Urban Swan

Redesigning the search experience

Project Overview

Urban Swan is an experience marketplace targeted to Gen Z and Millennials. This case study explores the redesign of the search and categories functionality for the Urban Swan website, aiming to improve user experience and conversion rates for activity bookings.

The Problem

Users were struggling to efficiently find experiences that matched their interests, leading to a high bounce rates on the search results page and low conversion from search to booking.

The Solution

A search bar that enables customers to quickly discover and book experiences that match their interests and preferences and allows customers to find specific activities without browsing through multiple category pages.

Our redesigned search experience will deliver the following key benefits, driving growth and user satisfaction:

Quick experience discovery - It offers customers a direct path to find activities that interest them. This eliminates the frustration of clicking through multiple category pages to find the right experience.

Enhanced customer experience - When customers visit UrbanSwan, they often have a specific type of experience in mind. Data shows that 43% of visitors immediately use the search bar upon arrival. An effective search function helps them quickly find experiences that match their interests, schedule, and location preferences.

Higher booking rates - When customers can easily find experiences that match their criteria, they're more likely to complete a booking. A robust search function encourages deeper engagement with the platform, as users can quickly narrow down options based on their specific interests, availability, and budget, leading to increased bookings.

Research Process

User Research

Methodology:

  • Conducted 12 user interviews

  • Performed 8 usability tests of existing system

  • Created heat maps of user interactions

Key Findings:

Users often combined multiple search criteria (e.g., "outdoor cooking class weekend")

Users wanted to filter by both activity type and practical constraints (time, location, price)

Category labels were confusing and overlapping

Competitive Analysis

Examined search and category systems of:

Identified successful patterns:

  • Progressive disclosure of categories

  • Robust autocomplete suggestions

  • Clear visual hierarchy in results

  • Smart filters based on search context

Design Solution

Search Improvement:

  • A prominent, full-width search bar that opens into an interactive modal

  • Three primary search parameters (what, where & when)

  • Calendar integration that allows for flexible date ranges or specific dates

  • Added "trending experiences" suggestions

Advanced Filtering:

  • Category tabs at the top showcasing property types with visual icons (Pottery, Bars, Massage, etc.)

  • Dynamic filters that appear based on search context

  • Added "Similar experiences" suggestions

Prototyping & Testing

Initial Prototypes

Created low-fidelity wireframes testing:

  • Category organisation

  • Search flow

  • Results display

  • Filter interaction

Usability Testing

Conducted 2 rounds of testing:

  • Low-fidelity prototype with 5 users

  • High-fidelity prototype with 10 users

Iterations

Made several key improvements based on testing:

  • Modified category icons

  • Simplified the category search section

  • Added ‘Trending Categories’ in search bar

  • Improved filter visibility

End Design

Through close collaboration with our engineering team, we implemented the redesign in strategic phases. Here are the final design outcomes

Search Bar:

A redesigned search bar with powerful filtering options that help you find exactly what you want. Simply type in what kind of experience you're interested in then use filters like available dates and location to get results that match what you're looking for. The idea is to connect you with the perfect experience without having to dig through pages of options that don't fit your needs.

Categories

We've thoughtfully placed additional filtering options right below the search bar to help you explore even further. Our refined search system helps connect you with experiences that spark your interest – without the endless scrolling.

Lessons Learned

What Worked Well

  1. User-centered design process

  2. Iterative testing approach

  3. Close collaboration with engineering

Areas for Improvement

  1. Earlier involvement of stakeholders

  2. More extensive A/B testing

Next Steps

  1. Add machine learning for better suggestions

  2. Expand on category system

Conclusion

The redesign successfully improved the user experience of finding and booking experiences. Key to this success was the combination of user research, iterative design, and close collaboration with stakeholders. The project demonstrates the importance of balancing user needs with business goals while maintaining a focus on simplicity and usability.

Appendix

Tools Used

  • Figma for design and prototyping

  • UserTesting for remote studies

  • Hotjar for heat mapping