B

Expert Lecture Fee Benchmark

3.30

Derivation Chain

Step 1 Career-based re-employment matching
→
Step 2 Freelancer/consulting opportunity discovery
→
Step 3 Unknown appropriate pricing in the lecture/mentoring market beyond consulting

Problem

When professionals in their 50s with 20-30 years of experience try to start corporate training, university guest lectures, or online courses after retirement, there is no standard for appropriate lecture fees. Korea Human Resources Development Service instructors earn 100,000-150,000 KRW (~$75-$112) per hour, executive guest lectures at large corporations cost 1,000,000-3,000,000 KRW (~$750-$2,250) per session, and online courses pay 50,000-200,000 KRW (~$37-$150) per transaction — the variance is extreme. First-time lecturers repeatedly either undervalue themselves and lose confidence, or price too high and miss opportunities.

Solution

On a web interface, users enter their career details (industry, position, years of experience), lecture type (corporate training/university guest lecture/online), and subject area. The service then: (1) displays the fee range (25th-75th percentile) of lecturers with similar profiles, (2) applies bonus points for career factors (certifications, publications, awards, etc.), (3) provides a first-lecture proposal template and fee negotiation guide.

Target: Ages 50-60, experienced professionals who retired from large corporations or public institutions and are starting lecture/consulting activities, with 1-2 or fewer lectures given so far
Revenue Model: Basic fee lookup Free, custom proposal template + fee negotiation guide bundle $7.40 (9,900 KRW), Premium Plan (lecture record management + automatic tax calculation) Monthly Subscription $11.20 (14,900 KRW)
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
3.0/5
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.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (72%)

Tech Complexity
29.3/40
Data Availability
22.5/25
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 (56/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
14.0/20
Revenue Signals
7.5/15
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

Frontend [low] Backend [medium] Data Pipeline [medium]
Dashboard