📊 WTP Analysis Plan

1.1 The Four Monetization Models Assessment

Objective: Classify each persona into one of the four monetization models to guide pricing strategy.

Model Definitions

• Maximizer (High Value, High Price): Premium customers willing to pay top prices
• Penetrator (High Value, Low Price): Value-seekers wanting quality at competitive prices
• Underdog (Low Value, Low Price): Budget-constrained customers with basic needs
• Champion (Low Value, High Price): Overpaying customers (rare, target for repositioning)

Classification Methodology

# Value Score (0-25 scale)
Value_Score = (Feature_Count × 2) + 
              (Performance_Priority × 3) + 
              (Developer_Tools × 3) +
              (Analytics_Priority × 2) +
              (Premium_Support × 4)

# Price Sensitivity Score (0-15 scale)
Price_Sensitivity = (7 - Budget_Tier) + 
                    (Low_Pricing_Priority × 3) +
                    (Lowest_Cost_Selection × 4) +
                    (Best_Value_Selection × 2)

Classification Rules

• Maximizer: Value Score > 15 AND Budget Tier ≥ $101+
• Penetrator: Value Score 8-20 AND Budget Tier $26-200
• Underdog: Value Score < 10 AND Budget Tier ≤ $50
• Champion: Investigate high spend + low value combinations

1.2 Feature-Value Hypothesis Testing

Objective: Categorize features as Table Stakes, Performance (differentiators), or Delighters.

The Three Feature Categories

1. Table Stakes: Features expected by all, don't justify premiums
   • Hypothesis: Selected equally across budget tiers (|r| < 0.10, selection>70%)
2. Performance Features: Features that increase WTP
   • Hypothesis: Positive correlation with budget tier (r > 0.25)
3. Delighters: Nice-to-have but don't drive WTP
   • Hypothesis: High selection but no WTP correlation (r ≈ 0)

Statistical Analysis Methods

• Point-biserial correlation with budget tier
• T-test: selectors vs. non-selectors
• Regression modeling to quantify budget increase per feature
• Association rules mining for feature bundles

2.1 WTP Distribution Analysis

Overall Distribution (Q24)

Descriptive Statistics: Mean, median, mode, SD, CV, IQR, skewness, kurtosis, percentiles (10th, 25th, 50th, 75th, 90th, 95th)

Budget Tier Mapping:

• $0-10 → $5
• $11-25 → $18
• $26-50 → $38
• $51-100 → $75.50
• $101-200 → $150.50
• $201-500 → $350.50
• $501+ → $600 (conservative)

WTP by Persona

• Descriptive statistics per persona
• One-way ANOVA (test if means differ)
• Post-hoc tests (Tukey HSD) for pairwise comparisons
• Within-persona variation (coefficient of variation)

Natural Price Tier Identification

• Gap analysis: Identify inflection points
• Density clustering: Find peaks and valleys
• Revenue optimization: Model revenue at different price points
• K-means clustering on budget tiers

2.2 Feature-Price Correlation Analysis

For Each Priority (Q7) and Extended Feature (Q14):

1. Binary correlation with budget tier
2. Mean budget comparison (selectors vs. non-selectors)
3. Chi-square test of independence
4. Feature count analysis (total features vs. budget)
5. Co-selection patterns (association rules)

Multi-Feature Regression Analysis

Budget = β₀ + Σ(β × Feature) + ε

Models:
- Linear Regression (OLS)
- Hierarchical (persona → priorities → features → interactions)
- Regularized (LASSO/Ridge) for feature selection
- Cross-validation (70/30 split)

Premium Support Analysis (Q12)

• Support type vs. budget correlation
• Willingness for premium/paid support
• Support-feature interactions
• Bundle vs. add-on pricing implications

2.3 Persona Monetization Profiles

For Each Persona:

1. WTP Characterization: Sample size, budget distribution, percentiles
2. Feature Preferences: Top priorities, most/least selected features
3. Budget-Feature Alignment: Feature_Value_Index = Value_sum / Actual_budget
4. Persona Sub-Segmentation: 2-3 sub-segments within persona
5. Pricing Strategy: Positioning, recommended tiers, feature packaging

2.4 Price Sensitivity Analysis

"Low Pricing" Priority Analysis (Q7)

• Actual WTP vs. stated preference
• Budget tier breakdown
• Feature expectations vs. budget gap
• Sub-segments: "Price-only", "Value-seekers", "Pragmatists"

Q8 Value-Seeking Behavior

WTP by selection: Premium features (highest) vs. Lowest cost (lowest)

Price Elasticity Indicators

• Budget spread as elasticity indicator
• Feature-budget gradient
• Persona elasticity comparison

3.1 Tiered Pricing Strategy

Tier Design Process

1. Determine Number: 3-4 tiers typical (Good-Better-Best-Premium)
2. Set Price Points: Percentile-based (25th, 50-60th, 80-90th)
3. Assign Personas: Map to primary/secondary tiers
4. Allocate Features: Table stakes in all; performance tier-specific

Good-Better-Best Psychology

• Anchor the high end (make others seem reasonable)
• Target the middle (40-50% of revenue)
• Make basic "good enough" (encourages upgrades)
• Clear differentiation (3-4 meaningful differences)
• Decoy effect (strategic tier positioning)

3.2 Value-Based Pricing Approach

Professional Tier Bundle Value:
Base (table stakes): $40
+ Feature 1: $48 (from regression)
+ Feature 2: $32
+ Feature 3: $25
= Total value: $145

Price at 65% of value = $94/month

Price Anchors

• Primary: Enterprise tier sets reference point
• Secondary: Show feature add-on values
• Competitive: Position relative to competitors

4.1 Statistical Methods

4.2 Data Preparation Steps

1. Load and validate data (check missing values, verify data types)
2. Create derived variables (feature counts, priority scores, value/sensitivity scores)
3. Handle categorical variables (create dummy variables for regression)
4. Create analytical datasets (feature-level, persona-level, segment-level)