Filming Location Tourism Demand Forecaster

3.05

Derivation Chain

Step 1 'The Man Who Lives with the King' surpasses 7M viewers → filming location tourism surge

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Step 2 Filming location municipality tourism marketing services

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Step 3 Content performance-based filming location tourism demand forecasting and response tool

Problem

When a drama or film becomes a hit, visitors surge to filming locations, but local governments (e.g., Mungyeong City) fail to predict the demand spike, causing infrastructure responses—parking, restrooms, guide staff—to lag by 2-3 weeks. As seen with the Mungyeongsaejae case from 'The Man Who Lives with the King' surpassing 7 million viewers, complaints were already flooding in before any response began, leading to recurring cycles of declining tourist satisfaction and resident conflicts.

Solution

A service that links film/drama box office metrics (advance ticket sales, social media buzz, search volume) with filming location data to forecast visitor influx timing and volume 2-4 weeks in advance, automatically generating infrastructure response playbooks for local governments. Core features: (1) Filming location visit demand prediction model based on content performance curves, (2) Auto-generated tourism infrastructure response checklists customized per municipality, (3) Real-time visitor monitoring via telecom carrier foot traffic data integration.

Target: Tourism department officials at filming location municipalities and marketing teams at regional tourism organizations

Revenue Model: Per-project report at 990,000 KRW (~$742), annual subscription at 299,000 KRW/month (~$224/month) for continuous monitoring + monthly reports, with government tourism promotion budget linkage

Ecosystem Role: Supplier

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

4.0/5

M Market

3.0/5

R Realizability

2.0/5

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.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (66%)

Tech Complexity

29.3/40

Data Availability

17.1/25

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 (51/100)

Competition

8.0/20

Market Demand

9.4/20

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] AI/ML [medium] Frontend [low]

Dashboard