The Rise of Incrementality Testing
Why A/B Testing Your Entire Marketing Budget is the New Standard
When Every Dollar Needs Defending
After running hundreds of marketing campaigns and watching attribution models fail to explain obvious performance discrepancies, I've become evangelical about incrementality testing. It's the only measurement approach that answers the question every CMO actually cares about: "What would happen if we stopped spending this money?"
The shift toward incrementality testing isn't just methodological—it's existential. In an economic environment where every marketing dollar must be justified, correlation-based measurement isn't enough. You need causation.
The Attribution Lie We Tell Ourselves
Traditional attribution tells you which touchpoints customers interacted with before converting. Incrementality testing tells you which touchpoints actually caused conversions. The difference is massive and often surprising.
I've seen campaigns with perfect attribution scores that generated zero incremental sales, and campaigns with terrible attribution that drove significant business impact. Attribution measures correlation; incrementality measures causation.
How Incrementality Testing Actually Works
The concept is elegantly simple: randomly divide your target market into test and control groups, expose the test group to your marketing while withholding it from the control group, then measure the difference in outcomes.
Modern incrementality testing platforms use sophisticated geographic, demographic, or temporal holdout designs that maintain statistical rigor while minimizing business risk.
The Geographic Advantage
Geographic holdout tests have become the gold standard for incrementality measurement. By turning off advertising in randomly selected geographic areas and measuring the impact on sales, you can calculate the true incremental impact of your marketing spend.
This approach works particularly well for brands with:
National or broad geographic distribution
Consistent baseline performance across markets
The ability to measure outcomes at geographic levels
Beyond Simple On/Off Tests
Sophisticated incrementality testing goes beyond simple presence/absence experiments:
Budget level testing: Understanding the optimal spend level for each channel
Creative testing: Measuring the incremental impact of different messages
Audience testing: Identifying which segments respond to marketing vs. convert naturally
Channel interaction testing: Understanding how channels work together rather than in isolation
The Statistical Requirements
Effective incrementality testing requires:
Sufficient sample size for statistical significance
Consistent measurement periods (typically 2-4 weeks minimum)
Randomized assignment to avoid selection bias
Baseline performance measurement for context
Integration with Marketing Mix Modeling
The most powerful measurement approaches combine incrementality testing with marketing mix modeling. Use incrementality tests to validate and calibrate MMM findings, creating measurement systems that are both comprehensive and scientifically rigorous.
Platform Solutions
Several platforms now make incrementality testing accessible:
Haus: Specializes in geographic holdout experiments
Measured: Offers multi-platform incrementality testing
Meta's Conversion Lift: Built-in incrementality testing for Facebook campaigns
Google's Brand Lift: Measuring brand impact incrementality
The Cost-Benefit Analysis
Incrementality testing does require temporarily reducing marketing in test areas, creating short-term opportunity costs. However, the insights generated typically identify inefficiencies that more than compensate for testing costs.
Most companies discover they're spending 20-40% of their budget on non-incremental activities that can be reallocated to higher-performing channels or tactics.
Building a Testing Culture
Successful incrementality testing requires organizational commitment:
Executive buy-in for temporary performance dips during testing
Statistical literacy across marketing teams
Integrated data systems that can measure outcomes quickly
Patience to run tests long enough for reliable results
What We're Learning
Incrementality testing is revealing uncomfortable truths about marketing effectiveness:
Many "high-performing" campaigns are simply capturing demand that would have occurred anyway
Brand advertising often has stronger incrementality than performance advertising
Cross-channel effects are larger and more complex than attribution suggests
Customer lifetime value incrementality often differs dramatically from short-term conversion incrementality
The Future of Marketing Measurement
Incrementality testing is becoming the foundation for a new approach to marketing measurement that prioritizes causal understanding over correlation analysis. Combined with MMM and enhanced attribution, it creates measurement systems worthy of marketing's strategic importance.
The brands that master incrementality testing will make fundamentally better strategic decisions than those relying on attribution theater.