📊 Data Visualizations

Professional Charts & Analysis Results

🎭 Persona-Based Visualizations Charts & Graphs for Business Segmentation
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Professional Data Visualizations

All visualizations are high-resolution (300 DPI) and ready for presentations. Click any chart to view full-size. All charts generated using Python's matplotlib and seaborn libraries.

1. WTP Distribution Analysis

WTP Distribution Analysis

Overall and persona-specific willingness-to-pay distributions

This comprehensive visualization shows:

  • Overall WTP histogram with mean ($119.61) and median ($38) markers
  • Box plots by persona showing significant variance (8× difference)
  • Violin plots revealing distribution shapes per persona
  • Cumulative distribution curve with percentile markers (25th, 50th, 75th, 90th)

Key Insight: Right-skewed distribution indicates premium opportunity with Enterprise willing to pay $368/month vs Hobbyists at $46/month.

2. Feature Correlations

Feature Correlation Analysis

Priority and extended feature impact on WTP

Four-panel analysis showing:

  • Priority Impact: 24/7 Support (+$88), Performance/Speed (+$43), Developer Tools (+$26)
  • Extended Features: E-commerce Tools (+$86), CDN (+$50), Email Hosting (+$34)
  • Regression Coefficients: Enterprise persona (+$223), Premium support willingness (+$62)
  • Model Comparison: Lasso regression shows best cross-validation performance (R²=0.508)

Key Insight: Performance features command measurable premiums - reserve for Business+ tiers.

3. Feature Classification Matrix

Feature Value Matrix

"Monetizing Innovation" feature value positioning

Matrix plotting features by selection rate vs. WTP impact:

  • Performance Features (Green): High WTP impact, justify premium pricing
  • Table Stakes (Blue): High selection, low WTP impact - include in all tiers
  • Delighters (Orange): Nice-to-have features with modest impact

Key Insight: Performance/Speed and 24/7 Support drive WTP premiums and should be tier-gated.

4. Persona Comparison

Persona Characteristics Comparison

Comprehensive persona analysis across multiple dimensions

Four-panel comparison showing:

  • Mean WTP by Persona: Enterprise ($368) >> Small Business ($60) >> Hobbyist ($46)
  • Features vs Budget: Correlation between feature count and willingness to pay
  • Sample Distribution: Small Business 31%, Agency 19%, Marketing 19%, Hobbyist 17%, Enterprise 14%
  • Price Sensitivity: Hobbyists most sensitive, Enterprise least sensitive

Key Insight: Clear market segmentation supports 4-tier pricing strategy targeting different personas.

5. Price Sensitivity Analysis

Price Sensitivity Segmentation

Market segmentation by price sensitivity

Comprehensive sensitivity analysis:

  • Low Pricing Priority Impact: -$73 budget difference vs others
  • Q8 Value Behavior: Template users show highest WTP ($177), one-click seekers lowest ($88)
  • Segment Distribution: 60% Low Sensitivity (mean $182), 40% Medium Sensitivity (mean $29)
  • Score Distribution: Bimodal distribution with clear segmentation thresholds

Key Insight: 60% of market is value-focused (not price-focused) - focus premium positioning here.

6. Monetization Models

Four Monetization Models Classification

Four Monetization Models framework application

Classification and revenue analysis:

  • Penetrator (55%): 277 customers, $27,914/month potential - largest revenue opportunity
  • Champion (26%): 128 customers, $14,391/month - risk segment needing optimization
  • Maximizer (8%): 38 customers, $16,460/month - premium positioning
  • Underdog (11%): 57 customers, $1,042/month - entry segment

Key Insight: Optimize Professional tier for Penetrators (high value needs, competitive price expectations).

📈 Chart Generation Details

Technical Specifications

  • Resolution: 300 DPI (print-ready quality)
  • Format: PNG with transparency support
  • Dimensions: 1200×800 pixels (standard), 1600×1200 (multi-panel)
  • Libraries: matplotlib 3.5+, seaborn 0.11+
  • Style: Professional whitegrid theme with custom color palettes
  • Accessibility: Color-blind friendly palettes used

Regeneration Instructions

All visualizations can be regenerated by running the analysis script:

cd /Users/dkuciel/Visual\ Studio\ Code/2025-11\ WTP\ 2.0
python3 analyze_wtp.py

Charts will be saved to the analysis/ directory with identical filenames.