How to detect fake or automated replies?

Automated replies inflate response metrics without representing genuine engagement. Identifying them improves measurement accuracy.

Common automated replies:

Out-of-office: Vacation or travel auto-responses

Left company: Former employee notifications

Mailbox full: Auto-generated bounce messages

Ticket confirmations: Support system auto-replies

Security scans: Some security tools reply to probe messages

Detection signals:

Subject line patterns (Out of Office, Automatic Reply)

Standard phrases (I am currently out, no longer with)

Immediate response timing (faster than human possible)

Identical structure across multiple replies

Reply-to addresses pointing to no-reply addresses

Handling in metrics:

Filter automated replies from engagement calculations

Track them separately for list hygiene purposes

Out-of-office may indicate timing issues

Left company signals data decay

Tool capabilities:

Many cold email tools auto-detect common patterns

Custom filters can catch additional cases

Manual review still needed for edge cases

Clean data leads to accurate decisions.