How do filters use engagement across users (crowdsourced learning)?

Filters use crowdsourced learning to aggregate engagement signals across all users, building collective intelligence about each sender.

When many users mark a sender as spam, the filter learns that sender is problematic for everyone, not just individuals. This aggregate feedback influences filtering globally.

Positive engagement works similarly. If most recipients engage positively with a sender, the filter builds confidence that mail from that sender is wanted.

Crowdsourced signals help detect new threats quickly. When a spam campaign starts, early victim reports protect later targets as the filter learns the pattern.

This collective learning also helps legitimate senders. Consistent positive engagement across users builds reputation that benefits deliverability broadly.

The wisdom of the crowd informs the filter. What many users demonstrate teaches the system about every sender.