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📈 SaaS CAC-LTV Model Analysis

Comprehensive Customer Acquisition Cost (CAC) and Lifetime Value (LTV) Analysis with Interactive Dashboard

A full-stack business intelligence platform that transforms raw customer data into actionable performance marketing insights for SaaS businesses.

🌐 Live Demo

🚀 Interactive Dashboard: https://mzhyi8c17zzl.manus.space/

📊 Key Performance Insights

  • LTV:CAC Ratio: 2.67:1 overall performance
  • Champion Channel: Referral Program (6.32x ROI)
  • Customer Analysis: 1,000 customers across 6 global markets
  • Revenue Analysis: $524k total revenue analyzed
  • Market Coverage: ARPU ranging from $21.64 (Africa) to $65.38 (Europe)

🎯 Features

Advanced Performance Marketing Intelligence

  • ARPU vs CAC Efficiency Matrix: Channel-specific revenue quality analysis
  • CAC Payback Period Analysis: 1.2 months (Referral) to 5.7 months (Paid Search)
  • LTV:CAC Ratio Optimization: Tier-based channel performance scoring
  • Marketing Spend Planning Framework: Data-driven budget allocation strategies

Interactive Dashboard

  • Real-time KPI metrics across 6 global markets
  • Interactive channel performance analysis with ROI calculations
  • Regional market insights with ARPU breakdowns
  • Cohort retention analysis with predictive modeling
  • Dynamic filtering by channels and regions

Executive Communication

  • Performance marketing-focused CEO briefings
  • Board-ready reporting with industry benchmarks
  • Professional PowerPoint presentations
  • Technical documentation and API framework

🔧 Technical Stack

  • Backend: Python Flask with Pandas analytics
  • Frontend: HTML5, CSS3, JavaScript with Chart.js
  • API: RESTful design with 6 real-time endpoints
  • Data Processing: NumPy, Pandas, Matplotlib, Seaborn
  • Performance: Optimized for executive use (<500ms response)

📁 Project Structure

CAC_LTV_Model_Analysis/
├── cac_ltv_analysis.py          # Core analysis and visualizations
├── dashboard_api.py             # Flask REST API backend
├── templates/dashboard.html     # Interactive frontend
├── cac_ltv_model.csv           # Sample dataset (1,000 customers)
├── CEO_PR_MESSAGE.md           # Executive performance marketing brief
├── DASHBOARD_SHARE_LINK.md     # Public sharing documentation
├── comprehensive_fact_check.py  # Data verification system
├── requirements.txt            # Python dependencies
├── plots/                      # Generated visualizations
│   ├── plot1_cohort_heatmap.png
│   ├── plot2_ltv_vs_cac.png
│   ├── plot3_ltv_cac_ratio.png
│   └── plot4_arpu_by_region.png
└── presentations/              # Executive materials
    ├── SaaS_Dashboard_Fixed.pptx
    └── saas-dashboard-presentation.html

🚀 Quick Start

1. Clone Repository

git clone https://github.com/419vive/CAC_LTV_Model_Analysis.git
cd CAC_LTV_Model_Analysis

2. Install Dependencies

pip install -r requirements.txt

3. Run Analysis

python cac_ltv_analysis.py

4. Launch Dashboard

python dashboard_api.py

Visit http://localhost:5001 to view the interactive dashboard.

5. Verify Data Integrity

python comprehensive_fact_check.py

📈 Business Impact

Channel Performance (ROI)

  1. Referral Program: 6.32x ROI ($52.91 CAC, $334.14 LTV)
  2. Organic Search: 4.39x ROI ($77.12 CAC, $338.48 LTV)
  3. Direct Traffic: 3.58x ROI ($91.38 CAC, $327.46 LTV)
  4. Email Marketing: 2.99x ROI ($114.94 CAC, $343.40 LTV)
  5. Paid Social: 1.92x ROI ($180.27 CAC, $346.75 LTV)
  6. Paid Search: 1.36x ROI ($237.52 CAC, $323.10 LTV)

Regional Market Opportunities

  • Europe: $65.38 ARPU (Premium market)
  • North America: $52.59 ARPU (Mature market)
  • Middle East: $49.54 ARPU (Developing market)
  • Asia Pacific: $40.36 ARPU (Growth market)
  • Latin America: $30.69 ARPU (Emerging market)
  • Africa: $21.64 ARPU (Early-stage opportunity)

🎯 API Endpoints

The Flask backend provides 6 real-time endpoints:

  • GET /api/summary - Overall business metrics
  • GET /api/channels - Channel performance analysis
  • GET /api/regions - Regional ARPU breakdown
  • GET /api/cohorts - Customer retention analysis
  • GET /api/filter - Dynamic filtering capabilities
  • GET /api/trends - Performance trend analysis

💼 Executive Documentation

🔍 Data Verification

All metrics are verified through comprehensive fact-checking:

  • Cross-file consistency validation
  • API endpoint accuracy verification
  • Mathematical precision confirmation
  • Business logic integrity checks

Run python comprehensive_fact_check.py for complete data verification.

🎨 Visualizations

The analysis generates professional visualizations:

  • Cohort Retention Heatmap: Customer behavior over time
  • LTV vs CAC Comparison: Channel efficiency analysis
  • LTV:CAC Ratio Chart: ROI performance ranking
  • Regional ARPU Analysis: Market opportunity mapping

📊 Performance Marketing Insights

CAC Payback Analysis

  • Referral: 1.2 months (exceptional)
  • Organic: 1.7 months (excellent)
  • Direct: 2.1 months (good)
  • Email: 2.6 months (acceptable)
  • Paid Social: 3.9 months (concerning)
  • Paid Search: 5.7 months (dangerous)

Budget Allocation Framework

  • 60% to channels with <2 month payback
  • 30% to channels with 2-3 month payback
  • 10% to channels with >3 month payback

🏆 Professional Impact

This platform demonstrates:

  • Full-stack development expertise
  • Business intelligence capabilities
  • Executive communication skills
  • Production-ready architecture
  • Data-driven optimization strategies

📄 License

This project is open source and available under the MIT License.

👤 Author

Jerry Lai - Data Science & Engineering Portfolio

  • GitHub: @419vive
  • Project Type: SaaS Business Intelligence Platform

Transforming raw customer data into actionable performance marketing intelligence for sustainable SaaS growth.