

I began by mapping out user journeys, flows, and task flows to better understand the customer experience. We developed low-fidelity wireframes and interactive prototypes to test the initial user experience. Collectors participated in usability testing and interviews, providing crucial feedback to refine the design.
I worked alongside engineers to design and launch the MVP. I collaborated closely with engineers ensuring technical feasibility and maintaining a high standard of quality throughout the development process. This allowed us to begin gathering pricing data, which was essential to training the AI's predictive pricing engine. The MVP focused on essential functionality, whereas the full launch included AI for authentication and valuation prediction.
Continuous user feedback shaped the development process. For instance, we discovered that auctions without a reserve price increased item sale percentages, with a 93% higher final sale value compared to those with reserve prices.



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