What are the challenges of A/B testing Gmail spam rates using Feedback Loop data?

Key findings

• Inconsistent data: Even control groups can show statistically significant differences in spam rates, which indicates issues with the data's reliability for precise A/B testing.
• FBL purpose: Google's FBL is primarily designed for Email Service Providers (ESPs) to detect abuse across their services, rather than for granular segmentation by individual senders to optimize specific campaigns. For a deeper dive into how it works, see our guide on how Gmail's Feedback Loop works.
• Data sparsity: It can be exceedingly rare to see meaningful data populate in Google Postmaster Tools (GPT) based on the Feedback-ID header, making it challenging to gather enough information for reliable testing.
• Threshold limitations: Gmail's FBL only provides data when a certain minimum volume of complaints is reached for a specific Feedback-ID, which can obscure insights for campaigns with lower complaint rates.

Key considerations

• Sampling accuracy: Achieving truly random samples for A/B testing is difficult, and any flaws in the group assignment algorithm can lead to skewed results.
• Statistical methodology: The choice of statistical method for determining significance may not always be appropriate for the nuances of FBL data, potentially leading to misinterpretations.
• Data consistency over time: FBL data tends to be more consistent and useful for campaigns that are sent consistently over time, rather than for one-off tests.
• Google Postmaster Tools scope: The data in GPT's FBL identifier spam rates specifically applies to @gmail.com consumer addresses, not Google Workspace domains, limiting its overall scope. For more information, read our article about the scope of Google Postmaster Tools Feedback Loop data. Mailchimp also offers insights into the importance of tracking spam complaint rates, highlighting the need for careful A/B testing, as discussed in their guide on spam complaint rate.

Key opinions

• Limited utility: Many marketers struggle to get useful feedback from Gmail's Feedback-ID header, finding it rare for data to populate consistently in Google Postmaster Tools.
• Segmentation challenges: There is a common sentiment that Gmail makes it incredibly difficult to pinpoint spam issues based on fine-grained segmentation, making it hard to identify problematic audience subsets.
• Perceived intent: Some marketers suspect that Google intentionally limits granular FBL data to prevent senders from gaming the system and sending emails to less engaged segments without exceeding complaint thresholds.
• Consistency matters: Feedback Loop data is generally more reliable and consistent over time for regular campaigns, rather than for one-off A/B tests.

Key considerations

• Group assignment: Even minor fluctuations in key metrics for control groups can indicate issues with the randomization process, which is critical for valid A/B testing.
• Statistical validation: Relying solely on basic A/B test calculators may not capture the complexities of email deliverability data, requiring a more nuanced statistical approach.
• Alternative metrics: Marketers may need to look beyond FBL data and consider other metrics, like engagement and direct spam complaints, to get a clearer picture of deliverability issues. Learn more about monitoring complaint rates.
• Long-term monitoring: Continuous monitoring and consistent data collection are essential to detect meaningful trends in spam rates, especially when using FBLs. EngageBay emphasizes the importance of consistent monitoring for sender reputation in their guide on email feedback loops.

Key opinions

• Data accuracy: Some experts are not convinced that Google Postmaster Tools accurately reports spam rates, especially when control groups show unexpected differences, even if the group split is seemingly correct.
• Intended use: The primary intent of FBL is to help identify abuse on the sender's side, not to enable granular segmentation for individual receivers. Using it for fine-tuned segmentation might go against its original design principles.
• Consistency required: Consistent sending over time is necessary for FBL data to be reliable; one-off campaigns, even with large volumes, may not yield accurate data.
• Anti-abuse and strategy alignment: Identifying segments that generate more complaints can be a valid anti-abuse measure, whether for an ESP's customer or at a sub-customer level. It aligns with good marketing strategy by helping senders identify and remove problematic segments or reconfirm them.

Key considerations

• Data interpretation: Misinterpreting the FBL's intended use can lead to misguided A/B testing strategies. It is crucial to understand that FBL data offers a macro-level view of spam complaints.
• Avoiding manipulation: If senders could too easily pinpoint specific datasets that complain more, they might try to manipulate their sending patterns to stay just under complaint thresholds, which ISPs actively try to prevent.
• Data availability: Gmail's FBL data typically applies only to @gmail.com consumer addresses, not Google Workspace domains, which affects the comprehensiveness of insights for B2B senders. For more detailed information on Gmail FBL reports, refer to our article on how to implement Gmail's feedback loop ID.
• Balancing access and abuse: Google's approach to FBL data balances wide availability with preventing abuse. Overly precise use for segmentation could lead to further restrictions or removal of the tool.
• Domain reputation: Google's FBL is one of the signals that ISPs use to assess a sender's reputation, as highlighted by resources like Word to the Wise, which emphasizes the importance of managing engagement and sender reputation.

Key findings

• Primary objective: Gmail's Feedback Loop is designed to notify senders when messages in their campaigns are marked as spam by recipients, primarily for abuse detection. This is explicitly stated in Google's documentation. You can read more about it in our ultimate guide to Google Postmaster Tools V2.
• Target audience: The FBL is particularly useful for Email Service Providers (ESPs) to detect abuse of their services across multiple clients, rather than individual senders for granular segmentation.
• Data aggregation: Google Postmaster Tools dashboards provide aggregated data about outgoing email sent to personal Gmail accounts, including spam rate and reputation, which may not offer the granularity needed for specific A/B test segments.
• Thresholds for reporting: FBL data only appears when certain complaint thresholds are met for a given Feedback-ID, meaning data might not be available for all campaigns or segments, particularly those with low complaint volumes.

Key considerations

• Limited granularity: While FBL provides data on campaigns, it is not designed to offer hyper-segmentation insights for individual senders. This inherent limitation can impact the feasibility of precise A/B testing strategies for spam rates.
• Interpreting intended use: Senders should align their use of FBL data with its stated purpose of identifying abuse rather than attempting to game the system by trying to find thresholds for sending to problematic segments.
• Complementary tools: Senders should use FBL data in conjunction with other metrics and tools, such as direct spam complaint reports from their ESPs and DMARC aggregate reports, to gain a more comprehensive view of their deliverability performance. Learn more about why Gmail Postmaster Tools data might not be updating. AWS also provides information on understanding Google Postmaster Tools for Amazon SES senders, which can offer further context.