How can predictive automation use AI or scoring models?

AI and predictive models enhance automation intelligence:

Prediction types: Purchase propensity: Likelihood to buy. Churn risk: Probability of leaving. Engagement prediction: Expected response to message. Optimal timing: Best moment to send.

Automation applications: Trigger automation when churn risk exceeds threshold. Select content based on predicted interests. Adjust frequency based on engagement prediction. Optimize send time per recipient.

Model integration: Scores calculated externally, synced to ESP. Real-time scoring via API at decision points. Pre-calculated scores stored as custom fields.

Benefits: Proactive rather than reactive targeting. Resource focused on highest-potential recipients. Continuously improving as models learn.

Considerations: Model accuracy affects automation quality. Need sufficient data for reliable predictions. Regular model retraining and validation.

Predictive automation anticipates rather than reacts. Intervention before problems, not after.