Why am I seeing a spike in hard bounces later in my email campaign sends?

Key findings

• Reporting lag: Initial low or zero bounce rates can be attributed to the time it takes for bounce responses to be processed and reported by your email service provider (ESP), typically a few minutes.
• Hard bounce codes: The prevalence of 550 SMTP codes indicates 'user unknown' or invalid email addresses, suggesting a list quality issue rather than direct content or IP reputation blocking at first glance.
• Send order: If your data is ordered by an internal ID (URN) and not by recency or engagement, the increased hard bounces later in the campaign are unexpected if the list is uniformly clean.
• Dynamic filtering: Receiving servers (ISPs) may exhibit dynamic filtering, where they initially accept mail but then begin to reject messages as a campaign progresses if they detect suspicious volume or patterns. This could mean they defer earlier mail and then begin rejecting.
• Incomplete data: Standard tracking exports from ESPs may not provide the granular detail needed, such as full rejection messages and peer IPs, to diagnose complex deliverability issues effectively.

Key considerations

• Deep dive into data: Beyond aggregate reports, investigate raw delivery logs and bounce data for detailed insights. Understanding the full SMTP bounce message is crucial.
• Verify send order: Confirm that your ESP is truly sending in the order you specify and not implicitly reordering or prioritizing contacts in a way that places less engaged or older addresses later in the queue. This is often the case with data that is not properly segmenting email lists.
• Analyze bounce timing: Distinguish between the email's sent time and the actual bounce response time. Bounces can take time to register, which might explain initial zeros, but not necessarily an escalating rate of permanent failures (hard bounces).
• List hygiene: A consistent spike in 550 errors across different domains later in a send suggests your list might contain a higher proportion of invalid or abandoned addresses in those later segments. Regularly cleaning your list and implementing double opt-in can mitigate this, especially when dealing with a high bounce rate from an old list.

Key opinions

• ESPs might reorder: Despite explicit statements from ESPs, some marketers believe that platforms might have internal mechanisms for ordering sends that could affect bounce rates later in a campaign, possibly based on perceived list quality.
• Timing matters: The time taken for bounces to register means initial low bounce rates are normal, but a consistent increase over time needs deeper investigation into why later-sent emails are experiencing more issues.
• Data view necessity: Relying solely on standard reports isn't enough; accessing and analyzing raw data views (e.g., in Salesforce Marketing Cloud) is crucial for accurate diagnosis.
• Impact of internal IDs: Ordering by internal IDs like URNs (Unique Reference Numbers) could unintentionally lead to grouping older or less active addresses towards the end of a send, even if not explicitly intended as a quality metric.

Key considerations

• Cross-reference send and bounce times: Ensure your analysis properly correlates the exact send time of each email with its corresponding bounce response time, rather than just using aggregated data.
• Detailed bounce reasons: Insist on getting full SMTP bounce rejection messages from your ESP. A generic 550 code isn't enough to understand the root cause of the spike in bounces later in the campaign. This is vital when trying to resolve hard bounces.
• Examine list health over time: If the URNs (or similar internal IDs) are assigned chronologically, newer, more engaged contacts might have lower URNs, while older, potentially less valid contacts have higher ones, leading to an increasing bounce rate as the campaign progresses through the list. This would explain why you might see increased soft bounces after a volume spike.
• Proactive list cleaning: To minimize hard bounces, consistently clean your email lists of invalid or inactive addresses before sending. A higher hard bounce rate is often a clear indicator that your email list is inactive, incomplete, or contains spam traps.

Key opinions

• Raw log analysis: Aggregate data is insufficient. Experts need to see underlying delivery logs, including send timestamps, response codes, full rejection messages, and sometimes even the peer IP (the IP address of the receiving mail server).
• Dynamic server behavior: Receiving spam filters may not immediately reject mail. They might deliver or defer early mails, then start rejecting as they detect a 'spike of traffic' that they dislike, even for 550 errors that typically indicate user unknowns.
• Importance of full messages: Without the complete rejection message, diagnosing a complex situation is very difficult, as a simple 550 code lacks specific context.
• List quality over time: Even with random internal ordering, there's a possibility that older or less engaged addresses (which are more prone to becoming invalid) might implicitly be grouped, affecting deliverability later in a send, which impacts your domain reputation.

Key considerations

• Access detailed ESP data: Utilize tools like Query Studio in Salesforce Marketing Cloud to extract granular data views (e.g., joining _Sent and _Bounce tables via _Job) to accurately correlate bounces with send times. Without this, your emails might fail.
• Investigate deferrals: Beyond rejections (5xx codes), also look at deferrals (4xx codes). A high rate of temporary failures could indicate a pattern that eventually leads to hard rejections later in the send.
• Analyze per-domain performance: Segment your bounce data by domain to identify if specific ISPs are more prone to these late-campaign hard bounce spikes, which could point to their specific filtering rules.
• Sender behavior review: Consider if the sending rate (e.g., 500 records/second) itself, while seemingly consistent, is perceived as a sudden surge by ISPs, particularly if the quality of the latter half of the list is implicitly lower.

Key findings

• Permanent failures: Hard bounces signify that an email could not be delivered for a permanent reason, such as the recipient's email address not existing or being disabled. These should be immediately removed from mailing lists.
• Sender reputation impact: A high hard bounce rate severely damages sender reputation, leading to more emails being sent to spam or blocked entirely. This is why high bounces are red flags.
• SMTP 550 codes: These are common indicators of a hard bounce, often meaning 'user unknown' or 'mailbox not found.' While specific wording varies by ISP, the core issue is usually a non-existent recipient.
• Data view access: ESPs typically provide 'data views' or 'tracking extracts' that allow users to pull raw, detailed information on sends and bounces, enabling more in-depth troubleshooting than standard reports.

Key considerations

• Leverage detailed data: Always access and analyze the most granular bounce data available from your ESP. This includes specific SMTP codes and the full rejection message to understand the precise reason for the bounce. Salesforce Marketing Cloud documentation offers guidance on using data views for bounce information.
• Implement list hygiene best practices: To prevent hard bounces, maintain a clean email list. This means regularly removing invalid addresses and potentially using a double opt-in process for new subscribers to ensure their validity and engagement.
• Understand ISP policies: Familiarize yourself with the postmaster pages and deliverability guidelines of major ISPs (like Gmail, Outlook, Yahoo). These often provide insights into how they handle high-volume sends and filtering. Implementing proper email authentication (SPF, DKIM, DMARC) also contributes significantly.
• Monitor blocklists: While 550 errors are typically not directly blocklist-related, consistent sending to invalid addresses can lead to your IPs or domain being placed on email blocklists (or blacklists). Regularly check if your sending infrastructure is listed on any major blocklists.