TOEIC Refund Settlement Manager

3.70

Derivation Chain

Step 1 Proliferation of TOEIC refund courses

→

Step 2 TOEIC academy/online course refund program operations

→

Step 3 Automated tracking and settlement of refund condition fulfillment

Problem

As TOEIC online course providers like YBM and Hackers competitively run complex refund programs (500% cashback, conditional refunds, etc.), managing per-student refund conditions (attendance rate, target score, enrollment period) requires 3-5 hours of manual work per month per student. Refund settlement errors account for 20-30% of all customer service complaints, and delayed refund payments generate negative reviews on Naver cafes and blogs, hurting new student acquisition.

Solution

A SaaS that automatically tracks each student's refund condition fulfillment via a real-time dashboard, auto-calculates expected refund amounts, and sends automated alerts to at-risk students who may not meet conditions. Integrates with LMS (Learning Management System) APIs to automatically collect attendance and grade data, and generates refund settlement statements with one click.

Target: Operations teams at TOEIC and language learning online course companies with annual revenue of 500 million to 5 billion KRW (~$375K-$3.75M) (CS/settlement staff at education companies)

Revenue Model: SaaS monthly subscription based on student count: up to 500 students at 99,000 KRW (~$74)/month, up to 5,000 students at 290,000 KRW (~$217)/month, above that at 590,000 KRW (~$442)/month. 20% discount for annual billing

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

4.0/5

V Validation

4.0/5

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 (69%)

Tech Complexity

29.3/40

Data Availability

19.4/25

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

6.2/20

Timing

14.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

Solo Buildability

8.0/10

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] Data Pipeline [low]

Dashboard