Why Emails Go to Spam

Key Causes of Spam Filtering

Mailbox providers evaluate several signals before determining if an email is spam:

1. Sender reputation
2. Engagement metrics (opens, clicks, deletions)
3. List quality and hygiene
4. Content quality (spammy language, excessive links)
5. Sending patterns and volume
6. Domain and IP history
7. Presence of traps or high-risk addresses

List-Related Issues

Your list is the first line of defense—or risk:

• High-risk addresses: spam traps, bots, disposable emails
• Decayed or exhausted emails: inactive or unengaged recipients
• Purchased or scraped lists: low-quality sources that reduce trust
• Duplicate or malformed entries: trigger bounce signals and reduce reputation

Poor list hygiene is the fastest way to trigger spam filtering.

Content & Sending Behavior Issues

Even high-quality addresses can filter emails if:

• Subject lines are misleading or contain spammy words
• Content has excessive images, links, or code errors
• Sending frequency is irregular or sudden
• Campaigns generate complaints, unsubscribes, or deletes without opening

Consistent, relevant content is key to engagement and inbox placement.

Domain & IP Reputation Factors

Spam filtering also considers technical and historical signals:

• Sending from a new or dormant IP without warm-up
• Poor DKIM, SPF, or DMARC configuration
• Previously blacklisted domains or IPs
• Mixed sending behavior from shared IP addresses

Maintaining domain and IP integrity is crucial for consistent inbox placement.

Spam Traps and High-Risk Emails

Spam traps are addresses created to catch negligent senders.

Sending to traps immediately harms reputation and can permanently reduce inbox placement.

High-risk emails—decayed, exhausted, or bot-generated—also signal low engagement and increase spam filtering probability.

Behavioral Signals from Recipients

Mailbox providers track recipient actions:

• Not opening emails
• Deleting without reading
• Moving emails to spam
• Reporting complaints

Repeated negative engagement signals lower sender reputation and increase spam placement risk.

Predictive Hygiene and Spam Prevention

Impressionwise uses predictive hygiene to prevent spam hits before they occur:

• Detects high-risk addresses, traps, and exhausted contacts
• Scores email risk based on behavior, source, and engagement patterns
• Suppresses or segments risky addresses proactively
• Monitors warm-up schedules, source integrity, and sender reputation

Predictive hygiene ensures campaigns reach real, engaged recipients, minimizing spam risk.

Best Practices to Avoid Spam

✔ DO:

• Maintain clean, verified, and engaged lists
• Monitor engagement trends and suppress inactive emails
• Avoid purchased or scraped lists
• Follow consistent sending schedules
• Use predictive hygiene and risk scoring
• Implement proper authentication (SPF, DKIM, DMARC)
• Test campaigns for spam triggers

✘ DON’T:

• Send blindly to all addresses
• Ignore decayed or high-risk emails
• Use misleading subject lines or spammy content
• Neglect IP/domain warm-up and reputation
• Rely solely on verification without predictive analysis

Final Recommendations

Emails go to spam because mailbox providers detect risk signals—behavioral, content, technical, and list-based.

Preventing spam placement requires:

• predictive hygiene
• high-quality, engaged lists
• domain/IP reputation management
• intelligent segmentation and suppression

Impressionwise identifies and mitigates hidden risks before they hit your inbox, ensuring campaigns reach the intended audience, protect reputation, and drive engagement.