In this tutorial, we build a complete Bayesian marketing mix modeling workflow using Google Meridian. We begin by installing the required libraries, verifying GPU availability, and exploring a geo-level marketing dataset that includes media impressions, spend, controls, promotions, conversions, population, and revenue. We then map the raw columns to Meridian’s data schema, define interpretable ROI-based priors, and configure the model before fitting it with prior and posterior NUTS sampling. After training, we evaluate convergence and predictive accuracy, examine channel contributions, ROI, marginal ROI, effectiveness, adstock, saturation, and response curves, and use the Analyzer API to extract custom posterior metrics. We conclude the workflow by optimizing both fixed and flexible budgets, generating shareable HTML reports, and saving the fitted model for reuse.
!pip install --upgrade -q "google-meridian[and-cuda]"
import numpy as np
import pandas as pd
import altair as alt
