Make your next euro
work harder.

Marketing mix modeling uses aggregate marketing and business data to estimate how channels contribute to outcomes. It can account for patterns such as diminishing returns and delayed effects. Dema connects this view with commerce economics, so teams can evaluate contribution as well as revenue.

Attribution allocates credit using the recorded touchpoints or platform reporting available to it. MMM works with aggregate spend and outcomes across the mix. Causal factor attribution can calibrate channel reporting with experiment evidence; MMM supports broader scenario and budget planning.

Average return describes performance across the spend you have already made. Marginal return describes the expected change from additional spend. As a channel saturates, the next euro can return less than earlier euros. Response curves help make that difference visible.

Yes. Compare objectives such as contribution, revenue and new customers, and set budget constraints that reflect your business. A useful plan should account for the channels you want to protect, limits on movement and spending tied to an active test.

Incrementality tests provide evidence about a specific intervention, channel, market and time period. That evidence can inform model calibration and challenge assumptions. It strengthens the model without making every future prediction certain.

MMM uses aggregate inputs rather than following individual user journeys. Its quality still depends on data coverage, variation, model assumptions and validation. Removing the need for cookies does not remove those requirements.

Connect marketing spend, commerce outcomes and relevant cost data. The appropriate history, level of detail and business context depend on your markets and channel mix. The team can help assess what is suitable for your model.