QRGuard
Executive Summary
Vision Statement
QRGuard becomes the mandatory pre-press checkpoint for every marketing campaign worldwide, saving agencies millions while making interactive print advertising foolproof and data-driven.
Problem Summary
Marketing teams running OOH, print, and billboard campaigns face catastrophic financial risks from tiny errors in interactive elements like QR codes and links. A single broken QR code pointing to a 404 page can cost $20K+ in reprints and reinstalls, as seen in real agency failures with McDonald's tray liners and programmatic ad overspends. Static QR codes are permanent—once printed, wrong destinations can't be fixed without massive expense[1][2].
Proposed Solution
QRGuard is a dynamic QR code platform that generates editable QR codes post-print, combined with automated pre-flight testing for all interactive elements in final artwork. Upload designs, scan/click everything automatically, track scans in real-time, and redirect URLs instantly if issues arise—eliminating reprint nightmares.
Market Analysis
Target Audience
Primary Persona: Sarah, Marketing Operations Manager
- Age: 28-42
- Role: Handles campaign execution at mid-sized agencies ($5-50M revenue) or in-house marketing teams
- Pain Points: Final approvals under deadline pressure, coordinating with creative agencies, explaining $20K+ reprint costs to executives
- Behaviors: Active in r/advertising, r/marketing, uses Canva/Figma for proofs, stressed about QR codes on OOH/print
- Budget: $50-500/month per tool
- Tech Stack: Google Workspace, Slack, Asana, creative tools
Secondary: Freelance designers and small agency owners doing OOH work.
Niche Validation
The Reddit post (22 upvotes, 85% ratio, 22 comments) shows genuine validation—OP nearly lost $20K from a QR code pointing to staging, built their own tool, and sparked 22 stories of similar disasters (McDonald's legal disclaimer error cost $500K, programmatic overspends). Top comments validate static QR risks and agency proofing failures. Web research confirms this as a widespread issue in OOH/print marketing[1][2].
Google Trends Keywords
Market Size Estimation
$1.2B: US/EU agencies & brands running 1,000+ print/OOH campaigns annually needing proofing tools.
$12M: 2,000 early adopters (mid-size agencies) at $500 ARPU paying $50-200/month.
$10B+: Global OOH advertising market ($20B in 2024) where 60%+ use QR codes/interactives, plus $100B+ print marketing[1].
Competitive Landscape
Static QR generators abound (free tools), but dynamic solutions like Bitly and Beaconstac focus on creation/tracking, not pre-print proofing. No dedicated 'final artwork tester' found. QR code platforms lack automated scanning of full campaign files (multiple QRs/links). Gap: Post-print editing + pre-flight automation.
Product Requirements
User Stories
As a marketing manager, I want to upload final artwork files so QRGuard can auto-detect and test all interactive elements.
As an agency lead, I want one-click URL redirects on dynamic QRs so I can fix post-print without recalls.
As a team member, I want approval workflows with scan reports so nothing ships broken.
As an ops user, I want scan analytics dashboards so I can optimize future campaigns.
MVP Feature Set
- File Upload & Auto-Scan: Detect/test all QRs, links, phone numbers in PDFs/images
- Dynamic QR Generator: Create editable codes with bulk upload
- Real-time Redirect Dashboard: Change destinations instantly
- Basic Reporting: Pass/fail results + screenshots of failures
- Team Sharing: Invite-only campaign folders
Non-Functional Requirements
- 99.9% uptime for redirect service (mission critical)
- Process 100MB files <30s with parallel QR detection
- SOC2 compliant for agency data security
- Mobile-responsive for on-site proofing
- API rate limited to prevent abuse
Key Performance Indicators
Activation: 70% upload first file & get passing scan report
Retention: 40% D30 (teams return for 2nd campaign)
Revenue: $50 avg. first month ARPU
Virality: 0.3 k-factor from agency shares
Customer Health: <5% campaigns flagged post-launch
Data Visualizations
Visual Analysis Summary
Charts visualize QR failure cost distribution from Reddit comments and dynamic QR adoption trends to prove market urgency and growth potential.
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Go-to-Market Strategy
Core Marketing Message
'Never reprint $20K+ again. Test every QR/link automatically. Edit post-print.'
Initial Launch Channels
- Targeted Reddit: Posts in r/advertising, r/marketing, r/OOH with 'I fixed my $20K QR mistake—free tool'
- Product Hunt: Launch as 'QR code reprint killer'
- LinkedIn/Twitter: Target 'Marketing Ops Manager' + 'OOH agency' with case studies from post comments
Strategic Metrics
Problem Urgency
High
Solution Complexity
Medium
Defensibility Moat
Data moat: Aggregate anonymized scan failure data across campaigns to AI-train better error detection. High switching costs: Once teams upload campaign libraries and set redirects, they're locked in. Network effects: Agency directories sharing pre-vetted proofs.
Source Post Metrics
Business Strategy
Monetization Strategy
Freemium + Tiered SaaS:
- Free: 5 dynamic QRs/month, basic testing
- Pro ($49/mo): Unlimited, team collab, scan analytics
- Agency ($199/mo): White-label, API, bulk upload Upsell: Custom QR design ($10/mo), enterprise compliance checks.
Financial Projections
Year 1: 200 Pro users ($10K MRR) + 20 Agency ($4K) = $14K MRR. Year 2: 1,000 Pro + 100 Agency = $70K MRR. Based on r/advertising (250K members) + Product Hunt launch, 1% conversion from validated pain.
Tech Stack
Node.js + Express for QR generation/redirects, BullMQ for async scan testing.
PostgreSQL for campaigns + Redis for scan analytics caching.
Next.js 14+ for fast dashboard with drag-drop file upload and real-time preview.
Google Vision API (QR/link detection), Stripe (payments), AWS S3 (file storage), SendGrid (alerts), OpenAI (AI proofing suggestions).
Risk Assessment
Identified Risks
1. Agencies stick to manual processes (entrenched habits) 2. QR detection misses edge cases (low quality scans, artistic QRs)
Mitigation Strategy
1. Free tier + case studies from Reddit post disasters 2. Human review fallback + continuous training on uploaded failures