Media Mix Modeling with PyMC-Marketing

Bayesian Media Mix Modeling workflow using the PyMC-Marketing MMM class.

PyMC prerequisite: This skill assumes familiarity with PyMC's core modeling API (coords/dims, priors, MCMC diagnostics, HSGP). For foundational patterns, see the pymc-modeling skill.

LLMs understand Bayesian inference, MCMC, and hierarchical models in general. But getting from those concepts to a correctly specified, well-diagnosed, and actionable PyMC-Marketing MMM requires domain-specific knowledge: which Prior to use for saturation beta informed by spend shares, how dims=("geo",) activates multidimensional partial pooling, why the final model must be fit on the full dataset (time-slice CV is only for stability assessment), how add_lift_test_measurements() resolves causal identification, and how BudgetOptimizerWrapper translates posterior uncertainty into optimal allocations.

This skill encodes those patterns. Without it, an LLM might hold out test data for the final fit (wrong -- use all data, validate with time-slice CV), use flat priors on saturation parameters (causes divergences), skip add_original_scale_contribution_variable (then contributions are on scaled space), or call BudgetOptimizer directly instead of BudgetOptimizerWrapper (misses geo-level allocation).

Quick Start

import arviz as az
import numpy as np
import pandas as pd
from pymc_extras.prior import Prior