“I learned that internal team members were facing difficulties in following up on Zendesk customer inquiries, so I developed a Redash VOC dashboard to address this issue. The system automatically collected and preprocessed Zendesk data, then used the OpenAI API to categorize and summarize customer inquiries by topic. Additionally, a Slack notification was set up to alert the team each Monday about the topics with the highest increase in inquiries, helping identify and respond to customer issues more efficiently. As a result, we were able to eliminate about $275 in opportunity costs each month and reduce the time spent by team members on VOC follow-ups.”

“Rolling MAU와 같은 복잡한 Rolling Metrics를 계산하는 데는 대규모 데이터셋에서 막대한 시간과 비용이 소요될 수 있습니다. 기존 쿼리로 6시간 이상 걸리던 작업을 쿼리 최적화와 B-tree Index를 통해 6초로 단축했습니다. 이 과정에서 불필요한 메모리 사용을 줄이고 쿼리 성능을 극대화하여 데이터 처리 효율성을 크게 향상시켰습니다. 이를 통해 기업이 Rolling MAU 지표를 효율적으로 관리하고 인프라 비용을 절감하는 데 기여할 수 있었습니다.”

“Calculating complex Rolling Metrics like Rolling MAU can consume significant time and cost on large datasets. A task that previously took over 6 hours with the original query was reduced to 6 seconds through query optimization and the use of a B-tree Index. This process significantly enhanced data processing efficiency by minimizing unnecessary memory usage and maximizing query performance. As a result, the company was able to manage Rolling MAU metrics more efficiently, contributing to infrastructure cost savings.”

“데이터 웨어하우스에서 IP 주소를 사용해 사용자의 국가 정보를 매핑하는 작업을 최적화하였습니다. 기존 방식은 연산 시간이 길어 비효율적이었으나, 새로운 접근 방식을 통해 쿼리 실행 시간을 90% 감소시켰습니다.”

“Optimized the process of mapping user country information using IP addresses in a data warehouse. The previous method was inefficient due to long processing times, but the new approach reduced query execution time by 90%.”