> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pylar.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# A/B Testing Assistant

> Build an AI agent that analyzes experiments, determines statistical significance, and recommends winners

## Overview

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

## 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

## 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

```mermaid theme={null}
graph LR
    subgraph "Data Sources"
        EXPERIMENTS[Experiment Data]
        BEHAVIOR[User Behavior]
        CONVERSIONS[Conversions]
    end
    
    PYLAR[Pylar<br/>Unified Views]
    
    TOOLS[MCP Tools<br/>analyze_results<br/>calculate_significance<br/>recommend_winner]
    
    AGENT[AB Testing Assistant]
    TEAM[Product Team]
    
    EXPERIMENTS --> PYLAR
    BEHAVIOR --> PYLAR
    CONVERSIONS --> PYLAR
    PYLAR --> TOOLS
    TOOLS --> AGENT
    AGENT --> TEAM
    
    style PYLAR fill:#FF4017,stroke:#CC3300,color:#fff
```

## Without Pylar vs With Pylar

### Without Pylar

**Challenges**:

* ❌ Manual statistical calculations
* ❌ Complex experiment data aggregation
* ❌ Time-consuming analysis
* ❌ Limited real-time monitoring

**Implementation Complexity**: \~4-5 weeks

### With Pylar

**Benefits**:

* ✅ Automated statistical analysis
* ✅ Real-time experiment monitoring
* ✅ Clear winner recommendations
* ✅ Easy experiment tracking

**Implementation Complexity**: \~5-6 hours

## Step-by-Step Implementation

### 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)

### Step 2: Create Experiment Views

**Experiment Results View**:

```sql theme={null}
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;
```

### 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)`

## 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"

## 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

## Next Steps

* [Marketing Campaign Optimizer Example](/examples/marketing-campaign-optimizer)
* [Marketing Attribution Analyzer Example](/examples/marketing-attribution-analyzer)