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.
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.
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.
17 Mar 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.
5 Sep 2022 - Email Marketing Forum
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.
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.
12 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.
25 Oct 2022 - Word to the Wise
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.
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.
15 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.
6 Jan 2024 - Spamhaus
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