Henry Innis

In a world-first partnership, we’ve integrated WARC intelligence into MAITE. Now, Mutinex customers can unite their internal performance data with external effectiveness context — to derive actionable growth answers on demand.

Bayesian priors are a standard component of modern Market Mix Models (MMM), used to improve model stability and incorporate business knowledge. But when used incorrectly, priors can introduce significant bias into a model: where a model can be made to appear accurate but produce misleading results based on flawed assumptions.
Let’s look at how Bayesian priors function, how their misuse creates risk, and what questions to ask to validate the integrity of your MMM.

This guide will walk you through why validation is so critical and provide a practical framework for testing your model against the five most common and challenging pitfalls.

Marketers today face a unique irony: as marketing mix modelling (MMM) becomes popular again, confusion around its definition is also growing. The acronym MMM now ambiguously refers to both media mix modelling and market mix modelling. But this isn’t mere semantics — the difference profoundly impacts business decisions.

Incrementality testing is a great way to validate models and their performance, and to hold them to account. But having them power a model solely is a short-sighted approach, pushed by inexperienced experimentation companies.

Mutinex Co-Founder and CEO, Henry Innis, recently presented at the ‘Most Contagious’ by WARC conference in NYC. This is the essence of his talk.