B

Multi-channel Settlement Discrepancy Auto-audit SaaS

4.15

Derivation Chain

Step 1 Explosive growth of multi-channel e-commerce
Step 2 Small D2C sellers commonly operating 3+ channels simultaneously
Step 3 Settlement staff manually cross-checking channel statements, fees, and promotional discounts
Step 4 Multi-channel settlement discrepancy auto-audit SaaS

Signal Sources (v8 Triple Source)

Trigger

멀티채널 주문통합 미들웨어

Market

OMS/정산관리 SaaS 카테고리 (셀러허브, 사방넷 등 대형 솔루션이 커버하지 않는 정산 감사 틈새)

Workflow

소형 셀러의 월말 채널별 정산서 수동 엑셀 대조 병목

Problem

Small D2C sellers (monthly revenue under 100 million KRW, approx. $75,000) operating 3+ channels simultaneously (Coupang, Naver, 11st, etc.) face settlement leakage of 2–5% monthly because each channel applies different fee rates, promotion deductions, and return deductions. Currently, the owner or office staff manually cross-check in Excel, spending 3–5 days, and many discrepancies go undetected.

Solution

Upload settlement CSV/Excel from each channel's seller admin, automatically match by order number, and highlight discrepancies in fee rates, discounts, and return deductions per transaction. Automatically generate a report with total discrepancy amounts and dispute templates ready to submit to channel customer support.

Target: Small D2C seller owners with monthly revenue of 30–100 million KRW (approx. $22,500–$75,000) / e-commerce office and settlement staff
Revenue Model: Premium 50,000 KRW/month (approx. $37.50) for 3 channels, plus 10,000 KRW (approx. $7.50) per additional channel per month
Ecosystem Role: -
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
4.0/5
M Market
3.0/5
R Realizability
5.0/5
V Validation
4.0/5
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.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (65%)

Tech Complexity
24.0/40
Data Availability
21.2/25
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.

Market Validation (59/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
16.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
12.0/15
Solo Buildability
6.0/10
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.
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