Informative Priors for MMM

To set informative priors for channel effects in PyMC-Marketing MMM, you calculate them from scaled data statistics and domain knowledge, then apply these values within the model_config structure to guide posterior calibration.

Wide MMM posteriors often result from poorly calibrated or overly loose priors. You can tighten posterior calibration by applying informative priors derived from data characteristics and domain knowledge to intercept and adstock parameters.

Yes, you can derive adstock priors from scaled time-series data statistics. This approach uses data-driven reasoning to configure appropriate distributions across varying channel scales within your MMM dataset.

Yes, the approach supports handling different prior distributions. It guides configuring intercept, channel effects, and adstock priors by deriving values from data statistics and integrating them into the model_config structure.

The best way to calibrate intercept priors for Marketing Mix Models is to calculate them based on data characteristics and domain knowledge, applying the resulting informative priors to achieve well-calibrated posteriors.