How Halon uses machine learning to simplify one of email’s biggest challenges

Every deliverability expert knows the chaos of bounce management: vague error messages, endless regex rules, and the constant fear of damaging your IP reputation.

At our latest CSA Live Webinar, Carlos Pereira and Steve Tuck from Halon shared how machine learning can finally bring clarity to this long-standing challenge. Their session, “From Chaos to Clarity: Optimizing Deliverability through Intelligent Bounce Classification,” showed how senders can move from manual, error-prone rule sets to a more adaptive, data-driven approach that boosts deliverability and saves time.

As host, the CSA continues to foster quality and trust in commercial emailing. Acting as a neutral interface between mailbox providers and senders, our goal is to promote best practices and technological progress across the ecosystem.

The Challenge: The Chaos of Bounce Management

Carlos and Steve began by highlighting a familiar pain point: bounce management is one of the biggest obstacles to smooth deliverability, especially during IP warm-up.

Traditional systems depend on manual work and static regular expressions (regex) to interpret bounce messages. But regex-based classification often causes more confusion than clarity.

Why Regex Falls Short

For over two decades, regex rules have been the industry’s default method for classifying bounce messages — yet they come with serious drawbacks:

• High complexity and error risk: Regex sets are difficult to write and easy to break. A single change can misclassify thousands of messages.
• Maintenance overload: Adjusting these rules over time creates technical debt, leading to unpredictable side effects.
• Inconsistent results: Even professional regex systems often disagree when classifying the same bounce data.
• Vague signals: Mailbox provider responses can be ambiguous, leaving senders unsure whether to slow a campaign, pause a domain, or reduce IP volume.
• Useless “Other” category: Many bounces fall into this bucket, leaving deliverability teams without clear next steps.

The result? Teams stuck in reactive troubleshooting instead of proactive deliverability management.

The Solution: Intelligent Bounce Classification

To replace this manual chaos with clarity, Halon developed the Bounce Next Action Classifier (BNAC) — a machine learning model that interprets bounce messages in context and recommends the right next action.

How It Works

1. Holistic understanding:
   Instead of relying on keywords like “blocked,” the BNAC analyzes the entire message to identify the true cause of the failure — even when it’s not explicitly stated.
2. Action-based categories:
   Classifications are tied to specific actions, eliminating vague labels like “Other.” Each output clearly indicates what to do next.
3. Learning, not guessing:
   Like DNA sequencing, the model recognizes patterns and relationships, allowing it to correctly classify bounce types it has never seen before — without manual rule updates.
4. Speed and efficiency:
   The BNAC processes thousands of bounce messages per second and runs efficiently on standard CPUs, requiring no GPUs or large infrastructure.

Training with Privacy in Mind

The model was trained on more than 9,000 anonymized bounce messages. To preserve privacy, all identifying details — such as email addresses, domains, and IPs — are replaced with placeholders.

Customers can optionally share anonymized data to help retrain the model regularly. This continuous learning allows the system to adapt to evolving mailbox provider behaviors over time.

The Impact: Smarter Deliverability and IP Warm-Up

Accurate bounce classification is not just a technical improvement — it’s essential to sender reputation. When bounce signals are misunderstood or ignored, reputation problems can escalate quickly.

By using ML-driven insights, senders can:

• Optimize warm-up schedules: Adjust sending rates per mailbox provider, domain, and IP to match individual reputation growth patterns.
• Automate repetitive work: Machines handle gradual send-rate increases (for example, over 30 days) while deliverability teams focus on strategy.
• React proactively: Real-time classification enables faster, more precise responses to delivery issues before they affect sender reputation.
• Gain full visibility: Clear reporting reduces the need for night-time dashboard checks and reactive triage.

In short, intelligent automation replaces guesswork with guidance.

Moving from Reactive to Proactive

Carlos and Steve summed up this transformation with three guiding principles:

1. Be proactive, not reactive: Automation prevents issues before they escalate.
2. Automate repetitive tasks: Let machines handle the “boring bits.”
3. Gain true visibility: Maintain oversight without constant manual monitoring.

Together, these principles lead to more reliable deliverability, stronger sender reputation, and less operational stress for teams.

Conclusion

As Carlos and Steve demonstrated, the future of deliverability is data-driven. By moving from regex lists to intelligent bounce classification, the industry can finally move beyond reactive firefighting and toward predictive, automated deliverability management.

Watch the full recording and hear the full discussion.