What is model retraining?
Model retraining is the process of updating a predictive model with newer data so it keeps pace with changes in customer behavior and markets. It's the standard fix when a model's accuracy starts to decline.
Last reviewed .
When to retrain
Some teams retrain on a fixed schedule, such as monthly or quarterly. Others retrain when monitoring shows model drift, or after a major change like a new product, pricing change, or tracking update.
A schedule is simple and predictable. A trigger-based approach saves effort but needs reliable monitoring.
Retraining safely
Train the new version on recent, clean data, then test it on data it hasn't seen. Only replace the current model if the new one performs at least as well.
Keep a record of each version and the data it used. That makes it possible to roll back and to explain why predictions changed.
Common questions
How often should you retrain a model?
It depends on how quickly your data and customer behavior change. Monitor accuracy over time and retrain when it slips, or set a regular schedule as a safeguard.
Does retraining always improve a model?
No. If the new data is poor or too limited, a retrained model can perform worse. Always test it against the current version before switching.
What data should you use to retrain?
Use recent, accurate data that reflects how customers behave now, along with enough history to cover seasonal patterns.
Is retraining the same as fine-tuning?
They're related but not the same. Retraining refreshes a predictive model with new data, while fine-tuning adapts a pretrained model, such as a language model, to a specific task.
Want help putting this to work?
Tell us where your marketing stands today, and we'll show you what to do first.