AI / ML
Model Drift
The gradual degradation of a machine learning model's accuracy over time as real-world conditions change. In marketing, model drift happens when consumer behavior shifts, competition changes, platform algorithms update, or seasonal patterns evolve. A model trained on Q1 data will perform increasingly poorly through Q2-Q4 without retraining. Compound learning systems counteract drift through continuous feedback loops. Static models suffer from drift silently until performance degrades noticeably.
Why it matters
A model accurate at training becomes wrong as conditions move, and it does so silently, since nothing about it announces the change.
In practice
Hold out recent data and score against it on a schedule. Accuracy measured only at training time tells you nothing about today.
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