Marketing Measurement Has Its Own Schrödinger’s Cat Problem

According to Quantum Mechanics:

Before measurement, a particle exists in multiple possible states (a superposition).
But the moment you observe it, reality “collapses” into one observed outcome.

Schrödinger illustrated this paradox with his famous cat experiment.

A cat is placed inside a sealed box with a radioactive trigger mechanism.
Until the box is opened and observed, quantum mechanics implies the cat exists in a superposition: Both alive and dead.

Only observation resolves the uncertainty into one realized state.

To explain this, Many Worlds Theory (MWT) proposes:

Every possible outcome actually happens, but in separate branching universes !!

So in one branch, the cat lives. In another, the cat dies.

Now whether one agrees with MWT, there is an interesting parallel to Marketing Mix Modeling (MMM).

📌 Bayesian MMM=Marketing’s MWT.

In MMM, we observe realized sales. We observe realized media spends.
The campaign has already happened.

Yet instead of treating the observed world as realized world, Bayesian MMM introduces a distribution of possible parameter realities around it.

A sort of posterior universe of possibilities. Different contribution / coefficient realities.

Marketing data is not in quantum superposition.

– The sales already happened.
– The tv spends already happened.
– The Meta / TikTok/ Google impressions already happened.

Once the outcome is realized, there is no unresolved state left to observe.

The customer did not simultaneously buy and not buy the product across parallel universes.

Even the Bayesian statistical interpretation feels wrong.

Let’s take beam splitter experiment for analogy, the electron ultimately takes one realized path when measured.
A Frequentist confidence interval behaves similarly. Either the interval captures the true parameter or it does not.

There is one realized parameter value in the world.
The uncertainty lies in our estimation procedure across repeated samples, not in the parameter existing simultaneously across multiple realities.

By contrast, Bayesian credible intervals often get interpreted as if the parameter itself exists probabilistically across many possible states.

And that is precisely why I often question the philosophical motivation for taking a Bayesian approach in the first place.

📌 Frequentist MMM is better.

In Frequentist MMM, we ask “What is the single best explanation of the realized world we observed?”

Not: “What are all the plausible worlds that could have generated this?” This part is same budget optimization scenario.

I also hence believe goodness of fit matter more than uncertainty theater.

Instead of probing posterior universes, I would rather ask:

▪️Does the attribution align with business reality?
▪️Are coefficient signs stable under bootstrap?
▪️Does the model generalize under perturbation?

The task is not exploring infinite possible worlds. The task is explaining this world correctly.