In one line
Multicollinearity happens when independent variables you put into a regression (say, spend on multiple channels) always move up and down together, so the model can't tell which one is actually driving the outcome.
Why it matters
This is a common trap in marketing data. If you always scale two channels together, revenue going up can't be cleanly attributed to one or the other — coefficients become unstable and can even flip sign. It's especially common in Marketing Mix Modeling, where channel-level contribution is being regressed.
How to spot it
- Check pairwise correlation between channels before modeling.
- Coefficients with counterintuitive signs or unusually large standard errors are a warning sign.
- The real fix isn't statistical — it's data design: you need periods where channels moved independently for the model to tell them apart.
Go deeper
See how multicollinearity distorts MMM results in What is MMM.