Attribution tells you who touched what. Incrementality tells you what actually worked. As privacy changes make deterministic attribution harder, understanding true incremental impact becomes essential for marketing efficiency.
What is Incrementality?
Incrementality measures the causal impact of your marketing—the conversions that would NOT have happened without your advertising. It separates true lift from conversions that would have occurred organically.
Why Traditional Attribution Falls Short
- Correlation ≠ causation: Seeing an ad before converting doesn't mean the ad caused the conversion
- Selection bias: You're targeting people already likely to convert
- Attribution wars: Multiple channels claiming credit for same conversion
- Privacy gaps: Increasing inability to track user journeys
📊 The Incrementality Gap
Studies show that 20-60% of attributed conversions would have happened anyway without the ad exposure. Understanding your true incremental rate is crucial for accurate ROI calculation.
Incrementality Testing Methods
Randomized Controlled Tests (RCTs)
The gold standard—randomly assign users to test (see ads) or control (don't see ads) groups.
ClicksFlyer Team
- Define your test hypothesis and success metrics
- Calculate required sample size for statistical significance
- Randomly split audience into test and control
- Run ads to test group only
- Measure conversion difference between groups
- Calculate lift and statistical significance
Ghost Bids / Intent-to-Treat
For auction-based environments, participate in auctions but don't show ads to control group.
- Bid on all eligible impressions
- Win auctions for both groups
- Serve ads only to test group
- Track conversions for both groups
Geo Experiments
Use geographic regions as test and control groups when user-level randomization isn't possible.
ClicksFlyer Team
- Match markets by size, demographics, and baseline behavior
- Account for seasonal and regional variations
- Run for sufficient duration (2-4 weeks minimum)
- Use synthetic control methods for analysis
"The best incrementality test is one you can act on. A statistically sound result that's too expensive to implement doesn't help anyone."
Designing Your Test
Hypothesis Formation
Start with a clear, testable hypothesis:
- "Facebook ads drive X% incremental installs"
- "Retargeting generates Y% lift in purchases"
- "Upper-funnel video increases Z% conversions"
Sample Size Calculation
Key factors determining required sample size:
- Baseline conversion rate: Lower rates need larger samples
- Expected lift: Smaller expected lifts need larger samples
- Confidence level: Usually 95%
- Statistical power: Usually 80%
Test Duration
- Minimum 1-2 weeks to capture conversion cycles
- Account for day-of-week effects
- Consider your typical time-to-convert
- Avoid major seasonality or events
Analyzing Results
Key Metrics
- Incremental lift: (Test - Control) / Control
- Incremental conversions: Total attributed × incremental rate
- True CPA: Spend / Incremental conversions
- iROAS: Incremental revenue / Spend
Statistical Significance
- Calculate p-value for observed lift
- Look for p < 0.05 (95% confidence)
- Report confidence intervals, not just point estimates
- Be wary of small sample sizes or short tests
Common Pitfalls
Contamination
When control group gets exposed to treatment:
- Cross-device exposure
- Spillover effects in geo tests
- Shared household exposure
Selection Bias
When test and control groups aren't truly comparable:
- Non-random assignment
- Pre-existing differences between groups
- Opt-in bias
Implement Incrementality Testing
ClicksFlyer's measurement team can help you design and execute incrementality tests for your campaigns.
Get StartedActing on Results
If Lift is Lower Than Expected
- Reduce budget on that channel/campaign
- Reallocate to higher-incrementality tactics
- Test different targeting or creative approaches
- Consider if you're over-investing in bottom-funnel
Building an Incrementality Culture
- Run regular tests across channels
- Build incrementality into planning models
- Train teams to think beyond attributed performance
- Invest in measurement infrastructure
Incrementality testing requires investment, but the insights are invaluable. Understanding true marketing impact allows you to allocate budget efficiently and prove value to stakeholders.