B

Box Office-Linked Pop-up Location Recommender

3.05

Derivation Chain

Step 1 Box office hit 'The King and the Man' with 7 million+ admissions
→
Step 2 Surge in movie IP pop-up store businesses
→
Step 3 Lack of data for pop-up operators to select optimal location and timing

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

Small planning agencies (1-5 people) operating movie and drama IP pop-up stores spend 2-3 weeks individually researching foot traffic, target age density, rent, and competing pop-up status when selecting a pop-up location. When the box office curve and pop-up opening timing are misaligned, more than 30% of openings occur after audience interest has waned, resulting in sales of only 40-50% of expectations.

Solution

Track movie and drama box office curves in real time and recommend optimal pop-up opening timing. Compare foot traffic (based on SKT/KT telecom data), target age ratio, rent, and current competing pop-up status for major pop-up spaces in Seoul and the metropolitan area on a map. Provide a simulation linked to remaining box office life: 'If you open at this location at this time, expected visitors: X'.

Target: Small agencies (1-5 people, including freelancers) planning and operating movie and drama IP pop-up stores
Revenue Model: Per Transaction Report: 29,000 KRW (approx. $22) per movie + 3 location comparisons. Monthly subscription: 99,000 KRW (approx. $74) for unlimited searches + real-time alerts. 20% discount for annual payment.
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 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.
N=.15 U=.20 M=.15 R=.30 V=.20

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/ 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.

Market Validation (51/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
5.0/10
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.

Technical Requirements

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