How do email service providers process feedback loop (FBL) emails to identify users and manage suppressions?

Key findings

• Data encoding: FBL messages, especially from major mailbox providers like Comcast, often encode or hash the recipient's email address (e.g., SHA256 strings) rather than providing it in plain text. This is a privacy measure.
• Internal identification: ESPs primarily rely on unique identifiers embedded within the email headers (such as Message-ID or custom X-Headers) to match FBLs to specific subscribers and campaigns. This allows them to accurately pinpoint who complained, even without seeing their direct email address.
• Suppression management: The core purpose of FBLs for ESPs is to identify users who marked an email as spam so they can be immediately added to a suppression list, preventing future mailings to that address. This helps maintain a good sender reputation.
• Third-party management: Some mailbox providers (MBPs) or FBL providers (e.g., Validity) manage FBLs for multiple ISPs, centralizing the reporting process for senders. This indicates a standardized approach across various providers.
• Historical practice: The elision of email addresses in FBLs is not a new development, such as a recent GDPR change, but has been a standard practice since the earliest FBL implementations, even before the Abuse Reporting Format (ARF) became widespread.

Key considerations

• Robust internal tracking: ESPs must have a robust internal system to uniquely identify recipients and campaigns, typically by including unique identifiers in email headers that can be cross-referenced when an FBL report is received. This is crucial for managing spam complaints.
• Automated processing: Efficient FBL processing requires automated systems to ingest reports, parse the relevant identifiers, and trigger immediate suppression actions. Manual processing is impractical given the volume of complaints.
• Proactive suppression: Upon receiving an FBL, the implicated address should be immediately removed from active mailing lists. This is a critical step in preserving sender reputation and avoiding further spam complaints, which also ties into how to respond to abuse complaints.
• Compliance with standards: Adhering to specifications like RFC 6449 (Complaint Feedback Loop Operational Requirements) is vital for proper FBL integration and processing, ensuring compatibility with various mailbox providers.

Key opinions

• Difficulty in decoding: Marketers frequently express frustration over the inability to directly decode encoded email addresses from FBL reports, making it hard to manually identify the complainant.
• Reliance on internal identifiers: It is widely accepted that marketers must rely on unique identifiers (e.g., custom X-Headers or Message-IDs) embedded in their outgoing emails to link FBLs back to specific subscribers.
• Importance for deliverability: FBLs are seen as a critical signal for email deliverability, directly influencing sender reputation. Failing to process them leads to a higher spam complaint rate, which can negatively impact inbox placement.
• Need for automated solutions: There's a recognized need for robust, automated solutions—either in-house or third-party—to efficiently process FBL emails and manage suppression lists, especially after changes in how some providers (like Microsoft) handle IMAP access.

Key considerations

• Proactive list hygiene: Marketers must prioritize timely FBL processing to ensure that complaining users are quickly removed from mailing lists. This practice is fundamental to good email list hygiene and preventing future unwanted emails.
• Integrate FBL data: Effective FBL management requires integrating the data received from mailbox providers into existing CRM or email marketing platforms. This ensures that suppression lists are accurately updated and maintained.
• Understand privacy measures: Marketers should understand that the encoding of email addresses in FBLs is a privacy feature, not a bug. They need to adapt their processing methods to work with hashed or unique identifiers rather than expecting plain text addresses.
• Continuous monitoring: Regularly monitoring FBL reports is crucial for identifying trends in spam complaints and adjusting sending practices accordingly. This continuous feedback loop helps in improving deliverability over time. Marketers can also consult resources like the MyEmailVerifier Blog for further insights on spam complaints and feedback loops.

Key opinions

• Encoding is standard: The practice of encoding or hashing recipient email addresses in FBLs is standard and has been present since the very first FBL implementations, predating modern privacy regulations.
• Alternative identification: ESPs must leverage other unique identifiers within email headers, such as the Message-ID, to reliably identify the complaining user and associate it with a sent email. This also plays a part in how ISPs track email engagement.
• Validity's role: Validity is noted as a key manager of FBLs for a significant number of mailbox providers, centralizing the delivery of these reports to senders.
• In-house solutions are common: Many organizations, particularly larger senders, build their own FBL processing systems to ensure full control over data integration and suppression logic, even leveraging open-source codebases like Abacus.
• Beyond plain text: The days of receiving plain text email addresses in ARF emails are behind us, reinforcing the need for senders and ESPs to adapt to processing FBLs using other header information.

Key considerations

• Prioritize suppression: The primary goal of FBL processing is to promptly add complaining users to a suppression list. This action is critical for maintaining good sender reputation and compliance. This ties into how FBLs function for major providers.
• Strategic data logging: Senders should ensure their systems log unique identifiers like Message-ID or custom X-Headers for every email sent, enabling them to cross-reference FBL reports effectively.
• Consider custom solutions: While third-party tools exist, experts often recommend developing in-house FBL processing solutions for greater flexibility, long-term maintenance, and the ability to generate custom reports tailored to specific business needs.
• Understand the intent: The primary intent of FBLs is to provide a signal about user dissatisfaction, not to reveal subscriber identities. Senders should focus on using this signal to improve their email program and adhere to best practices. This is a key aspect of handling spam complaints and feedback loops.

Key findings

• Privacy by design: FBL specifications are built with privacy in mind, meaning they do not transmit the actual email address of the complaining user. This design choice prevents the misuse of FBL data for purposes other than complaint management.
• Unique identifiers: The RFCs suggest that senders should use unique identifiers, such as Feedback-ID headers (as per RFC 6449), to track and associate FBL reports with specific recipients or campaigns.
• ARF format: FBLs are typically delivered in the Abuse Reporting Format (ARF), which provides structured data about the complaint, including original message headers, but without revealing the complainant's direct email address.
• Purpose of FBLs: Documentation consistently states that the primary purpose of FBLs is to enable senders to identify and suppress users who have reported their emails as spam, thereby improving sender reputation and reducing unsolicited mail.

Key considerations

• Standardized implementation: ESPs and senders should adhere to the guidelines outlined in RFCs (e.g., RFC 6449) for FBL implementation and processing. This ensures compatibility and proper reception of complaint data from various mailbox providers.
• Utilize Feedback-ID: Implementing the Feedback-ID header in outgoing emails is crucial for unambiguous complaint attribution. This header should contain unique identifiers that link back to the specific subscriber and campaign within the sender's system.
• Automated suppression: Documentation implicitly calls for automated processing of FBLs to ensure timely suppression of complaining users. Manual intervention is not scalable for effective complaint management. For technical details, refer to guides on email authentication protocols.
• Data interpretation: ESPs need to develop systems capable of parsing ARF-formatted FBL messages and extracting relevant data points, such as the Feedback-ID or Message-ID, to effectively update suppression lists and internal metrics. Knowing the meaning of DMARC tags can also provide additional context for deliverability issues.