VIF multicollinearity check
Before MMM, check whether channel spend moves too tightly together to separate contribution.
End of analysisNext steps and reference material below. Not part of the analysis result.
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Channel contribution
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Why does my MMM output look wrong?
If channel spends always moved together, contribution cannot be separated.
Method and FAQOpen only when you need the methodology
See how to read VIF and what to do about it
When to use it
Use it before attributing contribution with MMM or regression, when coefficient signs contradict domain knowledge, or when standard errors are so wide that conclusions flip. A per-period channel spend panel is enough.
What it calculates
VIF is derived from how well each channel's spend is explained by the others, alongside the full correlation matrix. Higher values mean less unique variation and therefore weaker identification.
How to act
Group highly collinear channels and interpret them together, or create unique variation by staggering budget schedules. Do not report per-channel contribution numbers produced without this check.
Frequently asked questions
Does high VIF mean I cannot run MMM?
You can run it, but per-channel separation will not be trustworthy. Interpreting grouped channels, or re-estimating after varying spend patterns, is the accurate path.
Why is a coefficient negative?
Collinear inputs make estimates unstable and signs can flip. Such a negative should be read as a symptom of non-identification, not as evidence of cannibalization.
How do I create unique variation?
Stagger increases and decreases across channels, or run a channel only in selected periods or regions, so spend schedules stop moving in lockstep.