PackPal
A packing list app that generates personalized, editable lists based on your specific trip, designed to reduce travel stress before it starts.
Research showed 71.1% of users overpack and 21.1% don't plan at all. PackPal was our response: a packing list app that generates personalized, editable lists based on a user's specific trip details. We ran a survey, conducted 3 interviews, and did a competitive analysis of Packr before developing personas, sketches, wireframes, and a high-fidelity prototype. The brand leaned calm and organized, using Quicksand and Inter to keep the experience clean and anxiety-reducing rather than overwhelming.
Most people either overpack or don't prepare at all. There's no calm middle ground.
Existing packing apps either overwhelm with generic lists or require too much manual input. PackPal's core insight: if the app knows where you're going, how long you'll be there, and what you'll be doing, it can generate a starting list that actually fits your trip. The user edits from there, not from zero.
Survey, interviews, competitive analysis, then into Figma.
We ran a user survey, held 3 in-depth interviews, and completed a competitive analysis of Packr before touching any screens. From research we built personas, sketched flows, and moved into wireframes before finalizing a high-fidelity prototype in Figma.
Early sketches and wireframe explorations
High-fidelity prototype: list generation and editing flow
Most Customer-Centric award, Design Club.
PackPal won the Most Customer-Centric award at Design Club's showcase, recognizing how directly the solution mapped to real user pain points discovered during research.
Restraint is a design decision, not a compromise.
The instinct in a team project is always to add more: more features, more complexity, more ideas from everyone. PackPal pushed back against that. The app was designed to reduce travel anxiety, and every feature that didn't serve that goal directly had the potential to create the thing it was trying to prevent.
The persona work was more valuable than I expected. When you can point to a specific user and ask "does this feature help her?", the conversation about what to include becomes a lot faster. It's not a preference argument anymore.
If I were to continue this project, I'd explore how the list-generation logic could get smarter over time, learning from what you actually pack and adjusting future recommendations. That's the version that builds a habit, not just solves a one-time problem.