AI / ML
Feature Engineering
The process of selecting, transforming, and creating input variables (features) that help machine learning models make better predictions. In marketing AI, features include spend by channel, day of week, time since last creative refresh, audience saturation level, competitive CPM index, and hundreds more. The quality of features matters more than the complexity of the model. Good feature engineering is why specialized marketing AI outperforms general-purpose tools.
Why it matters
It usually matters more than model choice. The same algorithm on better-constructed inputs beats a more sophisticated one on raw fields.
In practice
Encode what a domain expert would look at: days since last purchase, spend relative to trailing average, position in the seasonal cycle.
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