Why is Gmail Postmaster Tools FBL data inconsistent, and what factors determine its availability?

Email marketer from Email Marketing Forum highlights the importance of properly formatted Feedback-ID headers. They mention that incorrect or missing Feedback-ID headers can prevent FBL reporting. The structure of the header should be unique for each mailstream, and improper implementation can lead to suppressed data.

Email marketer from Neil Patel's Blog shares that inconsistent data in Postmaster Tools can stem from fluctuating sender reputation. Sudden spikes in negative engagement (spam complaints) or changes in sending patterns can impact the visibility of FBL data. Maintaining consistent sending volumes and positive engagement metrics are crucial for reliable reporting.

Email marketer from Litmus suggests that low engagement levels (opens, clicks) can indirectly affect the availability of FBL data. If Google perceives low engagement, they might deprioritize the collection or reporting of FBL data for those mailstreams, focusing instead on traffic with higher user interaction.

Email marketer from Email Geeks points out that even with the feedback-id header, FBL reports are not guaranteed in Gmail Postmaster Tools, as Google chooses whether to display them.

Email marketer from Email Geeks shares observations about Gmail Postmaster Tools (GPT) Feedback Loop (FBL) data, noting inconsistencies across domains with and without feedback-id signing. Some domains signing feedback-id don't display FBL data, while one unsigned domain does. Dmytro expresses paranoia about potentially missing something and questions why an unsigned domain receives FBL data despite Gmail guidelines.

Email marketer from emailgeek.com suggests that FBL data inconsistency can be influenced by both IP and domain reputation. Even if your domain is properly authenticated, a poor IP reputation (due to shared IP space or prior abuse) might hinder the reporting of FBL data. This can happen if Gmail suspects the overall traffic from that IP of being spammy.

Email marketer from Reddit suggests that when using a shared IP, FBL data might be skewed or suppressed due to the actions of other senders on the same IP. Google might prioritize the overall IP reputation rather than individual domain performance, leading to inconsistent FBL reporting. They recommend switching to a dedicated IP to gain more control over reputation.

Email marketer from Stack Overflow speculates that Google might apply anonymity thresholds to FBL data. If the number of spam complaints for a specific Feedback-ID is too low, the data might not be displayed to protect user privacy. This can lead to seemingly inconsistent FBL reporting, especially for smaller or segmented mailstreams.

Email marketer from Gmass shares to first ensure that your domain is authenticated using SPF, DKIM, and DMARC. Authentication proves that you are who you say you are, and it also shows Gmail that you're a legitimate sender and should be trusted.

Expert from Email Geeks shares confusion about feedback-id, recounting an experience where a client's feedback-id value was simply "L", corresponding to an internal list designator. Laura also points out that the FBL identifier is intended to distinguish mail sent with the same d= (domain), primarily for ESP usage, and that FBL identifiers would only be expected if it's the same d= but different mailstreams. Laura states that she's never understood why the FBL is so different than the spam rate.

Expert from Word to the Wise emphasizes the multifaceted nature of deliverability signals, where FBL is just one component. Inconsistencies could stem from how various signals (spam complaints, engagement, authentication) interact and weigh against each other. Also mentioned is the user-specific customisation of deliverability that Google could be using. Understanding the relationship betweeen these signals will paint a much clearer picture.

Expert from Spam Resource explains that Google uses volume thresholds for reporting data in Postmaster Tools, including FBL data. If your email volume is below a certain threshold, Google may not provide FBL data to protect user privacy and prevent statistically insignificant data from being misinterpreted. This is due to the fact the spam rate may be skewed.

Documentation from SparkPost clarifies that accurate FBL data hinges on correct setup of authentication, particularly DKIM, and proper formatting of the Feedback-ID header. If authentication isn't correctly configured or the Feedback-ID is missing/incorrect, the FBL reporting will likely be unreliable or absent. They emphasize that all domains using the same DKIM need unique identifiers or FBL reporting can be suppressed to prevent misuse.

Documentation from Microsoft specifies that SPF record configuration issues, like syntax errors, missing includes, or exceeding lookup limits, can lead to authentication failures. These failures not only impact deliverability but also affect the availability of FBL data, as Google relies on authenticated traffic for accurate reporting.

Documentation from Google Support explains that Feedback Loop (FBL) data is only reported when a significant volume of email is sent from a domain and a notable number of users mark those messages as spam. Insufficient volume or low spam complaints may result in no data being displayed. Also, FBL data relies on the proper setup and alignment of authentication methods like SPF, DKIM, and DMARC.

Documentation from RFC describes that DKIM implementation errors can cause your legitimate emails to fail authentication checks, potentially impacting your sender reputation and leading to inconsistent FBL data, or no data at all. Proper DKIM key management and syntax are crucial for data accuracy.

Documentation from Mailjet explains the criteria for FBL data availability. According to Mailjet, Gmail only provides FBL data for senders who consistently send a significant volume of emails. It also states the data will only be available after it identifies a consistent pattern of spam complaints exceeding a certain threshold to ensure the data is statistically relevant.