PyMC-Marketing MMM

You can build a Bayesian marketing mix model by configuring adstock and saturation components, setting priors, and running sampling. This approach applies carryover and diminishing returns transformations to channel spend data for principled effectiveness inference.

Yes, you can model marketing mix data across multiple regions or products. The workflow supports multidimensional hierarchies, allowing you to apply hierarchical priors and baseline configurations to multi-market datasets.

To start configuring Bayesian MMM priors and workflows, you need date variables, channel spend variables, and a target variable. Optional dimensions like regions or products can be included, followed by setting up a pipeline for priors and sampling.

Yes, the Bayesian MMM approach supports optional generalized additive model (GAM) components. You can integrate these into the model structure alongside adstock and saturation transformations to capture non-linear relationships in the data.

You evaluate model fit and posterior diagnostics by analyzing the inference workflows provided. This involves examining posterior distributions and diagnostic outputs to assess the reliability of your marketing mix model results.

Hierarchical priors in multi-dimensional marketing mix models require careful configuration to avoid over-shrinkage across regions or products. Proper structuring of baseline priors and model components is essential to maintain principled inferences.