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Incrementality Testing
Measure the True Causal Impact of Your Marketing
Move beyond correlation to establish the true, causal-driven value of your marketing spend. By using controlled experiments, Violet's Incrementality Testing isolates the actual lift your campaigns generate, providing the definitive proof needed to validate your models and invest with confidence.
Trusted by CMOs at companies with over $10m in marketing spend
Proving the Incrementality of Investment Is Hard
Attribution Shows Correlation, Not Causation
Your current models show correlation, not causation, leaving you to guess which channels are truly driving growth and which are taking credit for organic behavior.
Uncertain Budget Allocation
Without knowing the true incremental impact of your spend, you can't confidently decide whether to scale, maintain, or cut budget in a specific channel or campaign.
Marketing’s True Impact Is Hard to Defend
CFOs and CEOs don’t want to hear about attribution. When impact can’t be proven, it becomes harder to justify marketing budgets to your CFO, CEO, or board.
The Transformative Impact of Incrementality Testing
Establish Causal Proof of Performance
Incrementality testing delivers the most reliable way to measure true business impact — grounded in experiments, not assumptions.
Integrate Testing Into Your Growth Motion
Our data scientists build a custom Incrementality Backlog, prioritize tests, and design them around your goals and budget — embedding experimentation directly into your campaign planning and investment cycles. You receive expert-guided insights, not just reports
Validate and Improve Your Models
Use causal insights to calibrate your MMM and DDA models, creating a continuous feedback loop that strengthens every future decision.
How Violet Makes It Work
Comprehensive Testing Framework
We deploy geo-holdout, ghost-ad, and matched-market tests tailored to your data, your spend, and your growth goals.
White-Glove Guidance
Every program is led by our data scientists — handling prioritization, setup, execution, and interpretation — so you can focus on decisions, not statistics.
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