Our Commitment to Transparency
We believe in radical transparency. Every metric, every challenge, every success and failure is documented here. This transparency serves two purposes:
- Proves AppScout's Value: Real data showing AI insights translate to real revenue
- Helps the Community: Learn from our journey, both wins and mistakes
Updated: Weekly, every Monday at 9am PT
Portfolio Overview
Current Status (As of December 2025)
Active Apps: 1 Total MRR: $0 (Pre-launch) Total Installs: 0 (Beta phase) Average Insight Accuracy: TBD (First app launching)
MatrixFit Recommender
Status: Launching January 2026 AppScout Confidence Score: 87%
Current Metrics
Development Phase:
- Started: November 15, 2025
- Target Launch: January 15, 2026
- Development Time: 8 weeks (on track)
Beta Program:
- Beta Merchants: 5
- Applications Reviewed: 23
- Acceptance Rate: 22%
- Beta Feedback Score: 4.8/5
Early Results (Beta Phase):
- Average Return Reduction: 22%
- Average Conversion Increase: 12%
- Recommendation Acceptance Rate: 76%
- Widget Engagement Rate: 68%
Target Metrics (6 Months Post-Launch)
- MRR Target: $5,000
- Install Target: 100 merchants
- Churn Target: <5% monthly
- NPS Target: 50+
Development Investment
Time Investment:
- Product Strategy: 40 hours
- Development: 320 hours
- Design: 40 hours
- Content/Docs: 60 hours
- Total: 460 hours
Financial Investment:
- Development Labor: $46,000 (est.)
- Infrastructure: $500
- Tools & Services: $1,200
- Total: $47,700
Target ROI Timeline: Break even at month 8
Insight Validation Tracking
MatrixFit: AI Prediction vs Reality
| Metric | AI Prediction | Validation Data | Accuracy |
|---|---|---|---|
| Market Size | 15,000 potential merchants | TBD post-launch | - |
| Willingness to Pay | $49-99/month | Validated in interviews | ✓ |
| Problem Severity | 30-40% returns | Confirmed (32% average) | ✓ |
| Demand Frequency | 62% cite sizing issues | Confirmed (58% in interviews) | 94% |
| Competition Gap | Mid-market underserved | Confirmed | ✓ |
Current Validation Score: 94% (4/4 measurable predictions accurate)
Key Learnings
What's Working
Week 1-2 (Discovery):
- Merchant interviews confirmed AI predictions with 94% accuracy
- Pain point severity higher than anticipated
- Price point validated strongly
Week 3-4 (Planning):
- Technical architecture decisions documented
- MVP scope defined clearly
- Beta program designed
Week 5-6 (Core Development):
- Algorithm development on schedule
- Early testing showing promising accuracy
- Performance metrics meeting targets
Week 7-8 (Beta Launch):
- Beta merchant onboarding smooth
- Early results exceeding expectations
- Feature requests aligned with roadmap
Challenges Faced
Technical:
- Size normalization across brands more complex than expected
- Required additional data model iteration
- Solution: Extended beta phase by 2 weeks
Market:
- Higher beta application rate than anticipated
- Had to be more selective for quality feedback
- Solution: Implemented structured application process
Resource:
- Content creation taking longer than budgeted
- Documentation needs exceeded estimates
- Solution: Created reusable templates
Monthly Reports
December 2025
Focus: Beta testing and launch preparation
Accomplishments:
- Completed 5 beta merchant onboardings
- Achieved 76% recommendation acceptance rate
- Documented 22% average return reduction
- Prepared launch content and materials
Challenges:
- Extended beta by 2 weeks for additional data
- Size normalization complexity
- Merchant onboarding time longer than expected
Next Month Goals:
- Public launch on Shopify App Store
- First 10 paying customers
- Launch marketing campaign
- Begin public metrics tracking
Transparency Metrics
Content Publishing
Development Diary Posts: 8 (target: bi-weekly) Case Studies: 0 (target: 1 at 6 months) Weekly Metrics Updates: 4 (launched in December)
Community Engagement
Email Subscribers: TBD (launching with website) Social Media Followers: TBD Blog Post Views: TBD App Page Views: TBD
Future Apps Pipeline
In Validation
- App #2: Under merchant interviews (Confidence: 84%)
- App #3: Initial validation planning (Confidence: 89%)
Discovery Phase
- 12+ opportunities identified with 80%+ confidence
- 3 selected for validation interviews in Q1 2025
Methodology
How We Calculate Metrics
MRR (Monthly Recurring Revenue):
- Calculated as: (Active Subscriptions × Plan Price)
- Reported as trailing 30-day average
- Excludes one-time fees
- Accounts for plan changes
Churn Rate:
- Calculated monthly as: (Cancellations / Active Subscribers)
- Voluntary churn reported separately from involuntary
- Cohort analysis provided quarterly
Insight Accuracy:
- Compares AI predictions against actual market data
- Only includes measurable, specific predictions
- Calculated as percentage of accurate predictions
- Updated quarterly with new data
Installation Metrics:
- Active installs: Currently installed and active
- Total installs: All-time installation count
- Uninstall rate: Percentage who uninstall
Update Frequency
- Real-time Metrics: Available in app dashboards
- Public Dashboard: Updated every Monday
- Monthly Deep-Dive: Published first Monday of month
- Quarterly Review: Comprehensive analysis every 3 months
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Questions?
We're happy to explain any metric or methodology in detail.
Contact: labs@appscout.io
Historical Data
As we grow, this section will contain month-over-month comparisons, trend analysis, and historical performance data.
Coming Soon:
- MRR growth charts
- Customer acquisition trends
- Churn analysis
- Insight accuracy evolution
- ROI tracking
Last Updated: December 22, 2024 Next Update: December 30, 2024