In this tutorial we build a complete Bayesian marketing mix modeling workflow with Google Meridian. First we install the library with GPU‑enabled TensorFlow support and verify that a GPU is available for fast sampling. We then load a simulated geo‑level dataset that contains media impressions, spend, control variables, promotions, conversions, population and revenue. After a quick look at the shape, date range and spend distribution we map the raw columns to Meridian’s expected schema using CoordToColumns, specifying time, geo, KPI, revenue per KPI, media, media spend, organic channels, population and any non‑media treatments.
Next we set interpretable ROI‑based priors—a log‑normal distribution with mean 0.2 and sigma 0.9—and create the model specification. The Meridian model is initialized with the prepared input data. We draw 500 samples from the prior to check its behavior, then fit the model using the No‑U‑Turn Sampler across seven chains with suitable adaptation, burn‑in and keep steps. Posterior sampling is followed by convergence diagnostics (R‑hat plots), prior vs. posterior comparisons and model‑fit visualizations to ensure reliable estimates.
Using the MediaSummary and MediaEffects analyzers we examine channel contributions, ROI, marginal ROI, effectiveness, adstock decay and Hill saturation curves. The Analyzer API lets us extract raw posterior ROI draws, compute means and credible intervals, and calculate probabilistic channel comparisons. Finally we run the BudgetOptimizer under both a fixed‑budget scenario and a flexible budget targeting a specific ROI, visualizing the recommended allocation, spend changes and expected outcome gains. HTML reports are generated for model results and optimization summary, and the fitted model is saved to disk for later reuse or reloading.
This end‑to‑end process gives marketing teams a rigorous, uncertainty‑aware way to measure media performance, compare channels, and allocate budgets optimally, all while validating model stability before acting on the insights.
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