How to use multiple MMMs to make better paid media decisions

After some initial setup and tinkering, any marketing mix model (MMM) can produce a convincing chart with channel decomposition, response curves, a clean R², and a recommended reallocation. But the chart won’t show how much the data shaped the result versus how much the model’s built-in assumptions did. Adstock and decay windows determine how long a channel’s effect lingers. Saturation curves affect how quickly returns diminish, driving every reallocation the model recommends. Priors and regularization (Bayesian or ridge) encode beliefs about plausible effect sizes, while seasonality and control variables influence how much lift the model…

This is a curated summary. The full story was reported by Search Engine Land.