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Full-stack web application for Telegram management - Frontend: Vue 3 + Vben Admin - Backend: NestJS - Features: User management, group broadcast, statistics 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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marketing-agent/services/ab-testing/README.md
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marketing-agent/services/ab-testing/README.md
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# A/B Testing Service
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Advanced A/B testing and experimentation service for the Telegram Marketing Intelligence Agent system.
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## Overview
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The A/B Testing service provides comprehensive experiment management, traffic allocation, and statistical analysis capabilities for optimizing marketing campaigns.
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## Features
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### Experiment Management
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- Multiple experiment types (A/B, multivariate, bandit)
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- Flexible variant configuration
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- Target audience filtering
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- Scheduled experiments
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- Early stopping support
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### Traffic Allocation Algorithms
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- **Random**: Fixed percentage allocation
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- **Epsilon-Greedy**: Balance exploration and exploitation
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- **UCB (Upper Confidence Bound)**: Optimistic exploration strategy
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- **Thompson Sampling**: Bayesian approach for optimal allocation
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### Statistical Analysis
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- Frequentist hypothesis testing
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- Bayesian analysis
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- Confidence intervals
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- Power analysis
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- Multiple testing correction
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### Real-time Features
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- Live metrics tracking
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- Dynamic allocation updates
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- Real-time results visualization
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- Automated winner detection
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## Architecture
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```
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┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
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│ API Gateway │────▶│ A/B Testing Service│────▶│ MongoDB │
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└─────────────────┘ └──────────────────┘ └─────────────────┘
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│ │
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├───────────────────────────┤
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▼ ▼
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┌─────────────┐ ┌──────────────┐
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│ Redis │ │ RabbitMQ │
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└─────────────┘ └──────────────┘
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```
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## API Endpoints
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### Experiments
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- `GET /api/experiments` - List experiments
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- `POST /api/experiments` - Create experiment
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- `GET /api/experiments/:id` - Get experiment details
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- `PUT /api/experiments/:id` - Update experiment
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- `DELETE /api/experiments/:id` - Delete experiment
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- `POST /api/experiments/:id/start` - Start experiment
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- `POST /api/experiments/:id/pause` - Pause experiment
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- `POST /api/experiments/:id/complete` - Complete experiment
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### Allocations
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- `POST /api/allocations/allocate` - Allocate user to variant
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- `GET /api/allocations/allocation/:experimentId/:userId` - Get user allocation
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- `POST /api/allocations/conversion` - Record conversion
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- `POST /api/allocations/event` - Record custom event
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- `POST /api/allocations/batch/allocate` - Batch allocate users
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- `GET /api/allocations/stats/:experimentId` - Get allocation statistics
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### Results
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- `GET /api/results/:experimentId` - Get experiment results
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- `GET /api/results/:experimentId/metrics` - Get real-time metrics
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- `GET /api/results/:experimentId/segments` - Get segment analysis
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- `GET /api/results/:experimentId/funnel` - Get funnel analysis
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- `GET /api/results/:experimentId/export` - Export results
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## Configuration
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### Environment Variables
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- `PORT` - Service port (default: 3005)
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- `MONGODB_URI` - MongoDB connection string
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- `REDIS_URL` - Redis connection URL
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- `RABBITMQ_URL` - RabbitMQ connection URL
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- `JWT_SECRET` - JWT signing secret
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### Experiment Configuration
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- `DEFAULT_EXPERIMENT_DURATION` - Default duration in ms
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- `MIN_SAMPLE_SIZE` - Minimum sample size per variant
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- `CONFIDENCE_LEVEL` - Statistical confidence level
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- `MDE` - Minimum detectable effect
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### Allocation Configuration
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- `ALLOCATION_ALGORITHM` - Default algorithm
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- `EPSILON` - Epsilon for epsilon-greedy
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- `UCB_C` - Exploration parameter for UCB
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## Usage Examples
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### Create an A/B Test
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```javascript
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const experiment = {
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name: "New CTA Button Test",
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type: "ab",
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targetMetric: {
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name: "conversion_rate",
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type: "conversion",
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goalDirection: "increase"
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},
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variants: [
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{
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variantId: "control",
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name: "Current Button",
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config: { buttonText: "Sign Up" },
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allocation: { percentage: 50 }
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},
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{
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variantId: "variant_a",
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name: "New Button",
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config: { buttonText: "Get Started Free" },
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allocation: { percentage: 50 }
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}
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],
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control: "control",
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allocation: {
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method: "random"
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}
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};
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const response = await abTestingClient.createExperiment(experiment);
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```
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### Allocate User
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```javascript
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const allocation = await abTestingClient.allocate({
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experimentId: "exp_123",
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userId: "user_456",
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context: {
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deviceType: "mobile",
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platform: "iOS",
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location: {
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country: "US",
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region: "CA"
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}
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}
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});
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// Use variant configuration
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if (allocation.variantId === "variant_a") {
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showNewButton();
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} else {
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showCurrentButton();
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}
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```
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### Record Conversion
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```javascript
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await abTestingClient.recordConversion({
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experimentId: "exp_123",
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userId: "user_456",
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value: 1,
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metadata: {
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revenue: 99.99,
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itemId: "premium_plan"
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}
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});
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```
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### Get Results
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```javascript
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const results = await abTestingClient.getResults("exp_123");
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// Check winner
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if (results.analysis.summary.winner) {
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console.log(`Winner: ${results.analysis.summary.winner.name}`);
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console.log(`Improvement: ${results.analysis.summary.winner.improvement}%`);
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}
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```
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## Adaptive Allocation
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### Epsilon-Greedy
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```javascript
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const experiment = {
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allocation: {
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method: "epsilon-greedy",
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parameters: {
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epsilon: 0.1 // 10% exploration
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}
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}
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};
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```
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### Thompson Sampling
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```javascript
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const experiment = {
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allocation: {
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method: "thompson"
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}
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};
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```
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## Statistical Analysis
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### Power Analysis
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The service automatically calculates required sample sizes based on:
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- Baseline conversion rate
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- Minimum detectable effect
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- Statistical power (default: 80%)
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- Confidence level (default: 95%)
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### Multiple Testing Correction
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When running experiments with multiple variants:
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- Bonferroni correction
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- Benjamini-Hochberg procedure
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## Best Practices
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1. **Sample Size Planning**
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- Use power analysis to determine duration
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- Don't stop experiments too early
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- Account for weekly/seasonal patterns
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2. **Metric Selection**
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- Choose primary metrics aligned with business goals
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- Monitor guardrail metrics
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- Consider long-term effects
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3. **Audience Targeting**
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- Use consistent targeting criteria
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- Ensure sufficient traffic in each segment
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- Consider interaction effects
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4. **Statistical Rigor**
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- Pre-register hypotheses
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- Avoid peeking at results
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- Use appropriate statistical tests
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## Monitoring
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### Health Check
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```bash
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curl http://localhost:3005/health
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```
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### Metrics
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- Prometheus metrics at `/metrics`
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- Key metrics:
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- Active experiments count
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- Allocation latency
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- Conversion rates by variant
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- Algorithm performance
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## Development
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### Setup
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```bash
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npm install
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cp .env.example .env
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npm run dev
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```
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### Testing
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```bash
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npm test
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npm run test:integration
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npm run test:statistical
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```
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### Docker
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```bash
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docker build -t ab-testing-service .
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docker run -p 3005:3005 --env-file .env ab-testing-service
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```
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## Performance
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- Allocation latency: <10ms p99
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- Results calculation: <100ms for 100K users
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- Real-time updates: <50ms latency
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- Supports 10K allocations/second
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## Security
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- JWT authentication required
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- Experiment isolation by account
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- Rate limiting per account
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- Audit logging for all changes
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## Support
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For issues and questions:
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- Review the statistical methodology guide
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- Check the troubleshooting section
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- Contact the development team
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