Attribution & Analytics
Measured
Measured is a marketing measurement platform that combines incrementality experiments, media mix modeling, and multi-touch reporting to show which channels actually drive growth. It runs geo and audience holdout tests, blends them with MMM and platform data, and reports calibrated contribution and saturation curves so teams can reallocate budget with confidence. It is built for direct-to-consumer and retail brands spending across many paid channels that want results validated by experiments rather than last-click or platform-reported conversions.
What it does
Measured answers a single question for paid-media teams: how much incremental revenue did each channel actually cause? It runs continuous geo holdout and audience-based experiments that switch spend off in matched markets, then measures the lift against control to estimate true incrementality. Those experiment results calibrate a media mix model covering the full channel portfolio, including channels too small to test directly, and produce saturation curves showing where the next dollar is wasted. A unified dashboard reconciles experiment, MMM, and platform-reported numbers so analysts can see where last-click overcounts. Outputs feed budget planning scenarios, letting teams shift spend toward channels with proven marginal return rather than inflated platform attribution.
Where it fits
Measured sits at the analytics stage, layering incrementality and mix modeling on top of pixels and platform reporting to validate where spend truly pays off.
Core features
- Continuous geo and audience holdout incrementality tests
- Experiment-calibrated media mix modeling across all channels
- Saturation curves showing diminishing returns by channel
- Reconciliation of experiment, MMM, and platform-reported results
- Budget planning scenarios built from calibrated contribution
Best for
- DTC and retail brands spending across many paid channels
- Teams replacing last-click with experiment-backed measurement
- Marketers planning budget reallocation by marginal return
Beginner notes
- Expect incrementality numbers below platform-reported conversions, because holdout tests strip out the credit each channel claims for itself.
- Give geo experiments enough time and spend contrast to read a clean lift, since underpowered tests produce noisy results.
- Use saturation curves to find over-funded channels before chasing new ones, as cutting waste often beats adding budget.