K League Fan Data Monetization Coach

3.00

Derivation Chain

Step 1 K League promotion playoff excitement and fandom surge

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Step 2 Club fan CRM/marketing tool

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Step 3 Fan data-based revenue conversion consulting SaaS

Problem

With the promotion of Daegu FC and other K League lower-division clubs experiencing a surge in fandom, small club operations teams (3-5 people) lack a CRM to systematically collect and analyze fan data after ticket sellouts, missing out on additional per-fan revenue (merchandise, F&B, sponsorship) during the season. The lost opportunity for additional revenue per club per season amounts to tens of millions of KRW annually, but there is no budget for a large-scale CRM.

Solution

Provide a fan data dashboard dedicated to K League clubs. (1) Automatically integrate ticket booking, merchandise purchase, and social media engagement data; (2) generate purchase conversion probability and recommended campaigns (merchandise bundles, season ticket upsells) by fan segment; (3) automatically generate fan profile reports for sponsor proposals. The differentiation is club-specific templates and integration with Korean payment data compared to existing large-scale CRMs.

Target: Marketing staff of K League 1 and 2 clubs (club operations teams of 3-10 employees), approximately 40 clubs affiliated with the professional football federation.

Revenue Model: SaaS monthly subscription: 290,000 KRW per club (approx. $217.50) per month, 20% discount for annual payment. Premium plan with automatic sponsor report generation: 490,000 KRW (approx. $367.50) per month.

Ecosystem Role: Supplier

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

3.0/5

M Market

3.0/5

R Realizability

3.0/5

V Validation

3.0/5

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 (69%)

Tech Complexity

29.3/40

Data Availability

20.0/25

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 (64/100)

Competition

10.0/20

Market Demand

20.0/20

Timing

14.0/20

Revenue Signals

7.5/15

Pick-Axe Fit

7.5/15

Solo Buildability

5.0/10

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] AI/ML [low]

Dashboard