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:

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:

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

# 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:

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

# 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:

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?