Einstein Case Classification
Einstein Case Classification is a Service Cloud AI feature that automatically predicts and populates picklist fields on incoming Cases - Type, Reason, Priority, Sub-Type - based on patterns learned from your org's historical case data.
Definition
Einstein Case Classification is a Service Cloud AI feature that automatically predicts and populates picklist fields on incoming Cases - Type, Reason, Priority, Sub-Type - based on patterns learned from your org's historical case data. Admins pick the fields to auto-classify, Salesforce trains a model on the historical Cases that have those fields already set, and once deployed the model predicts values on new Cases so routing and reporting downstream run against clean, consistent data.
In plain English
“Einstein Case Classification is an AI feature that automatically fills in case fields like Type, Reason, and Priority based on patterns from historical cases. Instead of agents having to classify every case manually, the AI predicts the values from the case content.”
Worked example
At Coastwise Insurance, the service operations lead enables Einstein Case Classification on the Type, Reason, and Priority fields. The model trains on 18 months of historical Cases where those fields were set manually and reaches 87% accuracy. After deployment, new incoming Cases arrive with predicted values pre-populated (or suggested to the agent based on confidence). Omni-Channel routing - which keys off Priority and Type - becomes measurably more accurate because fewer Cases sit in queues with missing or defaulted classification values.
Why Einstein Case Classification matters
Einstein Case Classification is a Service Cloud AI feature that automatically predicts and populates field values on incoming cases based on patterns learned from historical case data. It uses machine learning to analyze the case description, subject, and other text fields, then predicts likely values for picklist fields like Type, Reason, Priority, and custom classifications. Predictions can be applied automatically or surfaced as suggestions for agents to confirm.
Case Classification reduces the manual work of categorizing cases and improves consistency by applying the same model to every case. Manual classification varies by agent and is often skipped under pressure, leading to inconsistent data that undermines reporting. Einstein Case Classification produces consistent classifications based on learned patterns, which improves the accuracy of downstream reports, routing decisions, and SLA enforcement. The model needs sufficient historical case data to train accurately, so it works best in mature support organizations with substantial case history.
How to set up Einstein Case Classification
Einstein Case Classification auto-populates picklist fields on inbound Cases — Type, Reason, Priority, Sub-Type — based on patterns learned from your org's historical Case data. Reduces agent triage time and produces clean data for downstream routing. Requires Einstein for Service licensing.
- Confirm Einstein for Service licensing
Setup → Einstein Setup. Einstein Case Classification is part of Einstein for Service add-ons.
- Open Setup → Einstein Case Classification
Setup gear → Quick Find: Einstein Case → Einstein Case Classification.
- Click New Predictive Field
Configure one model per picklist field you want to auto-classify.
- Pick the Case picklist field to predict
Type / Reason / Priority / custom picklists. Each gets its own model.
- Pick training data filter
Which historical Cases to learn from. Default: all closed Cases. Restrict to recent / specific record types if patterns shifted recently.
- Train the model
Salesforce builds the model from training data. Takes minutes-to-hours depending on volume.
- Review accuracy stats
Model card shows accuracy per picklist value. Below 60-70% accuracy means the model isn't reliable — refine training data or skip the field.
- Activate predictions on new Cases
Once activated, new Cases auto-fill the predicted field. Agents see Einstein-suggested values with a magic-wand icon — accept or override.
Picklist field to auto-classify.
Which historical Cases to learn from.
Minimum model confidence for predictions to populate.
Whether predictions write directly or just suggest to agents.
- Model accuracy depends on training data quality. Cases with inconsistent / wrong picklist values produce a confused model — clean training data first.
- Predictions can be wrong. Auto-Apply mode silently writes wrong values; Suggest mode lets agents override. Start with Suggest; promote to Auto-Apply only after observing accuracy.
- Models retrain on schedule (weekly default). If your org's case patterns shift (new product launch, new support tier), accuracy can drop temporarily until retraining catches up.
How organizations use Einstein Case Classification
Uses Einstein Case Classification to auto-populate Case Type and Reason on every new case. The consistent classifications improved their support reporting accuracy noticeably.
Combined Case Classification with Case Routing so cases are automatically classified and routed to the right queue without agent involvement.
Trains their Case Classification model on a year of historical cases, then retrains quarterly to capture new patterns as products and case types evolve.
Test your knowledge
Q1. What does Einstein Case Classification do?
Q2. What's the value over manual classification?
Q3. What does the model need to train accurately?
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