The budget optimiser uses a market-mix modeling approach. As input data it uses spend data but can also use impression data or other input data. The model builds ad-stock representing the recent investments and models decay-rates, seasonalities, country holidays and other factors that might be important.

Here it shows the impact by variables. The variables are the variables that have been used as input data, eg. sourceMedium, campaign or channelgrouping.

The actual response here is the actual revenue or conversions. The variable that has been selected as outcome. The predicted is the modeled outcome so one can see how well the model fits the actual revenue. Especially spikes can be hard to predict etc. But the more data the model has the better it can fit the model.

This chart shows the Share of the total spend vs. the total effect and the ROI. Where the ROI is the return on investment considering the chosen outcome. If its revenue its a simple ROI but if its conversions it has to be treated more as a relative figure.

This graph shows the Spend on X and the Response on Y considering the diminishing returns as the spend increases. The response curves are per channel.

Shows how many % the investment decays over time. The time unit is weekly by default. As an example if decay rate is 30% the effect of the investment in the channel decays 30% per week.

Shows the individual datapoints with the actual response (revenue) and the fitted datapoint from the model.