B

Anonymous Career Transition Archive

3.20

Derivation Chain

Step 1 Career transition matching
→
Step 2 Network-dependent career transition information
→
Step 3 Anonymous sharing of actual post-career paths by industry and position peers

Problem

Pre-retirees aged 50-60 at large or mid-sized companies want to know 'where did people with similar careers go after leaving,' but this information circulates only through personal networks. LinkedIn adoption among Koreans in their 50s is under 5%, and job portal reviews skew toward people in their 20s-30s. As a result, they spend 6-12 months exploring career transition paths and end up choosing suboptimal options from a limited set of alternatives they're aware of.

Solution

Builds an anonymous database where contributors share 'previous industry/position/tenure → post-retirement path (re-employment, entrepreneurship, Freelancer, teaching) → current satisfaction and income level.' Searchers filter and view career transition cases from profiles similar to their own. A contribute-to-access model rewards case contributors with credits to view other cases, driving data accumulation.

Target: Workers aged 50-60 with 20+ years at large or mid-sized companies, 1-3 years before retirement, exploring career transition paths but with limited people to ask
Revenue Model: Contribute 1 case to view 5 cases Free (contribute-to-access model). Unlimited access at $3.70/month (~4,900원). Future B2B Recruitment data offering for companies and headhunters
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
3.0/5
M Market
2.0/5
R Realizability
3.0/5
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 (70%)

Tech Complexity
29.3/40
Data Availability
20.6/25
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 (50/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
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

Backend [medium] Frontend [medium] Infrastructure [low]
Dashboard