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Overview

An A/B Testing Assistant powered by Pylar analyzes experiment data, calculates statistical significance, and provides clear recommendations on which variant performs better.

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What the Agent Needs to Accomplish

The agent must:
  • Analyze A/B test results
  • Calculate statistical significance
  • Compare variant performance
  • Recommend winning variants
  • Track experiment progress
  • Identify significant differences

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How Pylar Helps

Pylar enables the agent by:
  • Unified Experiment View: Combining experiment data, user behavior, and conversion data
  • Statistical Analysis: Automated significance calculations
  • Real-time Monitoring: Querying current experiment performance
  • Clear Recommendations: Data-driven variant recommendations

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Without Pylar vs With Pylar

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Without Pylar

Challenges:
  • ❌ Manual statistical calculations
  • ❌ Complex experiment data aggregation
  • ❌ Time-consuming analysis
  • ❌ Limited real-time monitoring
Implementation Complexity: ~4-5 weeks

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With Pylar

Benefits:
  • ✅ Automated statistical analysis
  • ✅ Real-time experiment monitoring
  • ✅ Clear winner recommendations
  • ✅ Easy experiment tracking
Implementation Complexity: ~5-6 hours

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Step-by-Step Implementation

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Step 1: Connect Data Sources

  1. Connect Experiment Platform (A/B test data, variants)
  2. Connect Analytics (User behavior, conversions)
  3. Connect Product Data (Feature usage, engagement)

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Step 2: Create Experiment Views

Experiment Results View: 
CREATE VIEW experiment_results AS
SELECT 
  e.experiment_id,
  e.experiment_name,
  e.variant,
  COUNT(DISTINCT u.user_id) as participants,
  COUNT(DISTINCT CASE WHEN u.converted THEN u.user_id END) as conversions,
  (COUNT(DISTINCT CASE WHEN u.converted THEN u.user_id END) / 
   NULLIF(COUNT(DISTINCT u.user_id), 0)) * 100 as conversion_rate,
  AVG(u.time_on_page) as avg_time_on_page,
  AVG(u.engagement_score) as avg_engagement
FROM experiments.experiments e
JOIN experiments.users u ON e.experiment_id = u.experiment_id
GROUP BY e.experiment_id, e.experiment_name, e.variant;

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Step 3: Create MCP Tools

Tool 1: Analyze Experiment
  • analyze_experiment(experiment_id: string)
Tool 2: Check Statistical Significance
  • check_significance(experiment_id: string, confidence_level: number)
Tool 3: Recommend Winner
  • recommend_winner(experiment_id: string)
Tool 4: Monitor Experiment
  • monitor_experiment(experiment_id: string, check_interval: number)

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Example Agent Interactions

User: “What’s the status of the homepage headline test?” Agent: “Homepage Headline Test Results:
  • Variant A: 12.5% conversion (2,450 participants)
  • Variant B: 14.8% conversion (2,380 participants)
  • Statistical Significance: 95% confidence
  • Winner: Variant B (18% improvement)
  • Recommendation: Deploy Variant B”

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Outcomes

  • Decision Speed: 60% faster test decisions
  • Accuracy: 95% confidence in recommendations
  • Testing Efficiency: 2x more tests run
  • Conversion Improvement: 15% average lift from tests

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Next Steps