Definition
Inbox monitoring is the practice of continuously testing where your emails are delivered by sending messages to controlled seed addresses across different mailbox providers and then checking their placement. This gives senders visibility into whether campaigns reach the primary inbox, land in spam or appear in a promotional or update tab.
Most inbox monitoring services maintain seed lists at Gmail, Yahoo, Outlook, Apple Mail and regional providers. They report placement rates and often alert you to sudden drops that indicate a deliverability incident.
What Inbox Monitoring Measures
- Inbox placement rate by mailbox provider
- Spam folder placement rate
- Promotions or update tab placement (Gmail)
- Missing messages indicating delivery failures
- Authentication pass or fail status per seed
Best Practices
- Monitor inbox placement continuously, not just during campaign launches
- Investigate any provider where placement drops below 90%
- Use monitoring data alongside engagement metrics — high engagement and poor placement together suggest an inbox provider filtering issue
- Compare placement rates between authenticated and unauthenticated sends to isolate DNS configuration issues
Related Glossary Terms
Bounce Classification
Bounce classification uses SMTP codes (550, 551, 552, 553, 554, 450, 451, 452) and enhanced status codes to categorise permanent and transient delivery failures.
DMARC Alignment
DMARC identifier alignment determines whether the domain in the From header matches the domains used in SPF and DKIM authentication. Strict or relaxed.
Email A/B Test Confidence Level
The confidence level in an email A/B test indicates the probability that the observed result is genuine and not due to random chance, with 95% being the standard threshold.
Email A/B Test Examples
Email A/B test examples show practical testing scenarios that help marketers improve open rates, click-through rates, and conversions through data-driven experimentation.
Email A/B Test Minimum Detectable Effect
The minimum detectable effect (MDE) is the smallest improvement an A/B test can reliably detect given the available sample size, directly affecting how long a test must run.
Email A/B Test Sample Size
A/B test sample size is the minimum number of recipients needed per variant to achieve statistically significant results, determined by baseline conversion rate, minimum detectable effect, and confidence level.