Predictive routing is enabled by setting up Einstein Case Classification, then letting its predicted fields feed your existing routing. This is a high-level path; check current data requirements and your Service Cloud Einstein license before starting.
- Confirm the license and prep your data
Verify you have the Service Cloud Einstein add-on (or Try Einstein for one model). Then clean your closed cases so the field you want to predict has consistent, accurate values across a large enough sample.
- Create a classification model
In Setup, create an Einstein Case Classification model. Choose which closed cases it learns from and which field it predicts, such as Priority, Reason, or Type. Build the model so Einstein can train on your history.
- Review predictions before automating
Run the model in recommendation mode so agents see suggested values without them being applied automatically. Compare predictions against what agents and closed cases actually show to gauge accuracy.
- Let predictions drive routing
Once accuracy holds, allow the predicted field values to populate automatically. Your existing assignment rules or skills-based routing then act on those values to send each case to the right agent or queue.
The case field the model fills in, like Priority, Reason, Type, or a custom picklist. Pick fields your routing rules already depend on.
The set of historical closed cases the model learns from. Needs roughly 400 cases minimum (1,000+ ideal), with values present in the target field.
Whether predictions are shown as recommendations or applied automatically. Start with recommendations, then widen automation as accuracy proves out.
How many models you run per app. Try Einstein allows one; the full add-on allows up to five, useful for different record types or regions.
- Predictive routing is not a standalone setting. It is Einstein Case Classification feeding values into your existing assignment or skills-based routing.
- Thin or messy closed-case history produces weak predictions. Fix data quality before training, not after.
- Below roughly 400 closed cases with a value in the target field, Einstein cannot build a reliable model for that field.
- Accuracy can drift as your case mix changes. Review prediction performance on a regular cadence, not just at launch.