Summary
The common belief that specific spam trigger words instantly land emails in the spam folder is largely outdated. While terms like free or guaranteed were once primary indicators for early spam filters, modern systems are far more sophisticated. Today, email service providers (ESPs) and mailbox providers (MBPs) use advanced machine learning algorithms to assess the overall context and sender reputation rather than relying on simple keyword blacklists (or blocklists).
Key findings
- Outdated lists: Many lists of spam words are based on rules from 10-15 years ago and are largely irrelevant for major email providers today.
- Context is key: Modern spam filters evaluate the entire email, including sender reputation, engagement metrics, email content, and historical sending patterns, not just individual words.
- Machine learning: Leading mailbox providers utilize machine learning to identify spam, which means that what triggers a filter is dynamic and based on collective user behavior rather than static word lists.
- Smaller providers: Some smaller, web-hosted email providers or those using older software like SpamAssassin may still rely on more rudimentary rule-based filtering that flags specific words or phrases.
- Aggressive tactics: Overly salesy, pushy language, excessive capitalization, multiple exclamation marks, or suspicious phrasing (act now, urgent, winner) are still red flags that contribute to a negative sender reputation and higher spam scores.
Key considerations
- Focus on reputation: Prioritize building a strong sender reputation through consistent sending, engaged subscribers, and proper authentication (like SPF, DKIM, DMARC). This is far more impactful than avoiding a list of words.
- Audience engagement: Emails that receive high engagement (opens, clicks, replies) are less likely to be marked as spam, even if they contain words that might have been flagged in the past. Disengaged recipients are more likely to mark any email as spam, regardless of content.
- Content quality: While individual words are less important, the overall quality and relevance of your email content are crucial. Avoid deceptive language, misleading subject lines, or content associated with scams.
- Monitor deliverability: Regularly monitor your email deliverability and inbox placement rates. Use tools to check if your emails are landing in the inbox or spam folder. For a deeper dive into modern spam filtering, consider this resource on spam words.
What email marketers say
Email marketers often express frustration with the persistent myth that specific keywords directly trigger spam filters. Many acknowledge the existence of outdated advice, often found on ESPs' own websites or perpetuated through old blog posts. While they understand the need for caution, the focus has shifted from rigid word lists to broader content quality and sender reputation.
Key opinions
- Myth persistence: Many marketers lament that the advice about spam words is outdated but continues to be spread, sometimes even by reputable ESPs (due to old content or poor fact-checking).
- Content marketing role: It is suggested that some of this outdated advice is generated by content marketers who research and write articles without deep deliverability expertise, or by AI tools that pull from older, inaccurate sources.
- Legacy systems: There's an acknowledgment that smaller, web-hosted providers might still rely on simpler, rule-based spam filters, which could be triggered by specific words or formats (e.g., numbers in subject lines or phrases like big money).
- Focus on user experience: Ultimately, if an email looks or feels spammy to a recipient, they will mark it as such, regardless of specific keywords, and this user behavior is what truly influences modern filters.
Key considerations
- Review old content: Marketers should regularly audit their own content (especially old blog posts) to ensure they are not perpetuating outdated deliverability advice.
- Understand filter evolution: Recognize that spam filtering has evolved significantly beyond keyword matching. Focusing solely on avoiding certain words is a misdirection from the real issues affecting deliverability.
- Prioritize audience and value: Instead of obsessing over words, concentrate on providing genuine value to your subscribers, maintaining list hygiene, and ensuring your emails are expected and welcomed. This significantly reduces the likelihood of being marked as spam.
- Holistic approach: A comprehensive strategy for avoiding spam filters involves technical setup, sender reputation, and user engagement, not just content. You can find more insights on common spam trigger words on Moosend's blog.
Marketer view
Email marketer from Email Geeks sighs at the persistence of outdated advice, noting, "Common spammy language includes words like 'free' and 'guaranteed' or excessive use of exclamation marks and all capital letters." They find it frustrating that this kind of advice is still being promoted.
Marketer view
Email marketer from Moosend highlights that spam trigger words are keywords and phrases identified as red flags by email service providers like Gmail and Outlook. They suggest avoiding overly salesy or pushy language.
What the experts say
Email deliverability experts largely agree that the concept of a fixed list of spam words is outdated for major mailbox providers. Their perspective centers on the sophisticated nature of modern spam filtering, which relies heavily on dynamic machine learning models and holistic sender reputation rather than simple keyword matching.
Key opinions
- Machine learning dominance: Experts emphasize that large email providers use machine learning to identify spam. This means that spam is defined by what users report, not by a hardcoded list of words. A string might become a flag, but it's not a pre-determined blacklist.
- Contextual analysis: Spam filters now analyze the entire message, including the sender's history, recipient engagement, and overall intent. A word that might be spammy in one context is perfectly legitimate in another.
- Sender reputation first: The primary factor for inbox placement is the sender's reputation (IP and domain reputation). Good reputation allows for more flexibility in content, while a poor one makes even innocuous emails problematic.
- Behavioral triggers: User complaints (marking as spam), low engagement, and sending to spam traps are far more impactful triggers than specific keywords.
Key considerations
- Educate clients: Experts advise marketers to educate their clients and teams about the modern realities of spam filtering, moving beyond simplistic keyword avoidance. This also relates to broader subject line best practices.
- Holistic deliverability: Acknowledge that deliverability is a complex interplay of authentication, infrastructure, content quality, and user engagement. No single factor, like a keyword blacklist, dictates success or failure.
- Focus on value and engagement: The best way to ensure inbox placement is to send relevant, desired emails to an engaged audience, thus positively influencing the machine learning models of MBPs. More expert opinions on this can be found on SpamResource.
Expert view
Email expert from Email Geeks clarifies that while specific strings can become spam flags, it's no longer about a Postmaster manually updating a spammy list. Instead, modern filtering relies on machine learning to find commonalities in large volumes of mail that users mark as spam.
Expert view
Email expert from SpamResource explains that spam filters operate on a much broader range of signals than just content. They emphasize that a strong sending reputation, built on consistent engagement and low complaint rates, can often outweigh the presence of words that might have been flagged by older systems.
What the documentation says
Technical documentation and research on email deliverability typically focus on authentication protocols (SPF, DKIM, DMARC), sender reputation metrics, and the underlying algorithms used by mailbox providers. They rarely, if ever, present static lists of spam words. Instead, the emphasis is on complex systems designed to detect patterns of abuse and sender trustworthiness, reflecting a nuanced understanding of unwanted commercial email (UCE).
Key findings
- Reputation-based filtering: Official documentation from major email providers consistently points to sender reputation (including IP and domain reputation) as the cornerstone of their spam filtering decisions. Content is secondary to trust.
- Behavioral analytics: Documentation often highlights the use of advanced analytics to track recipient engagement (opens, clicks, unsubscribes, spam complaints). These behavioral signals are crucial in determining an email's legitimacy.
- Content analysis complexity: While content is analyzed, it's done through sophisticated algorithms that detect patterns indicative of spam, such as phishing attempts, deceptive links, or malicious code, rather than simple keyword matching.
- Authentication importance: Proper implementation of email authentication standards like SPF, DKIM, and DMARC is consistently emphasized as foundational for deliverability, signaling sender legitimacy to filters.
Key considerations
- Refer to official sources: When seeking advice on spam filtering, always prioritize official documentation from major mailbox providers (e.g., Gmail Postmaster Tools, Microsoft SNDS) over generic blog posts or outdated lists. This helps in understanding what impacts spam placement.
- Understand spam algorithms: Familiarize yourself with the principles of how modern spam filters work, focusing on contextual analysis, reputation scores, and user feedback loops, rather than isolated keywords.
- Adhere to best practices: Technical guidelines often emphasize opt-in practices, clear unsubscribe options, and consistent sending volumes as crucial for avoiding spam classifications, as these build a positive sender profile. For example, Textmagic's blog offers similar advice.
Technical article
Official documentation from Google Postmaster Tools outlines that spam rate metrics are based on user feedback. It indicates that if users frequently mark your emails as spam, it negatively impacts your sender reputation, regardless of specific words used in the content.
Technical article
RFC 5321 (SMTP) specifies how mail servers should interact but does not define spam content. It focuses on the technical aspects of mail transfer. The concept of spam words is a layer built on top of these foundational protocols, driven by evolving abuse patterns.
Related resources
13 resources
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