“Details matter. It’s worth waiting to get it right.” — Steve Jobs
What if the most valuable insights from your Marketing Mix Model (MMM) are hiding in what we usually consider uncertainty or noise?
What would happen if we could look at the results of the MMM models that we are using today from a different angle?
Those of us who have made MMM models using Bayesian hierarchical models have seen that these models¹ provide lots of information about each of the parameters we set up in the model. By applying rigorous and widely validated statistical techniques, we choose, for example, the mean (sometimes the median) of the posterior distribution as the value of the influence for a certain channel. Then, we consider and generate actionable insights from this value. However, the truth is that Bayesian analysis gives us as output a probability distribution of values, and the tails are frequently large with rare occurrences and exceptions. If we underestimate the information contented in these tails, we are losing a valuable opportunity. In the expression of those long tails, if we look with the proper lens, we can find very valuable insights. Actually, the basic idea for which most users use MMM models is to quantify the…