B

Small Business Marketing ROI Tracker

3.05

Derivation Chain

Step 1 Solo Entrepreneur digital operations
→
Step 2 Unable to measure effectiveness by marketing channel
→
Step 3 ROI comparison across delivery app ads, SNS, blog campaigns, and flyers

Problem

A 55-year-old Solo Entrepreneur running a Cafe or Restaurant after retirement spends 200,000–500,000 KRW ($150–$375) monthly on marketing — Baemin Ultra Call (88,000 KRW/$66 per month), Instagram ads (100,000–300,000 KRW/$75–$225 per month), blog review campaigns (30,000–50,000 KRW/$22–$37 each), and flyers (150,000 KRW/$112 per 10,000 copies) — but has no way to measure which channel actually drives sales. Marketing budgets are allocated by gut feeling, and money likely keeps flowing to ineffective channels.

Solution

Users input monthly spend per marketing channel along with corresponding revenue changes. The tool visualizes estimated contribution and cost-efficiency per channel. It provides A/B test guides simulating questions like 'How much would revenue drop if I cancel Baemin Ultra Call this month?' and compares results against anonymous benchmark data from similar businesses in the same industry and area.

Target: Ages 50–62, Solo Entrepreneurs in Food Service, Cafe, or retail, spending 200,000 KRW ($150)+ monthly on marketing, unsure which channels are effective
Revenue Model: Basic channel comparison (2 channels) Free, full channel analysis + benchmarks at 9,900 KRW ($7.50)/month, 1-on-1 marketing consultant referral at 49,000 KRW ($37) Per Transaction
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
3.0/5
M Market
3.0/5
R Realizability
4.0/5
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 (69%)

Tech Complexity
24.0/40
Data Availability
25.0/25
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 (51/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
9.0/15
Pick-Axe Fit
9.0/15
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

Frontend [medium] Backend [medium]
Dashboard