Give credit where
it’s actually due.

Causal attribution uses evidence of incremental impact to calibrate recorded attribution. In Dema, causal factor attribution applies an explicit multiplier to a chosen baseline: ad-platform reporting or multi-touch attribution. Experiments help you assess that multiplier. The result is an evidence-informed allocation of channel credit, rather than treating a recorded touchpoint as proof that an ad caused a sale.

Multi-touch attribution divides credit among the touchpoints it can observe. It does not, on its own, establish what would have happened without the advertising. Dema can use MTA as the baseline, then calibrate its attributed value using incrementality evidence. You can also choose the ad platform as the baseline. The appropriate multiplier depends on which base you choose.

Dema shows a distribution informed by finalized incrementality experiments, together with matching experiments from your own business. Its Bayesian model combines benchmark evidence with your results. You review the distribution and experiment evidence, then choose an explicit multiplier for the relevant channel, funnel and market. A benchmark is a starting point; it is not the same as a test of your own business.

It means the calibrated channel value is higher than the selected baseline. For example, €20,000 multiplied by 1.20 becomes €24,000. This can be appropriate when the evidence suggests that the baseline under-credits a channel. The multiplier is relative to the chosen base, so a factor for ad-platform reporting cannot simply be reused for MTA.

No. It changes how revenue is attributed. When calibration reduces paid-channel credit, the difference is redistributed to the channel groups selected in your settings, such as Direct, Backfilled or Search Organic. Total revenue remains balanced. The illustrative €50,000 → €30,000 example changes channel credit; it does not remove €20,000 from the business.

The multiplier applies to the selected attributed value. When you calibrate conversion counts and keep spend fixed, CPA moves in the opposite direction: 50% of the conversions means twice the CPA. For profit decisions, look beyond revenue to contribution after product costs, fulfilment, returns and advertising. Equal attributed revenue does not imply equal profit.

Incrementality tests estimate what changed because of advertising and help inform calibration. Causal factor attribution applies that evidence to channel reporting. Marketing mix modeling takes a broader view of the mix and supports budget planning across channels. They work together, rather than being interchangeable measurement methods.