What is “spam pattern detection” and how to avoid it?

Spam pattern detection uses machine learning to identify bulk automated sending that differs from genuine communication.

Patterns filters detect:

Content similarity: Same or nearly same text across many messages

Timing regularity: Sends at predictable intervals

Infrastructure fingerprints: Same headers, links, or tracking domains

Volume anomalies: Spikes inconsistent with normal sending

Engagement patterns: Low opens, no replies, high complaints

How to reduce pattern detection:

Content variation: Personalization, spintax, multiple template versions

Timing variation: Random delays, varied sending windows

Volume management: Gradual ramp, sustainable rates

Engagement focus: Better targeting to improve engagement metrics

What doesn't help:

Minor word changes to templates

Tricks intended to fool filters

Ignoring fundamental quality issues

Sending more to compensate for low engagement

Pattern detection is sophisticated. The best approach is genuine quality and relevance, not technical workarounds. Filters are trained on spam. Be different from spam, and you'll be treated differently.