Attribution models are misleading. They divide up credit, but they cannot test true causality. Switch to automated, data-science grade Geo-Experimentation.
“Which of your ad channels are actually driving incremental revenue, and which are taking credit for sales that would have happened anyway?”
Standard attribution models look exceptional in a dashboard, but they fail to answer the only question that matters to your bottom line: If we turned this channel off tomorrow, would our revenue actually drop?
To uncover the truth, you have to run a “go-dark” test in select regions and measure it against an unexposed control group. Historically, executing this rigorously required data scientists and advanced statistical modeling.
Our platform automates the complex math so you can protect your ad budget from waste.
Simply upload your sales and spend history by region (US DMAs, states, or custom geographies). The platform automatically excludes regions where your channel barely spends, selects up to five optimal test markets, and blends the remaining regions into a highly accurate “synthetic control twin” that perfectly tracked your test markets historically.
Every candidate design declares its Minimum Detectable Effect (MDE). This lets you pick your exact point on the tradeoff curve: fewer dark markets means less revenue disruption during the test, while more dark markets buys you a sharper, finer reading of your true incrementality.
Before anything launches, the platform executes Placebo Rehearsals on quiet stretches of your historical data where the true effect is zero—ensuring the design doesn't flag false positives. A Detectability Check then verifies your paused spend is large enough to produce a drop the design can actually see. If the test cannot work, the platform tells you upfront so you keep your budget safe.
Once live, the platform automatically splits the go-dark period into latency, washout, and clean testing windows. This ensures yesterday's lingering impressions do not contaminate the measurement. Finally, it mathematically verifies that channel spend actually stopped in the dark regions before trusting any analytical result.
Get the definitive drop versus your synthetic twin accompanied by a clear confidence interval. Discover your true incremental Return on Ad Spend (incremental revenue per dollar of paused spend) or incremental Cost Per Acquisition for lead-gen tracking.
Results are independently validated using Synthetic Difference-in-Differences (SDID)—a structurally distinct method. If the two analytical methods diverge, the platform flags and investigates the disagreement. We never average away the truth.
Seamlessly feed your newly measured carryover decay curves and effect priors straight into your Media Mix Modeling (MMM) stack, establishing an unassailable ground-truth audit of your broader attribution framework.
Don't guess where your next ad dollar should go. Let our platform analyze your data feasibility and build a test design structure for your brand.