100+
Funding Projects Conducted125%
Above Fundraising Goal52,000,000+
52,200,000+
Total Fund Raised (KRW)
What We Do
At Givers Polaris, giving begins with research, not assumptions. We guide student-led fundraising projects through a structured process that connects social issues, public data, recipient screening, campaign execution, and impact documentation. Each step is designed to help students make more thoughtful donation decisions and ensure that the funds they raise are directed toward communities where support can create meaningful impact.
1.Define the Giving Question
We begin by defining the core social issue and donation question: which regions in Korea most urgently need mental-health support? This step sets the direction of the entire project by turning a broad intention to give into a clear, data-driven problem to solve.
2.Collect Public Data
We gather reliable public datasets on population, depression prevalence, mental-health facilities, and basic-livelihood recipients across Korea. These indicators help us understand both the size of mental-health need and the social conditions that may increase vulnerability.
3.Analyze Regional Need
We calculate a weighted Need Score by combining three factors: estimated at-risk population, mental-health infrastructure gap, and vulnerability level. This allows each region to be compared objectively and ranked by donation priority.
4.Design Donation Strategy
We translate the analysis into practical giving directions. Each priority tier is matched with a recommended support strategy, such as counseling vouchers, outreach programs, facility support, or targeted services for vulnerable groups.
5.Drill Down Locally
After identifying high-priority regions, we zoom in further at the district level. In Seoul, district-level depression data helps identify neighborhoods where more concentrated mental-health support may be needed.
6.Methodology & Limitations
We record the data sources, formulas, assumptions, and limitations behind the analysis. This ensures that the donation decision is transparent, explainable, and ready to be improved with more detailed data in the future.