Marketing Measurement & Channel Impact
Know which channels are actually driving growth.
30–50% of conversions attributed to paid channels are non-incremental. The customers would have converted anyway. Budget decisions made from that data are systematically wrong, and most CMOs know it but can't prove it.
Attribution tells you where credit goes. It doesn't tell you what caused the sale.
Every attribution model, last-click, multi-touch, data-driven, shares the same fundamental limitation: it tells you where conversions are credited, not what caused them. A customer who sees a retargeting ad and converts was going to convert anyway. The model credits the ad. The budget follows the model. The result is a systematic overallocation to channels that are good at claiming credit rather than channels that are good at driving growth.
This is not a technology problem. Better attribution platforms don't solve it. The problem is the difference between correlation (this channel was present when the conversion happened) and causation (this channel caused the conversion to happen). Measuring causal impact requires a different methodology: incrementality testing.
For companies spending $500K or more on digital marketing, the gap between attributed performance and actual incremental impact is often worth more to understand than any individual channel optimization.
Measure what actually drives growth.
- Attribution model review We assess your current attribution setup: what model is in use, what it measures well, and where it systematically over- or under-credits channels.
- Incrementality study design We design holdout tests and geo-based experiments to measure the true causal impact of specific channels or campaigns.
- Baseline measurement We establish conversion baselines before any test, ensuring comparison groups are appropriately matched and seasonal factors are accounted for.
- Study execution and analysis We run the measurement study and analyze results with appropriate statistical controls, separating signal from noise and reporting confidence levels honestly.
- Budget reallocation recommendations We translate measurement findings into specific budget recommendations, quantifying the expected impact of reallocation based on measured rather than attributed performance.
Who this is for
- CMOs with $500K+ in annual media spend Who suspect their attribution model is giving a misleading picture of what's working, but haven't yet found a clean way to test it.
- CFOs and founders evaluating marketing ROI Who need a defensible methodology for understanding what the marketing budget produces, beyond platform-reported ROAS.
- Performance marketing leads Who want to move budget allocation from attributed metrics to incrementally measured impact, and need the analytical infrastructure to do it.
FAQ
What's the difference between attribution and incrementality?
Attribution assigns credit to channels based on their presence in the conversion path. Incrementality measures causation: what would have happened without that channel. They answer different questions and often point to different conclusions.
How is incrementality testing different from media mix modeling?
Both try to answer the same question from different angles. MMM uses historical data to estimate channel contribution over time. Incrementality testing isolates causal impact via controlled experiments. They're complementary. We often recommend both.
What spend level does this become relevant at?
Typically $500K+ in annual media spend, where the gap between attributed and incremental impact is large enough to justify the measurement investment.
How long does an incrementality study take?
Four to twelve weeks, depending on conversion volume and the minimum detectable effect required. Studies with lower traffic volume need longer windows to achieve sufficient statistical power.
What platforms do you work with?
We're platform-agnostic. The methodology, holdout design, geo experiments, statistical analysis, applies regardless of where you're running campaigns.