# Quick Start Guide

Get up and running with GeoLift in 5 minutes to measure the causal impact of your marketing campaigns.

## Business Value in 30 Seconds

**Problem Solved**: Measure true incremental lift from regional marketing campaigns  
**Why It Matters**: Prove ROI, optimize budget allocation, avoid false positives  
**Integration**: Works alongside MMM, attribution, and A/B testing  
**Impact**: Enables data-driven optimization for 10-30% efficiency improvements  
**Process**: 3 simple steps - find controls, check power, measure lift

## Installation

```bash
pip install -e .
```

## Your First Analysis in 3 Steps

### Step 1: Prepare Your Data

Create a CSV file with your sales data containing these columns:
- `date` - Date of observation
- `geo` - Geographic unit identifier (DMA, state, etc.)
- `sales` - Your outcome metric
- `treatment` - Binary indicator (1 for treated markets, 0 for control)

Example data structure:
```csv
date,geo,sales,treatment
2023-01-01,501,1000,0
2023-01-01,502,1200,1
2023-01-02,501,1050,0
2023-01-02,502,1300,1
```

### Step 2: Run the Analysis

```python
from geolift.analyzer import GeoLiftAnalyzer

# Initialize analyzer
analyzer = GeoLiftAnalyzer()

# Load your data
analyzer.load_data('your_data.csv')

# Run the complete analysis
results = analyzer.run_analysis(
    treatment_start_date='2023-06-01',
    treatment_markets=[502, 503],  # Your test markets
    outcome_column='sales'
)
```

### Step 3: View Results

```python
# Print summary
print(results.summary())

# Generate plots
analyzer.plot_results()

# Export detailed report
results.export_report('geolift_results.html')
```

## What You'll Get

- **Causal Impact Estimate**: How much incremental lift your campaign generated
- **Statistical Significance**: P-values and confidence intervals
- **Visual Diagnostics**: Pre/post treatment plots and synthetic control fit
- **Business Metrics**: ROI, cost per incremental unit, and efficiency metrics

## Next Steps

- **Need more control?** → See [User Guide](USER_GUIDE.md) for detailed configuration
- **Want to understand the methods?** → Check [Advanced Topics](ADVANCED_TOPICS.md)
- **Having issues?** → Review [FAQ](FAQ.md) for common solutions

## Sample Output

Your analysis will produce:
```
=== GeoLift Analysis Results ===
Treatment Period: 2023-06-01 to 2023-08-31
Treated Markets: [502, 503]

Causal Impact:
- Absolute Lift: +2,450 units (95% CI: +1,200 to +3,700)
- Relative Lift: +15.2% (95% CI: +7.8% to +22.6%)
- P-value: 0.003 (statistically significant)

Business Impact:
- Total Incremental Revenue: $245,000
- Campaign Cost: $50,000
- Incremental ROI: 4.9x
```

Ready to dive deeper? Continue to the [User Guide](USER_GUIDE.md) for comprehensive workflows and best practices.