End-to-End Bayesian Marketing Mix Modeling with Google Meridian: Media Measurement, ROI Analysis, and Budget Optimization

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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.

Copy CodeCopiedUse a different Browser!pip install --upgrade -q "google-meridian[and-cuda]" import numpy as np import pandas as pd import altair as alt import tensorflow as tf import tensorflow_probability as tfp from IPython.display import display, HTML from meridian import constants from meridian.data import load from meridian.model import model from meridian.model import spec from meridian.model import prior_distribution from meridian.analysis import analyzer from meridian.analysis import visualizer from meridian.analysis import optimizer from meridian.analysis import summarizer def show(chart_or_obj, title=None): if title: display(HTML(f"<h3 style='font-family:sans-serif'>{title}</h3>")) …

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