🎲 Response Generation Strategy

Statistically Valid Synthetic Survey Responses

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Overview

Strategy for generating realistic synthetic responses for the Web Hosting Market Segmentation Survey.

24 questions, 5 personas, 73% target qualification rate with 20+ consistency rules.

1. Personas & Distributions

Primary Personas (Q1 Distribution)

Persona Weight Mean Budget Mean Sites Characteristics
Small Business Owner 28% $25/mo ~4 sites WordPress-heavy, value-conscious
Marketing Professional 18% $50/mo ~12 sites Analytics-driven, SEO-focused
Agency/Freelance 22% $75/mo ~25 sites Technical, needs dev tools
Enterprise 12% $300/mo ~50 sites Performance-critical, high budget
Hobbyist 15% $12/mo ~2 sites Price-sensitive, exploring options
None of above 5% — — DISQUALIFIED

2. Key Distributions

Qualification Flow (Target 73%)

  • Q2: 60% primary, 30% part of, 10% not involved (DQ)
  • Q3: 85% No, 12% Yes (DQ), 3% Not sure

Technology (Q4)

WordPress 40% | Custom 18% | AI Builder 16% | JAMstack 12% | Static 7% | Hire 5% | Other 2%

Volume (Q5) - Log-normal by persona

1-5 sites: 50% | 6-10: 20% | 11-25: 15% | 26-50: 10% | 51+: 5%

Budget (Q24) - Aggregate

$0-10: 20% | $11-25: 25% | $26-50: 20% | $51-100: 15% | $101-200: 12% | $201-500: 6% | $501+: 2%

3. Multi-Select Patterns

Q7 Priorities (up to 3)

Selection count: 15% one, 35% two, 50% three

Top combos:

  • "Easy+Low Pricing+Performance" (18%)
  • "Performance+Support+Dev" (12%)
  • "Analytics+Performance+SEO" (10%)

Q14 Extended Features (up to 3)

Selection count: 18% one, 38% two, 44% three

Popularity: Email 65% | Domain 55% | CDN 42% | eCommerce 38% | SEO 35% | Database 32% | Builders 28%

Conditional Logic

  • Q7 selections trigger Q8-Q13 (45-70% trigger rates)
  • Q14 selections trigger Q15-Q21 (28-65% trigger rates)
  • Q1 Agency/Marketing triggers Q22 (~40% of responses)

4. Budget Calculation Formula

final_budget = base_persona_budget × feature_multipliers × consistency_adjustments

Feature Multipliers (cumulative)

  • Low Pricing (Q7): 0.7×
  • 24/7 Support (Q7): 1.3×
  • 3 features (Q14): 1.2×
  • eCommerce (Q14): 1.5×
  • Premium support (Q12): 1.4×
  • 4+ agency features (Q22): 1.6×
  • Developer tools (Q7): 1.2×

Key Correlations

  • Low Pricing → Budget: r = -0.45
  • 24/7 Support → Budget: r = +0.38
  • Feature count → Budget: r = +0.42
  • Premium support → Budget: r = +0.48

5. Consistency Rules (20 rules with exception rates)

Budget Consistency

  1. IF Q8 "Lowest Cost" THEN Q24 bottom 40% (5% exceptions)
  2. IF Q12 any premium THEN Q24 ≥ median (8% exceptions)
  3. IF Q14 3 high-value features THEN Q24 top 60% (10% exceptions)

Role-Feature Alignment

  1. IF Q4 WordPress THEN Q12 "WP Specialists" 2× likely (45%→70%)
  2. IF Q4 JAMstack/Custom THEN Q7 "Dev Tools" 2.5× likely (30%→75%)

Cross-Question Logic

  1. IF Q7 "Analytics" THEN Q11 high engagement + Q14 "SEO" +20%
  2. IF Q7 "Support" THEN Q12 75% engage + Q24 top 50%
  3. IF Q7 "Low Pricing" THEN Q8 60% "Lowest Cost" + Q24 bottom 50%

6. Edge Cases & Variance (18-27% total)

Contradictory (5-8%)

  • Budget-priority mismatch (3%): Low pricing but high budget
  • Feature-budget mismatch (2%): Premium features but low budget
  • Support contradiction (5%): Support priority but no premium selections

Minimal Engagement (8-12%)

  • Minimum selections (5%): One option in all multi-selects
  • "Don't know" tendency (4%): Not sure + minimal selections
  • Incomplete conditionals (3%): Trigger but minimal engagement

Power Users (5-8%)

  • Maximum engagement (4%): Max selections + high budget
  • Technical expert (3%): JAMstack + all dev tools
  • Agency power user (2%): 30+ sites + all agency features

Survey Fatigue (5-7%)

  • Decreasing engagement (4%): Normal→reduced→minimal
  • Straight-lining (2%): First options consecutively
  • Satisficing (3%): Safe middle options

7. Implementation Approach

Stack

Python + pandas + NumPy + SciPy

Rationale: Strong statistics, tabular data handling, easy export

Algorithm

  1. Select persona (weighted random)
  2. Apply cross-persona modifiers
  3. Generate Q1-Q3 (qualification)
  4. If disqualified, stop
  5. Generate Q4-Q5 (tech/volume)
  6. Generate Q6-Q7 (purpose/priorities)
  7. Generate Q8-Q13 conditionals
  8. Generate Q14 (features)
  9. Generate Q15-Q21 conditionals
  10. Generate Q22 if applicable
  11. Generate Q23
  12. Calculate Q24 with multipliers
  13. Apply edge case patterns
  14. Validate consistency
  15. Export

Validation

  • Statistical: Chi-square tests, correlation validation, qualification rate ~73%
  • Business logic: Consistency rules, conditional accuracy, persona profiles
  • Realism: Edge case frequencies, manual review, domain expert feedback

8. Success Metrics

  • Qualification rate: 73% ±3% ✓ Achieved
  • Persona distribution: ±5% of target ✓ Achieved
  • Budget correlations: ±0.05 of target ✓ Achieved
  • Conditional logic: 100% accuracy ✓ Achieved
  • Consistency exceptions: ±2% of rule targets ✓ Achieved
  • Edge cases: Within specified ranges ✓ Achieved

Quick Reference

Persona Budgets (medians): HOB $6 | SBO $12 | MKT $25 | AGN $40 | ENT $150

Multi-select Limits: Q7 up to 3 | Q14 up to 3 | Q23 up to 2

Conditionals: Q7→Q8-13 | Q14→Q15-21 | Q1(Agency/Mkt)→Q22

View Survey Structure → See Implementation →