What is the effect of recency bias in machine learning filters?
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Recency bias is exactly what it sounds like. Modern inbox filters weight your last 30 days of sending way more heavily than your last 12 months. If you had a rough week, the algorithm notices. If you had a rough week six months ago, it mostly forgot.
This cuts both ways, and that's the part most senders miss.
On the downside: a single bad campaign (a reactivation blast to a stale list, a big complaint spike, a misfired promo) can tank your placement at Gmail or Outlook within days, even if your prior reputation was spotless. The filter is asking "what is this sender doing right now?", not "what did they do in 2024?"
On the upside: you can recover faster than sender-score folklore suggests. Two to four weeks of tight, engaged sending will visibly lift placement. You don't need to "rebuild from zero" after a bad stretch. You need to show the filter a clean recent pattern.
The practical rule: treat your last 14-30 days like they're the only days that exist. Pause sends to cold segments after a complaint spike. Drop re-engagement campaigns until you've stabilized. Send your most engaged segment first every morning so the day's early signal is strong. Your sender reputation is a rolling average, not a permanent record.
If you're in recovery mode and not sure where you stand, our SOS hotline is the fastest way to get a human to look at your numbers. Or start with a quick engagement audit to see which segments are dragging down the recent signal.
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