# Frequently Asked Questions

Quick answers to the most common GeoLift questions.

## Business Questions

### Q: What problem does GeoLift solve?

**A:** GeoLift measures the **true incremental impact** of regional marketing campaigns. It answers the critical question: "How much additional revenue/conversions did my campaign actually generate?" by using advanced causal inference to separate campaign effects from natural market fluctuations.

### Q: Why is this important for my business?

**A:** 
- **Prove ROI**: Get statistical confidence in your marketing returns
- **Optimize Budget**: Identify which regional campaigns work best
- **Avoid Waste**: Stop spending on campaigns that don't drive incremental results
- **Support Decisions**: Provide rigorous evidence to leadership for budget allocation
- **Competitive Advantage**: Make data-driven decisions while competitors rely on guesswork

### Q: Is this the only measurement tool I need?

**A:** No, GeoLift is **one component** of a comprehensive measurement strategy:
- **Best for**: Regional campaigns, store rollouts, geo-targeted advertising
- **Complements**: Media Mix Modeling (MMM), multi-touch attribution, A/B testing
- **Integrates with**: Your existing analytics stack (Google Analytics, Adobe, etc.)
- **Use alongside**: Brand studies, customer surveys, and other measurement approaches

### Q: What's the cost and expected ROI?

**A:** 
- **Investment**: Proprietary software license + 2-4 weeks implementation
- **Typical ROI**: 5-10x return through improved campaign optimization
- **Payback Period**: Usually within first campaign optimization cycle (3-6 months)
- **Long-term Value**: Compound returns as you optimize multiple campaigns over time
- **Contact**: Reach out for specific pricing based on your organization size

### Q: How does the process work?

**A:** Simple **3-step workflow**:
1. **Find Fair Comparison** (1-2 days): Identify control markets that behave like your test markets
2. **Check Test Strength** (1 day): Ensure your experiment can detect meaningful results  
3. **Measure the Lift** (1-2 days): Calculate actual incremental impact with statistical confidence

**Total time**: 1 week from data to actionable insights

---

## Getting Started

### Q1: What data do I need to run a GeoLift analysis?

**A:** You need:
- **Time series data**: At least 12-24 weeks of pre-campaign data
- **Geographic units**: Markets, DMAs, states, or regions
- **Outcome metric**: Sales, conversions, or other KPIs
- **Treatment assignment**: Which markets received the campaign

Data format: CSV with columns for date, geographic unit, outcome metric, and treatment indicator.

### Q2: How long should I run my campaign to get reliable results?

**A:** Use the PowerCalculator to determine optimal duration:

```python
from geolift.analyzer import PowerCalculator
power_calc = PowerCalculator()
power_results = power_calc.calculate_power(
    treatment_markets=[502, 503],
    baseline_data=your_data,
    campaign_duration_weeks=12
)
print(f"Recommended duration: {power_results.min_duration} weeks")
```

Generally, 8-16 weeks provides good statistical power for most campaigns.

### Q3: How many control markets do I need?

**A:** Typically 5-15 control markets work well. The DonorEvaluator will automatically select the best ones:

```python
evaluator.evaluate_donors(
    treatment_markets=[502, 503],
    max_donors=10  # Will select best 10 controls
)
```

## Analysis Issues

### Q4: My pre-period fit looks poor. What should I do?

**A:** Try these solutions in order:
1. **Extend pre-period**: Add more historical data
2. **Remove outliers**: Exclude unusual periods (holidays, events)
3. **Check data quality**: Ensure consistent measurement methodology
4. **Adjust donor selection**: Use stricter correlation thresholds

```python
# Stricter donor selection
evaluator.evaluate_donors(
    treatment_markets=[502, 503],
    min_correlation=0.8  # Increase from default 0.7
)
```

### Q5: My results show no significant effect. What went wrong?

**A:** Common causes:
- **Low statistical power**: Campaign too short or effect too small
- **Poor control selection**: Controls don't match treatment markets well
- **External factors**: Market disruptions during campaign period
- **Data issues**: Measurement problems or missing data

Check power analysis first:
```python
if power_results.power < 0.8:
    print("Insufficient statistical power - extend campaign or add markets")
```

### Q6: The effect size seems too large to be believable. Is this normal?

**A:** Large effects can be real, but verify:
1. **Check data definitions**: Ensure consistent measurement
2. **Review external events**: Major market changes during campaign
3. **Validate treatment assignment**: Confirm which markets got treatment
4. **Run sensitivity analysis**: Test with different time periods

```python
# Sensitivity test with shorter post-period
results_sensitive = analyzer.run_analysis(
    treatment_start_date='2023-06-01',
    treatment_end_date='2023-07-31',  # Shorter period
    treatment_markets=[502, 503]
)
```

## Interpretation

### Q7: How do I interpret the confidence intervals?

**A:** Confidence intervals show the range of plausible effect sizes:
- **Narrow intervals**: More precise estimates
- **Wide intervals**: More uncertainty in the estimate
- **Intervals excluding zero**: Statistically significant effects

Example interpretation:
```
Relative Lift: +15.2% (95% CI: +7.8% to +22.6%)
```
"We're 95% confident the true lift is between 7.8% and 22.6%"

### Q8: What's the difference between absolute and relative lift?

**A:** 
- **Absolute lift**: Raw units of incremental impact (e.g., +1,000 sales)
- **Relative lift**: Percentage increase over baseline (e.g., +15%)

Both are important:
- Use **absolute lift** for revenue calculations
- Use **relative lift** for comparing across campaigns

### Q9: How do I calculate ROI from the results?

**A:** The analyzer calculates ROI automatically:

```python
print(f"Campaign Cost: ${campaign_cost:,.0f}")
print(f"Incremental Revenue: ${results.incremental_revenue:,.0f}")
print(f"ROI: {results.roi:.1f}x")
print(f"Net Profit: ${results.incremental_revenue - campaign_cost:,.0f}")
```

### Q10: When should I trust the results vs. be skeptical?

**A:** **Trust results when:**
- Good pre-period fit between treatment and synthetic control
- Statistically significant p-value (< 0.05)
- Effect size is reasonable for your industry
- Results are stable across sensitivity tests

**Be skeptical when:**
- Poor pre-period fit or large residuals
- Very large or very small effect sizes
- High p-values (> 0.10)
- Results change dramatically with small specification changes

## Need More Help?

- **Technical details**: See [API Reference](API_REFERENCE.md)
- **Step-by-step guidance**: Check [User Guide](USER_GUIDE.md)
- **Mathematical background**: Review [Advanced Topics](ADVANCED_TOPICS.md)
- **Quick start**: Try [Quick Start Guide](QUICK_START.md)