Summary
Problem
We knew from past research that carsales’ current service lacks a seamless local experience, requiring users to take extra steps to refine location and manually determine distances to cars. This process can feel repetitive and inefficient. In contrast, competitors offer a more integrated approach by automatically remembering user locations, enabling sorting by distance, etc. which creates a smoother, more personalised experience. Our goal was to enhance our location-based experience to reduce friction, improve usability, and ultimately drive more successful interactions between buyers and sellers.
My role
I took charge of the research and design efforts, which involved:
- conducting a competitive review,
- analysing existing internal user research,
- mapping the current experience using the service blueprint framework,
- designing concepts to address key pain points,
- setting up and conducting user interviews to test concepts,
- synthesising feedback into actionable recommendations.
Results
Validated user need
Research validated that users expect a seamless, 'local' experience with minimal manual input. While location is key, it’s not top of mind like car attributes, and users often don’t think to filter by it. They assume the system will automatically show local results.
Uncovered internal complexities
Legacy business rules and technical challenges, like handling multiple search locations, made implementation difficult. A one-size-fits-all solution wouldn’t meet the diverse needs of carsales users. More time and exploration were needed to find the right approach.
Future feasibility work
While development was not immediately possible due to shifting business priorities, our insights have us direction for future enhancements. Further feasibility work will explore scalable solutions to deliver a more seamless and personalised location experience.
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