A Working Checklist for Spam Filter Trigger Words

• All-caps subject lines or body text, especially combined with punctuation stacking like multiple exclamation points.
• Excessive links in a single email, particularly several links to different domains rather than one clear call to action.
• Image-heavy emails with little actual text, since filters weight the ratio between visible text and other content.
• Inconsistent formatting that suggests a template with mismatched fonts or spacing left over from a copy-paste.

• Urgency language stacked in a single message, such as multiple phrases pushing immediate action.
• Financial promises or guarantees, which filters weight heavily regardless of how legitimate the underlying offer is.
• A subject line that doesn't match the body's actual content, which filters increasingly cross-check.
• Generic greeting patterns with no personalization at all, which correlate with mass-blast behavior more than any single word does.

Filters weigh dozens of signals together rather than flagging on any single trigger word, which means a message can include a word from an old banned list and still land fine, or avoid every listed word and still get flagged, if the surrounding pattern reads as spam-like overall. Sender reputation, engagement history and formatting now carry more weight than word choice alone, so a team that spends its review time purging specific words while ignoring sender reputation and formatting is optimizing the wrong lever.

Instead of scanning copy for banned words, read each draft and ask whether it would look like spam to a stranger seeing it cold, checking formatting consistency, link count and whether urgency language is stacked rather than used once. This catches the patterns that actually matter faster than cross-referencing a word list, and it scales better as filters keep evolving past whatever list was current a year ago.

For example, imagine a draft with an all-caps subject line and two exclamation points, four links pointing to three different domains, and a line promising guaranteed savings. None of those problems is a banned word, so a word scan would miss all of them, while a cold read as a stranger would catch each within seconds. The fix is structural: lowercase the subject, cut to one link and one clear call to action, and remove the guarantee. Then send a small seed batch and check where it lands before rolling the template out to your full list.

Run a small test batch of any new template through seed accounts across a few major providers before rolling it out to your full list, checking where it lands (inbox, promotions, spam) rather than assuming a template that worked last quarter still performs the same way. lemlist's built-in deliverability checks catch some of this automatically, but a manual spot check on a genuinely new template is still worth the few minutes it takes.

Rather than a long checklist someone has to remember to run every time, fold two or three of the highest-value checks (link count, urgency stacking, formatting consistency) into whatever review step already happens before a new template ships. A quick habit that runs every time beats a thorough checklist that only gets used occasionally, since the templates that skip review entirely are usually the ones that end up causing the most trouble later.

Automated deliverability checks inside a sending platform can score a message's likely spam risk and flag specific lines before it ever sends, catching things a human reviewer skims past after reading the same template a dozen times. Treat the automated score as a second opinion rather than the final word, since it can miss context-specific issues (a phrase that's fine generally but risky for your specific audience or industry) that only a person familiar with your list would catch.