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
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 observationgeo- Geographic unit identifier (DMA, state, etc.)sales- Your outcome metrictreatment- Binary indicator (1 for treated markets, 0 for control)
Example data structure:
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
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
# 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 for detailed configuration
Want to understand the methods? → Check Advanced Topics
Having issues? → Review FAQ 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 for comprehensive workflows and best practices.