Subsidy Spending Analytics SaaS

3.20

Derivation Chain

Step 1 Livelihood subsidies

→

Step 2 Municipal subsidy disbursement

→

Step 3 Subsidy spending data analytics tool

Problem

Government officials and planning teams managing municipal livelihood subsidies and economic stimulus grants manually aggregate tens of thousands of card transaction records in Excel to analyze 'how much was spent in which business categories,' consuming 2-3 weeks and over ~$3,750 (5 million KRW) in labor costs per disbursement cycle. When deadlines approach, spending encouragement campaigns rely on intuition for targeting, resulting in 15-20% unspent rates.

Solution

Automatically integrates card merchant sales data with subsidy disbursement records to provide real-time dashboards with spending heatmaps by business category, region, and time of day, plus target lists of households with unspent balances. An unspent-balance prediction model optimizes SMS campaign timing, and one-click PDF report generation serves legislative reporting needs.

Target: Economic policy division officials and regional currency operations teams at small-to-medium municipalities (population 50K-300K), public administration professionals ages 30-50

Revenue Model: SaaS monthly subscription at ~$370 (490,000 KRW)/municipality account, 20% discount for annual billing. Additional analysis reports at ~$37.50 (50,000 KRW) per report

Ecosystem Role: Infrastructure

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

4.0/5

M Market

3.0/5

R Realizability

3.0/5

V Validation

3.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (68%)

Tech Complexity

29.3/40

Data Availability

18.3/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (57/100)

Competition

8.0/20

Market Demand

9.4/20

Timing

14.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

Solo Buildability

5.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

Backend [medium] Frontend [medium] AI/ML [low]

Dashboard