React, and I helped ensure that the user experience was intuitive and aligned with our vision for the project.
By developing an online pet recommender, we aim to solve a number of problems or difficulties that people have when they want to get a pet. To start with, most people nowadays are not aware of how they should make their proper choice of a pet. Also, some of them experience difficulties when taking care of the animal. This is why we want to add value with our project by reducing people's insecurities so that they can be sure that they are making the correct choice and are also informed about how to treat the animal in a proper way.
This platform will guide potential pet owners towards making a responsible and well-informed decision, aligning their lifestyle and preferences with the needs and care requirements of their new pet. This not only ensures a harmonious relationship between pets and their owners but also mitigates the risk of future abandonment or mistreatment, thereby indirectly contributing to the reduction of animals in shelters.
To better understand the needs, expectations, and concerns of our future users, we conducted a detailed survey. Through analyzing the results, we gained valuable insights into users' enthusiasm for pet ownership, their worries about responsibilities and costs, and the strong interest in having a smart platform that could help guide them towards the right pet match. Below you can explore the key findings in visual form.
Most participants are excited about pet ownership but seek more guidance.
The idea of a recommendation platform resonated strongly with our audience.
Primary concerns revolve around pet care knowledge, financial costs, and maintaining a clean environment.
People who are interested in getting a pet.
To expose users to a variety of animal options based on their lifestyle and preferences.
Most users lack proper information and often choose pets that don't match their lifestyle needs.
Through research and user feedback, we identified key user needs and insights to shape a pet recommendation platform that genuinely addresses the gaps between user expectations and responsible pet ownership.
Our moodboard helped set the visual style for Petica. We chose soft colors, friendly shapes, and warm imagery to make the platform feel welcoming and trustworthy. It gave us a clear direction for the look and feel of the website and app.
Through our online research, we identified a growing need for better support in pet adoption. While there are already educational platforms available, there is a clear gap when it comes to helping people choose the right pet. Many animals end up in shelters because owners realize too late they lack the time, experience, or resources to care for them properly. This insight reinforced the value of an online pet recommendation platform: to guide users toward informed decisions, promote better animal care, and ultimately help reduce the number of abandoned pets.
Through a series of user interviews, we explored how people feel about the idea of a pet recommendation platform, gathered important advice about different breeds, and identified key user concerns. The feedback helped us better understand what information users expect, what their biggest worries are when choosing a pet, and how we could add real value through Petica. This input directly influenced the features and guidance we prioritized in the platform.
After conducting interviews, we used the Affinity Map method to group insights into key categories: users' questions about pet experiences, reasons for getting a pet, factors to consider before adopting, and other concerns. This process confirmed the need for a smart pet recommendation tool, helping people find the right match for their lifestyle and reducing the number of pets ending up in shelters.
Based on our research, we created detailed personas to better understand our users' lifestyles, needs, and challenges. These personas guided important design and feature decisions throughout the project, ensuring Petica meets real user expectations.
To illustrate the user journey and highlight key scenarios, we created storyboards that visualize how users might interact with the Petica platform. These visual narratives helped us better empathize with users' needs and motivations.
Storyboard illustrating the journey of a first-time pet owner.
Storyboard depicting a user finding a companion pet for their existing dog through Petica.
To test basic structure and functionality early, we developed a low-fidelity prototype. This version focused on user flows and core features before moving into detailed design.
View Low-Fidelity PrototypeWe conducted remote user testing sessions to validate Petica's design and functionality. Participants performed tasks such as navigating the platform, completing the recommendation quiz, and finding care tips. Feedback was positive on usability, colors, and layout, while users also suggested improvements like making the quiz link more visible and adding an About Us page. These insights helped polish the final design and enhance the user experience.
The high-fidelity prototype incorporated polished visuals, branding, and refined user interactions based on feedback from user testing sessions.
View High-Fidelity Prototype