Attribution bias refers to systematic errors in assigning credit for outcomes, often caused by oversimplified models or incomplete data.
It affects decision-making and investment.
Examples in Action
- Over-crediting last-touch channels
- Ignoring assistive interactions
- Misinterpreting cross-device behaviour
- Inflated channel performance claims
Typical Outcomes / Results
- Misallocation of resources
- Reduced ROI
- Improved clarity with better modelling
- More balanced decision-making
This glossary entry reflects common attribution challenges.