In this case study, I’ll share my contribution to improving the search experience for the PatPat mobile app as part of the YML team. I worked with PatPat for a year on various iOS projects, but this case focuses on one that was not only designed but also implemented and analyzed.

Typically, in an agency setting, we don’t get to see the results of our work—we deliver and move on. However, this project was different, allowing me to stay long enough to see the impact of our efforts.

PatPat is a curated online boutique of fashionable matching outfits for the whole family to bring the crew together in style.

Our analysis revealed that search performance was falling short. Collaborating with engineers, our product team gathered data to quantify the issues and confirm the need for improvements.

Improve the search experience of the Mobile App using best practices in usability to make it more intuitive, engaging, and effective, reducing user frustration and improving overall engagement.

Despite having quantitative data, we needed deeper qualitative insights into search usage. To address this, I collaborated with our researcher, to conduct a series of user interviews.

We recruited participants through the usertesting.com platform to gather firsthand feedback and better understand user behavior and pain points.

In addition to gathering quantitative and qualitative insights, I conducted a competitive analysis to benchmark search functionality against both direct and indirect competitors.

This analysis helped identify common industry standards and emerging trends in search experiences.

I evaluated key metrics such as search bar visibility, autosuggestions, typo correction, result relevance, filtering and sorting options, the handling of empty search results and more.

Finally, I conducted desk research to gather insights from industry-leading sources without direct user interaction. This involved reviewing best practices, usability guidelines, and research findings from reputable organizations such as Baymard Institute, Nielsen Norman Group (NNG), and Forrester Research.

This research provided a solid foundation for our decisions, helping us align with established UX principles and identify proven strategies to improve the search experience.

The search bar has been improved by placing it prominently on key screens and ensuring it remains visible while scrolling. This enhancement makes it easier for users to locate and use the search function without unnecessary effort.

Popular search suggestions have been introduced to inspire users and encourage exploration. This enhancement helps users discover trending items and engage with the app even when they don’t have a specific product in mind.

Recent search queries have been introduced to help users quickly revisit previous searches without retyping. This improvement enhances convenience and encourages repeated engagement with the search feature.

An autosuggest feature has been implemented to provide relevant suggestions as users type, helping them find items faster and stay focused on their intent.

The search input now remains visible in the search bar after submission, allowing users to refine their search without needing to retype their query from scratch.

The total number of matching results is now displayed, helping users gauge the effort needed to browse through available options and make informed decisions.

Typo detection and correction have been implemented to help users find relevant products even if they enter incorrect search terms, ensuring a smoother search experience.

The ‘no results’ page has been enhanced with an “Other options to consider” section, featuring flash sales, new arrivals, and trending items. This update helps users explore relevant products even when their initial search doesn’t yield exact matches.

Search relevancy has been significantly improved by refining the ranking algorithm to consider multiple parameters, each with a different weight, ensuring users find what they need quickly.

The most relevant items are now prioritized and appear first, enhancing the overall shopping experience.

For example, if a user searches for “black jeans,” search results are ranked based on:

• Exact match – Items with “black jeans” in the title rank highest.
• Popularity – Frequently bought jeans appear at the top.
• Ratings – Higher-rated products rank above lower-rated ones.
• Availability – In-stock items show before limited or out-of-stock options.
• etc.

This project reinforced the importance of a user-centered, data-driven approach to improving search functionality. By combining qualitative feedback with quantitative data, collaborating across teams, and benchmarking industry best practices, we successfully enhanced the search experience and drove meaningful improvements.

• Collaboration is key: Close teamwork between design, product, and engineering ensured feasible and impactful solutions.
• Balancing scope and priorities: Some features were deferred for future implementation, highlighting the need to focus on high-impact improvements first.
• Data-driven design works: Using research insights to guide decisions led to measurable improvements in user engagement and satisfaction.