AI-Powered Spam Score Checker

Predict your email's spam probability using machine learning. Get instant AI-powered analysis with provider-specific predictions for Gmail, Outlook, Yahoo, and Apple Mail.

AI-Powered Spam Score Analyzer

Get instant spam probability predictions using machine learning

What is ML-Powered Spam Scoring?

Overview

Our AI-powered spam score checker uses machine learning to predict the likelihood of your email landing in spam folders. Unlike traditional rule-based checkers, our ML model analyzes 28+ features including authentication, content quality, sender reputation, and provider-specific signals to give you accurate, actionable predictions.

How It Works

Our system extracts features from your email including subject line characteristics, content structure, authentication status, and historical sender data. These features are analyzed by our prediction model which has been trained on real-world deliverability data to provide spam probability scores with confidence intervals.

Key Features Analyzed

  • Authentication: SPF, DKIM, and DMARC alignment status
  • Subject Line: Urgency words, capitalization ratio, emoji count
  • Content Quality: HTML/text ratio, link count, image count
  • Compliance: Unsubscribe links, physical address, List-Unsubscribe header
  • Headers: Message-ID, Date, Return-Path alignment
  • Reputation: Historical bounce rate, spam rate, domain age

Provider-Specific Predictions

Different email providers weight deliverability factors differently. Our tool provides tailored predictions for:

  • Gmail: Heavily weights authentication and engagement metrics. Predicts inbox, promotions tab, or spam placement.
  • Outlook: Strict on content quality and link ratios. Distinguishes between junk and spam folders.
  • Yahoo: Requires strong authentication (DMARC) and penalizes poor sender reputation.
  • Apple Mail: Focuses on privacy and user experience signals.

Understanding Risk Levels

Low Risk (0-25)

Excellent deliverability expected. Email has strong authentication, compliant structure, and no spam triggers detected.

Medium Risk (25-50)

Good deliverability with some improvement opportunities. Minor issues detected that could affect inbox placement.

High Risk (50-75)

Deliverability concerns present. Multiple issues detected that significantly increase spam probability. Immediate action recommended.

Critical Risk (75-100)

Very likely to be flagged as spam. Critical authentication or compliance issues detected. Do not send until issues are resolved.

Common Spam Triggers Detected

  • Missing or misaligned SPF/DKIM/DMARC authentication
  • Urgency words in subject line (ACT NOW, LIMITED TIME, etc.)
  • Excessive capitalization or special characters
  • Too many emojis in subject line (3+)
  • High link-to-text ratio
  • Missing unsubscribe mechanism
  • No physical address in footer
  • Poor sender reputation (high bounce/spam rates)

Best Practices for Low Spam Scores

  • Implement full authentication: Set up SPF, DKIM, and DMARC with proper alignment
  • Avoid spam triggers: Use natural language without urgency or clickbait phrases
  • Maintain compliance: Include unsubscribe links and physical address
  • Balance content: Keep HTML/text ratio reasonable and limit links
  • Build reputation: Maintain low bounce rates and good engagement
  • Test before sending: Always check spam score before major campaigns
  • Monitor regularly: Track deliverability metrics and adjust based on feedback

Improving Your Score

Our AI analyzer provides specific, actionable recommendations based on the issues detected in your email. Each recommendation targets a particular weakness and explains how to fix it. Start with authentication issues first as they have the highest impact on deliverability, then address compliance, and finally optimize content.

Accuracy & Confidence

Our ML model provides a confidence score with each prediction indicating how reliable the assessment is. Higher confidence (80%+) means we have sufficient data (authentication status, historical metrics) to make an accurate prediction. Lower confidence suggests limited data availability, but predictions are still valuable for identifying potential issues.

Limitations

While our ML model is highly accurate (94% based on testing), spam filtering is complex and providers frequently update their algorithms. Actual inbox placement depends on many factors including:

  • Individual recipient engagement history
  • Provider-specific blacklist status
  • Real-time reputation signals
  • Time-of-day and sending volume patterns
  • Recipient mailbox rules and filters

For the most accurate results, combine this tool with seed list testing to verify actual inbox placement.

Who Should Use This Tool?

  • Email Marketers: Test campaigns before sending to ensure deliverability
  • Developers: Validate transactional emails from applications
  • Agencies: QA client email campaigns for deliverability issues
  • Compliance Teams: Verify emails meet authentication and legal requirements
  • Small Businesses: Improve newsletter and promotional email performance