EV Charging Business Revenue Analyzer

3.90

Derivation Chain

Step 1 Hyundai Ioniq 9 named Car of the Year / EV mass adoption

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Step 2 EV charging infrastructure expansion

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Step 3 Charging operator site selection & revenue simulation SaaS

Problem

With the Hyundai Ioniq 9 being named Car of the Year and EV adoption accelerating, small-to-mid-size charging operators (sole proprietors and small corporations) are rapidly increasing. However, when selecting charging station locations, operators must individually research nearby EV registrations, existing charger density, and power infrastructure costs. Over 40% of operators suffer monthly losses of 300,000–500,000 KRW (~$225–$375) per charger due to poor site selection.

Solution

Enter a candidate location and the system comprehensively analyzes EV registration density, competing charger status, power infrastructure costs, and projected utilization rates to simulate monthly revenue. Core features: (1) Public data-based analysis of EV registrations and charger density within a 1km radius, (2) KEPCO electricity rate tier-based cost calculation, (3) Time-of-day utilization forecasting and break-even point simulation. The differentiator is quantitative analysis based on public API data.

Target: Sole proprietors planning to enter the EV charging business, building owners with parking facilities, small charging network operators

Revenue Model: Per-analysis report at 59,000 KRW (~$44), monthly plan at 99,000 KRW/month (~$74, unlimited site analyses). Premium Plan at 190,000 KRW/month (~$142, includes revenue monitoring)

Ecosystem Role: Supplier

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

5.0/5

M Market

5.0/5

R Realizability

3.0/5

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 (67%)

Tech Complexity

29.3/40

Data Availability

17.9/25

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 (60/100)

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

7.0/10

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