Summary
Email providers make filtering decisions based on complex algorithms that consider a wide array of factors, often influenced by user behavior and assumptions about sender intent. These factors include sender reputation, engagement metrics, content quality, list hygiene, IP address reputation, sending frequency, complaint rates, sender information completeness, adherence to email standards, blocklist status, and feedback loop participation. Key assumptions include that users accurately report spam, senders with poor reputation or hygiene send unwanted emails, low engagement indicates irrelevant content, and improper authentication suggests malicious intent. The overall goal is to deliver the 'least bad' email that recipients want to see while minimizing spam and phishing attempts. Bayesian learning algorithms are also utilized to identify senders of authentic and desirable content.
Key findings
- Multifaceted Filtering: Email filtering is a complex process involving numerous interconnected factors, not solely based on identifying outright spam.
- User Behavior Impact: User feedback, such as spam reports and engagement levels, significantly shapes filtering decisions.
- Authentication is Critical: Proper authentication (SPF, DKIM, DMARC) is essential for establishing sender legitimacy and avoiding being flagged as malicious.
- Reputation Matters: Sender reputation, both domain and IP-based, plays a dominant role in determining deliverability.
- Content Analysis: Email content, including keywords, formatting, and links, is scrutinized to identify potentially harmful or unwanted messages.
- List Hygiene is Crucial: Maintaining a clean and engaged email list is vital for positive deliverability outcomes.
- Algorithmic Learning: Providers employ algorithms that learn from sender behavior and user interactions to continuously refine filtering accuracy.
- The 'Least Bad' Philosophy: Filtering decisions sometimes prioritize delivering the 'least bad' email, rather than strictly eliminating all potential spam.
Key considerations
- Monitor Reputation: Consistently monitor and protect your sender reputation across various metrics.
- Improve Engagement: Focus on delivering valuable, engaging content to encourage opens, clicks, and positive user interactions.
- Implement Authentication: Ensure robust email authentication protocols are properly configured and maintained.
- Prioritize List Hygiene: Regularly clean and segment your email list to remove inactive or unengaged subscribers.
- Adhere to Standards: Comply with all relevant email standards and best practices to avoid being flagged as non-compliant.
- Monitor Feedback Loops: Actively monitor feedback loops and address any spam complaints promptly and effectively.
- Stay Informed: Continuously stay updated on the latest email filtering algorithms, best practices, and emerging trends.
- Provide Value: Focus on providing genuine value to your subscribers and building a trustworthy relationship
What email marketers say
10 marketer opinions
Email providers make filtering decisions based on a variety of factors, often relying on assumptions about sender behavior and user preferences. These decisions are influenced by sender reputation, engagement metrics, content quality, list hygiene, IP address reputation, sending frequency, complaint rates, and the completeness of sender information. Providers assume that poor reputation, low engagement, spam-like content, poorly maintained lists, a history of sending spam from an IP address, sudden spikes in sending volume, high complaint rates, and misleading sender information are indicators of unwanted or harmful emails. They also operate under the assumption that users report spam correctly and that they use feedback loops to monitor and address complaints.
Key opinions
- Sender Reputation: Email providers heavily rely on sender reputation, assuming senders with poor reputation are more likely to send unwanted emails.
- Engagement Metrics: Low engagement rates (opens, clicks) lead providers to assume content is irrelevant, filtering emails into spam/promotions.
- Content Analysis: Spam-like keywords, poor formatting, and excessive links trigger filters, based on the assumption that content is unwanted.
- List Hygiene: Poorly maintained email lists damage sender reputation, as providers assume these senders are more likely to send spam.
- IP Reputation: The IP address's sending history matters; providers assume an IP with a spam history will continue to send spam.
- Sending Frequency: Sudden spikes or excessive frequency raise red flags, with providers assuming such patterns indicate spam campaigns.
- Complaint Rates: High spam complaint rates signal unwanted content, resulting in aggressive filtering by email providers.
- Sender Information: Incomplete or misleading 'From' information leads providers to suspect malicious intent, resulting in filtering.
- User Feedback: Providers often assume that users know how to report spam correctly, with user reports significantly influencing filtering decisions
Key considerations
- Monitor Reputation: Actively monitor your sender reputation and take steps to improve it, as this is a primary factor in filtering decisions.
- Improve Engagement: Focus on creating engaging content that encourages opens and clicks to avoid being filtered as irrelevant.
- Optimize Content: Avoid spam-like keywords and formatting to prevent content-based filtering.
- Maintain List Hygiene: Regularly clean your email list to remove inactive or invalid addresses, preventing deliverability issues.
- Warm-Up IP Addresses: Establish a good sending history for your IP address to avoid being flagged as a source of spam.
- Control Sending Volume: Gradually increase sending volume to avoid triggering filters that detect sudden spikes in email traffic.
- Minimize Complaints: Ensure your emails are relevant and targeted to avoid high spam complaint rates.
- Provide Accurate Information: Ensure that 'From' information and contact details are complete and accurate to build trust with providers.
- Implement Feedback Loops: Actively monitor and respond to feedback loops to address spam complaints and improve deliverability.
- A/B Test campaigns: A/B test email campaigns to find out what type of content and subjects work best with your audience to improve deliverability.
Marketer view
Email marketer from StackExchange shares that sending frequency and volume can influence filtering. Providers assume that sudden spikes in sending volume or excessively frequent emails are indicative of spam campaigns, leading to stricter filtering.
2 Apr 2022 - StackExchange
Marketer view
Email marketer from Email Marketing Forum shares that engagement metrics like open rates and click-through rates play a crucial role. Providers assume low engagement indicates irrelevant or unwanted content, resulting in emails being filtered into spam or promotions tabs.
20 Sep 2022 - Email Marketing Forum
What the experts say
2 expert opinions
Email providers make filtering decisions based on complex algorithms heavily influenced by user behavior. A key assumption is that users accurately report spam. The goal of filtering isn't simply to block spam but to deliver the 'least bad' email. Gmail's filtering algorithm (as of 2011) uses a Bayesian-trained system to learn which senders provide authentic emails that users want to see.
Key opinions
- Complex Algorithms: Filtering algorithms are intricate and consider multiple factors beyond just identifying spam.
- User Influence: User behavior, particularly spam reporting, significantly impacts filtering decisions.
- Least Bad Approach: Filtering aims to deliver the most relevant emails, even if they aren't perfect, and avoid false positives.
- Bayesian Learning: Gmail's algorithm uses Bayesian learning to identify senders that send authentic and desirable email, prioritizing those senders.
Key considerations
- Understand User Behavior: Analyze user engagement and feedback to optimize email content and sending practices.
- Focus on Authenticity: Prioritize sending genuine, valuable content that recipients want to receive to improve deliverability.
- Monitor Spam Reports: Pay close attention to spam complaints and take corrective actions to avoid being flagged.
- Stay Informed: Keep up with the latest filtering algorithm updates and best practices to adapt your email strategy.
Expert view
Expert from Spam Resource shares insight into the 2011 Gmail spam filtering update and the algorithm they use. He mentions about Bayesian-trained algorithm learning senders that send authentic email that recipients want to see.
27 Oct 2022 - Spam Resource
Expert view
Expert from Word to the Wise explains that filtering algorithms are complex and influenced by user behavior. They share that providers assume users know how to report spam correctly, and that filtering is often about finding the 'least bad' email to deliver, rather than just blocking spam.
10 Nov 2022 - Word to the Wise
What the documentation says
6 technical articles
Email providers' filtering decisions are heavily influenced by user feedback, authentication protocols, adherence to email standards, blocklist status, and feedback loop participation. Providers assume that negative user feedback indicates unwanted content, lack of authentication suggests spoofing or phishing, violations of email standards point to malicious or poorly formatted emails, being on blocklists confirms spam activity, DMARC policies are meant to be enforced, and a lack of FBL monitoring signals disregard for spam complaints. These assumptions lead to stricter filtering for emails that trigger these flags.
Key findings
- User Feedback Matters: Negative user feedback (spam reports, unsubscribes) significantly impacts deliverability and triggers stricter filtering.
- Authentication is Key: Proper email authentication (SPF, DKIM, DMARC) is crucial for establishing sender legitimacy and avoiding filtering.
- Standards Compliance: Adhering to email standards outlined in RFCs is essential for ensuring deliverability and avoiding being flagged as malicious.
- Blocklist Impact: Being listed on blocklists (e.g., Spamhaus) results in immediate and severe filtering due to the assumption of confirmed spam activity.
- DMARC Enforcement: Implementing a DMARC policy gives providers clear instructions on how to handle unauthenticated emails, with the assumption that you want them to enforce the policy.
- FBL Monitoring: Actively monitoring and responding to feedback loops (FBLs) is important for managing spam complaints and maintaining deliverability.
Key considerations
- Monitor User Feedback: Regularly monitor user feedback and take steps to address any negative responses to improve deliverability.
- Implement Authentication: Ensure that SPF, DKIM, and DMARC are properly configured to authenticate your emails and establish legitimacy.
- Adhere to Standards: Comply with email standards outlined in RFC documents to avoid being flagged as poorly formatted or malicious.
- Avoid Blocklists: Take proactive measures to avoid being listed on blocklists by maintaining good sending practices and monitoring your reputation.
- Implement DMARC Policy: Implement a DMARC policy to control how providers handle unauthenticated emails using your domain.
- Monitor Feedback Loops: Actively monitor and respond to feedback loops to address spam complaints and maintain a positive sending reputation.
Technical article
Documentation from Validity (formerly ReturnPath) describes the use of feedback loops (FBLs) to monitor spam complaints. Providers assume that senders not actively monitoring and responding to FBLs are less concerned about sending unwanted emails, leading to continued filtering.
31 Aug 2021 - Validity
Technical article
Documentation from Spamhaus explains that being listed on blocklists (e.g., Spamhaus Blocklist) can severely impact deliverability. Providers assume that IP addresses or domains listed on these blocklists are confirmed sources of spam, leading to immediate filtering.
21 Jan 2024 - Spamhaus
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