How does Google's Feedback Loop (FBL) work and what are the best practices for implementation?

Key findings

• Aggregated data: Google's FBL provides aggregate spam complaint data, not individual recipient email addresses, to prevent list washing.
• Volume requirements: Data for specific identifiers (within the Feedback-ID header) is only generated if there's a certain volume of mail and distinct user spam reports for that identifier on a given day.
• Identifier strategy: Senders should use identifiers that help pinpoint the source of spam complaints (e.g., campaign ID, client ID, traffic type), rather than trying to identify specific users.
• Policy-driven: Google's FBL is designed to help ESPs police their customers and improve overall sending practices, aligning with their goal for senders to deliver only wanted mail. For more on this, Twilio provides insight into leveraging Gmail Feedback Loop identifiers.

Key considerations

• Data granularity: Small senders or those with very granular identifiers might not see FBL data due to insufficient complaint volume for each specific identifier.
• Header format: Ensure the Feedback-ID header is correctly implemented as per Google's guidelines, typically in the format a:b:c:SenderId.
• Strategic identifiers: Consider a hierarchy of identifiers (e.g., broad categories like ESP name, then narrower like account or campaign ID) to ensure at least some level of aggregation meets Google's volume thresholds.
• Reputation impact: Sender reputation can influence whether Google shares FBL data. A poor reputation may result in less or no data being provided. For related issues, see the scope of Google Postmaster Tools Feedback Loop.

Key opinions

• Data visibility issues: Many marketers report passing the Feedback-ID header correctly but still seeing no data in Google Postmaster Tools, despite considerable email volume.
• Volume dependency: There is a strong belief that the absence of FBL data is due to not meeting the unspecified volume thresholds for individual identifiers or complaint rates.
• List washing concerns: Marketers acknowledge Google's strict stance against using FBL data for list washing, which affects the type of data shared. Learn more about the benefits and downsides of Google's FBL.
• Identifier structure: The use of composite identifiers (e.g., Campaign-identifier:Account-identifier:DKIM-Domain:ESP-Name) is a common approach, but questions arise if individual segments also need high complaint volumes.

Key considerations

• Distinguish from other FBLs: Marketers must recognize that Google FBL operates differently from traditional FBLs (like Validity's or Yahoo's), which provide per-MTA abuse reports to suppress individual recipients.
• Intermittent data: Even with high volume, Google FBL data can be rare or intermittent, only appearing when Google deems it actionable for the sender. This can explain why the graph might be flat or zero.
• Focus on campaign/client analysis: Leverage the FBL data to identify problematic campaigns or sending clients, allowing for strategic adjustments and customer management. As Medium points out, this aligns with navigating new email sender requirements.
• Header complexity: Consider if one of the Feedback-ID parameters can be a base64 encoded text for internal email address identification, though Google's policy on individual identification might limit its utility.

Key opinions

• Anti-listwashing policy: Google explicitly states that its FBL data is not for suppressing individual recipients, but rather for senders to police their customer base and ensure they send wanted mail. This differs significantly from other FBL implementations. Read more on how FBLs function for Google and Oath.
• Volume and distinct users: FBL reports are generated only when a given identifier is present in a sufficient volume of mails and distinct user spam reports, making it unsuitable for low-volume senders or overly granular identifiers.
• Strategic identifiers: Identifiers should be used to track customer segments, campaigns, or traffic types, not individual recipients, to align with Google's privacy and policy goals.
• Data intermittency: Google FBL data can be rare and intermittent, appearing only when Google's algorithms detect a significant, actionable spam complaint trend for a specific identifier. This is elaborated on by the Certified Senders Alliance regarding sender-receiver communication using enhanced feedback loops.

Key considerations

• Reputation dependence: A sender's overall reputation directly impacts whether Google shares FBL data; a poor reputation may mean no data, regardless of volume.
• Aggregate complaints over time: Even if a campaign generates complaints, they must occur rapidly enough to aggregate into a reportable volume within a short period (typically a day or two), otherwise, no FBL data will be provided.
• Identifier breadth: Utilize both broader and narrower categories for identifiers to ensure at least some level of aggregation meets the necessary volume thresholds.
• Postmaster tools utilization: Rely on the Postmaster Tools' overall spam complaint percentage for a general overview, but understand that this does not guarantee FBL identifier data. For a deeper understanding of complaint management, see how abuse reports and FBLs work.

Key findings

• Purpose of FBL: Google's FBL helps high-volume senders, typically ESPs, to identify abusive campaigns or clients by providing aggregate spam complaint data. It is not intended for individual list cleaning.
• Feedback-ID header: Senders must include a Feedback-ID header in their emails containing unique, URL-safe identifiers that allow Google to associate spam complaints with specific sending entities. As Google Postmaster Tools documentation states, this is how the Feedback Loop operates.
• Data generation conditions: FBL reports are only generated for a given day's traffic if a specific identifier is present in a certain volume of mails AND distinct user spam reports, ensuring meaningful data is shared.
• Identifiers for segmentation: Google encourages using identifiers to represent different customer segments or campaign categories, allowing senders to gain insight into which parts of their sending program are causing complaints.

Key considerations

• Minimum data thresholds: It is critical to understand that data will not appear for identifiers that do not meet Google's internal (and unspecified) volume and complaint thresholds. This can lead to seemingly missing data. For more on how data is presented, see the Ultimate Guide to Google Postmaster Tools V2.
• Privacy focus: The FBL's design prioritizes user privacy, which means senders will never receive email addresses or personal identifying information from complaint reports.
• Data granularity vs. visibility: While more granular identifiers (e.g., per recipient) might seem desirable, they are unlikely to generate data due to the volume requirement. Broader identifiers are more likely to provide actionable insights. This also impacts the Google Postmaster Tools V2 Spam Rate Dashboard.
• No direct suppression: The FBL is not a mechanism for direct recipient suppression, unlike some other FBLs. Senders are expected to proactively manage their lists and prevent sending unwanted mail.