How can you predict when a deferred message will bounce?

Predicting deferral outcomes improves queue management:

Prediction factors:

Time in queue: Longer deferrals less likely to resolve. Retry count: More retries without success indicates problem. Error consistency: Same error each retry suggests persistent issue. Historical patterns: This address's past behavior.

Conversion probability indicators:

Deferral reason (greylisting resolves fast; full mailbox may not). Provider involved (some providers resolve faster). Time of day patterns. Volume patterns.

Modeling approach:

Track historical deferral-to-bounce rates. Segment by error type and provider. Build probability curves over time. Apply to current deferrals.

Practical application:

Prioritize retry for likely-to-succeed. Predict final outcomes for reporting. Optimize queue resource allocation.

Prediction turns deferred uncertainty into probabilistic insight. Not all deferrals are equally likely to resolve.