Bayesian MMM
Bayesian MMM explained
With sparse data, many different channel contributions may remain plausible. A Bayesian model makes the interaction between prior information and data explicit. It does not remove missing information.
**Justify the priors.** Assumptions can draw on relevant experiments or documented domain knowledge. Record their source, period and strength. A desired channel return is not a defensible prior. Check how other reasonable assumptions affect the findings.
**Interpret uncertainty.** A credible interval describes posterior possibilities under the selected model. It does not automatically include every omitted variable or data error. Non-Bayesian methods can also quantify uncertainty.
For budget planning, transparent scenarios are more useful than one apparently certain recommendation. Especially for a new channel, make clear how much the finding depends on observed data and how much on prior assumptions.
Examples
Hypothetical application
A new channel has little internal evidence. The team compares several justified priors. If the recommended budget changes substantially, it reports this uncertainty and prioritises an experiment.
Key Points
- Distinguish priors, data and posterior.
- Document prior sources and sensitivity.
- Uncertainty intervals depend on model assumptions.
- Sparse data remain a limitation.
Practical application
Ask for prior sources, sensitivity analyses and model diagnostics alongside the findings.
Useful measures
Posterior uncertainty
Variation in estimated quantities under the model assumptions.
Prior sensitivity
Changes in relevant decisions under other justified priors.
Common mistakes
- Selecting priors to produce a desired result.
- Treating a narrow interval as protection against every model error.
Sources and context
- Google Meridian: Bayesian inference
Foundations of prior, likelihood and posterior in the modelling context.
- Google Meridian: MMM as causal inference
Assumptions and limitations of causal interpretation in marketing mix models.
Frequently Asked Questions about Bayesian MMM
No. Quality depends on the data, model and assumptions. An inappropriate prior can pull an estimate in the wrong direction.
A probability distribution representing assumptions about an unknown model quantity before incorporating the current data. It should be justified and open to scrutiny.
It can incorporate prior information. With little direct evidence, the result may depend heavily on it. This dependence needs to remain transparent.
Related links
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