B

Fandom Real-Time Reaction Collector

3.20

Derivation Chain

Step 1 Active K-drama and sports fandoms
→
Step 2 Demand for fan reaction data
→
Step 3 Multi-channel real-time fan reaction collection and analysis SaaS

Problem

Content planning teams at drama production companies, sports teams, and entertainment agencies need to monitor fan reactions during airings or games across 5+ channels simultaneously (X, YouTube comments, DCInside, TheQoo, Naver Cafe). Currently 2-3 interns manually screenshot and compile data, making real-time response impossible — crisis situations (e.g., actor departure controversies) take an average of 4-6 hours for initial response.

Solution

A SaaS that collects reactions from major fan communities in real-time, timed to drama airings or sports games, and auto-generates sentiment analysis, keyword clouds, and reaction trend graphs. Key features: (1) Multi-channel real-time crawling (X, YouTube, DCInside, TheQoo, Naver), (2) Auto-classification of positive/negative/crisis signals with Slack/KakaoTalk alerts, (3) Weekly and per-episode reaction trend reports.

Target: Drama production companies with 10-50 employees, small-to-mid entertainment agencies, professional sports team marketing departments
Revenue Model: SaaS monthly subscription: $142/project (5 channels). Additional channels: $22/month each. 15% discount on annual contracts.
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
4.0/5
M Market
4.0/5
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
32.0/40
Data Availability
17.5/25
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
6.2/20
Timing
16.0/20
Revenue Signals
10.5/15
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.
Total 100 points; below 50 is filtered out.

Technical Requirements

Data Pipeline [medium] AI/ML [low] Backend [medium] Frontend [low]
Dashboard