How do filters detect “fake” engagement (bots, internal opens)?

Filters have developed sophisticated methods to detect fake engagement that attempts to inflate metrics artificially.

Bot patterns are identifiable through timing, user agents, IP addresses, and behavior. Bots open at inhuman speeds, use identifiable signatures, and originate from known bot networks.

Internal opens from security scanning are recognized. When corporate email gateways pre-open links and images, filters distinguish this from genuine user engagement.

Timing analysis catches unrealistic patterns. Genuine opens distribute naturally over time. Fake opens may cluster unnaturally.

Behavioral correlation matters. Genuine engagement involves open followed by read time followed by possible click. Fake engagement often involves opens without subsequent human behavior.

Filters have learned to distinguish real sailors from decoys. Artificial inflation is detected and discounted.