B

Youth Employment Program Performance Tracker

3.35

Derivation Chain

Step 1 Expansion of university youth employment support programs
→
Step 2 Employment program operations infrastructure
→
Step 3 Participant performance tracking & reporting automation

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

Hundreds of billions of won are invested annually in university employment support programs such as the Ministry of Labor's 'First Step Job Guarantee Centers,' but outcome metrics — employment rates, retention rates, satisfaction scores — are collected and reported using Excel. A single staff member spends 30+ hours per month on data collection and organization, and when the Ministry of Labor changes its reporting format, reprocessing existing data takes an additional 2-3 days.

Solution

Provides automated SMS/KakaoTalk survey distribution to participating students, automatic response data aggregation, and one-click performance report generation formatted for Ministry of Labor and Ministry of Education submission requirements. Automatically tracks each student's employment timeline for real-time monitoring of 6-month and 12-month retention rates.

Target: University career support center staff (3-10 employees), universities commissioned by regional labor offices (~60 universities nationwide)
Revenue Model: SaaS Monthly Subscription $97/month per university (~129,000 KRW, up to 500 students), $187/month for 1,000+ students (~249,000 KRW)
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
4.0/5
M Market
3.0/5
R Realizability
4.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (74%)

Tech Complexity
34.7/40
Data Availability
19.4/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (59/100)

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
7.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Backend [medium] Frontend [low] Infrastructure [low]
Dashboard