Multi-channel Settlement Discrepancy Auto-audit SaaS

4.15

Derivation Chain

Step 1 Explosive growth of multi-channel e-commerce

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Step 2 Small D2C sellers commonly operating 3+ channels simultaneously

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Step 3 Settlement staff manually cross-checking channel statements, fees, and promotional discounts

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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

Competitor references

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

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 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.

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	/ 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 (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	/ 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.

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