I turned 390K merchants into a recommendation list users can pick from right away.
A service that analyzes 390K Gyeonggi local-currency merchants and recommends them by location and spending category
- Period
- Jul – Aug 2025
- Role
- PM · Frontend · Design (team of 6)
- Status
- 2nd round · top 12% of 247
Summary
In a survey we ran ourselves, 53% of respondents said local-currency merchants were hard to find (27%) or too few (26%), and Gyeonggi's 390K merchants were published only as raw data. After analyzing all 390K records, I put a list recommending merchants by distance and spending category on the first screen, instead of making users search the map themselves. I built the frontend myself in React and TypeScript, and the project reached the second round of the LIKELION 13th hackathon, in the top 12% of 247 teams.
Impact
- finding a merchant
- Searching the maptoRecommended by place & category
- evidence · Gyeonggi merchants · 53% in our survey (hard to find 27% + too few 26%)
- All 390K analyzed
- LIKELION 13th hackathon · 2nd round
- Top 12% of 247
Background
- Despite a large base of Gyeonggi local currency users, spending concentrated in large merchants due to difficulty discovering stores — leaving small business owners with minimal benefit.
- Submitted to the LIKELION 13th national hackathon — as part of a 6-person team, planned and developed a service solving the public problem of Gyeonggi local currency store discovery with AI.
Execution
Product Planning & Design
- Planned an AI feature that analyzes user's current location, spending patterns, and category preferences to recommend personalized merchants.
- Designed the full UI/UX in Figma and structured the service to simultaneously achieve local small business promotion and user benefits.
Frontend Development
- Developed responsive UI in React + TypeScript + Tailwind CSS. Implemented core screens: location-based merchant map, AI recommendation list, and category filters.
Results
- Advanced to the 2nd round of the LIKELION 13th national hackathon — top 12% among 247 teams.
What I learned
- Improving recommendation accuracy was easier than building a screen that makes people actually visit the store. Making merchants legible on the map mattered more than the algorithm.
- 'Revitalizing the local economy' is a phrase for judges; for users it was just 'where near me do I get a discount?' Translating macro goals into personal benefits is the planner's job.