Correlation vs Causation in Email Deliverability: Why Your Assumptions Are Wrong

One of the most dangerous mistakes in email deliverability troubleshooting is confusing correlation with causation. You see two things happening together and assume one caused the other—but that's often not the case. This misunderstanding leads to wasted time, incorrect fixes, and continued deliverability problems.

Understanding the difference between correlation and causation is crucial for diagnosing email delivery issues correctly and implementing effective solutions.

Understanding Correlation vs Causation

What's the Difference?

Correlation means two things happen at the same time or in sequence, but one doesn't necessarily cause the other.

Causation means one thing directly causes another thing to happen.

The Classic Example:

• Correlation: Ice cream sales increase when drowning deaths increase
• Causation: Hot weather causes both ice cream sales and swimming (which leads to more drowning)

In email deliverability, you might see:

• Correlation: Low open rates coincide with high spam folder placement
• Causation: High spam folder placement causes low open rates (because emails never reach the inbox)

Why This Matters for Email Senders

Email deliverability is complex, with dozens of factors influencing inbox placement. When something goes wrong, it's tempting to blame the most obvious or recent change—but that's often not the actual cause.

Common Mistakes:

1. Assuming the last change you made caused the problem
2. Blaming visible symptoms instead of root causes
3. Fixing correlations instead of actual issues
4. Overlooking hidden factors that drive both outcomes

Common Correlation vs Causation Mistakes in Email Delivery

Mistake #1: "Adding DMARC Caused My Deliverability to Drop"

The Correlation:

• You implemented DMARC
• Shortly after, spam folder placement increased
• You conclude: "DMARC broke my email delivery"

The Reality: DMARC doesn't cause deliverability issues—it reveals existing problems. Here's what's actually happening:

The Causation Chain:

1. Your emails were already failing SPF/DKIM authentication
2. Before DMARC, providers didn't enforce strict policies
3. DMARC policy enforcement exposed the authentication failures
4. Providers now reject/quarantine emails that fail authentication

The Real Cause: Pre-existing SPF or DKIM misconfigurations, not DMARC itself.

How to Identify:

• Check DMARC reports for authentication failures
• Review SPF and DKIM records before DMARC implementation
• Look at historical authentication rates

The Fix: Correct SPF/DKIM setup, then gradually implement DMARC policies.

Mistake #2: "My IP Got Blacklisted, So Deliverability Dropped"

The Correlation:

• Your IP appears on a blacklist
• Deliverability decreases
• You conclude: "The blacklist caused my deliverability problem"

The Reality: Blacklist listings are usually symptoms, not causes. They indicate underlying reputation issues.

The Causation Chain:

1. Poor sending practices (high spam complaints, low engagement)
2. Recipients mark emails as spam
3. Blacklist operators detect spam signals
4. Blacklist listing occurs
5. Additional providers start blocking based on blacklist

The Real Cause: Sending practices that generate spam complaints, not the blacklist itself.

How to Identify:

• Check complaint rates before blacklist appearance
• Review engagement metrics (opens, clicks)
• Analyze sending patterns and list quality
• Look at bounce rates and invalid addresses

The Fix: Improve sending practices, clean your list, reduce complaints—then request delisting.

Mistake #3: "Switching Email Providers Fixed My Deliverability"

The Correlation:

• You switched from Provider A to Provider B
• Deliverability improved
• You conclude: "Provider A was the problem"

The Reality: The provider switch often coincides with other changes that actually fixed the issue.

The Causation Chain:

1. You switch providers (visible change)
2. You also clean your email list (hidden change)
3. You update SPF/DKIM records (hidden change)
4. You reduce sending volume temporarily (hidden change)
5. Deliverability improves

The Real Cause: List cleaning, authentication fixes, or volume reduction—not the provider switch.

How to Identify:

• Compare what changed beyond the provider switch
• Review list quality improvements
• Check authentication record updates
• Analyze sending volume changes

The Fix: Identify and maintain the actual improvements, regardless of provider.

Mistake #4: "High Bounce Rates Caused My Reputation to Drop"

The Correlation:

• Bounce rates increased
• Sender reputation decreased
• You conclude: "Bounces caused reputation damage"

The Reality: High bounce rates and reputation damage are both caused by the same underlying issue: poor list hygiene.

The Causation Chain:

1. You acquire low-quality email lists
2. Many addresses are invalid or abandoned
3. Bounce rates increase
4. Providers see high bounce rates as a spam signal
5. Reputation decreases
6. More emails bounce due to reputation issues

The Real Cause: Poor list acquisition practices, not bounces themselves.

How to Identify:

• Review list sources and acquisition methods
• Check bounce types (hard vs soft bounces)
• Analyze when bounces started vs when reputation dropped
• Look at list growth patterns

The Fix: Improve list acquisition, implement double opt-in, regularly clean lists.

Mistake #5: "Low Open Rates Mean My Emails Are Going to Spam"

The Correlation:

• Open rates decrease
• You assume emails are going to spam
• You conclude: "Spam filtering caused low opens"

The Reality: Low open rates can have many causes, and spam filtering is just one possibility.

The Causation Chain (Multiple Possibilities):

Scenario A:

1. Subject lines become less compelling
2. Open rates decrease
3. Providers interpret low engagement as spam signal
4. More emails go to spam

Scenario B:

1. Emails actually go to spam
2. Recipients don't see emails
3. Open rates decrease

Scenario C:

1. Sending frequency increases
2. Recipients become fatigued
3. Open rates decrease
4. Providers see low engagement
5. More emails go to spam

The Real Cause: Could be subject lines, spam filtering, sending frequency, or content quality—need investigation.

How to Identify:

• Check inbox placement rates directly (not just opens)
• Review subject line performance
• Analyze sending frequency changes
• Compare open rates across different segments
• Use seed lists to test inbox placement

The Fix: Identify the actual cause through testing and data analysis.

Mistake #6: "Changing My 'From' Address Fixed Deliverability"

The Correlation:

• You changed from noreply@company.com to hello@company.com
• Deliverability improved
• You conclude: "The 'From' address was the problem"

The Reality: The address change often coincides with other improvements or the new address starts with a clean reputation.

The Causation Chain:

1. Old address had poor sending history
2. You change to new address
3. New address has no reputation (starts neutral)
4. You also improve sending practices
5. Deliverability improves

The Real Cause: Fresh reputation start + improved practices, not the address itself.

How to Identify:

• Compare sending practices before/after change
• Review reputation history of old address
• Check if other changes occurred simultaneously
• Analyze long-term performance of new address

The Fix: Maintain good sending practices regardless of address.

Mistake #7: "Weekend Sending Causes Lower Deliverability"

The Correlation:

• You send emails on weekends
• Deliverability is lower
• You conclude: "Weekend sending causes deliverability issues"

The Reality: Weekend sending might correlate with lower deliverability, but the cause is usually different engagement patterns or provider filtering.

The Causation Chain:

1. Recipients check email less on weekends
2. Lower engagement (opens, clicks) on weekends
3. Providers see lower engagement as spam signal
4. More emails filtered to spam
5. Deliverability appears lower

The Real Cause: Lower engagement rates, not the day of sending.

How to Identify:

• Compare engagement rates by day of week
• Check if spam folder placement actually increases
• Analyze if content quality differs on weekends
• Review sending volume differences

The Fix: Improve weekend content/strategy or send on higher-engagement days.

How to Identify Real Causes vs Correlations

Step 1: Gather Comprehensive Data

Don't rely on single metrics. Collect multiple data points:

Essential Metrics:

• Inbox placement rates (not just opens)
• Authentication pass rates (SPF, DKIM, DMARC)
• Bounce rates (hard vs soft)
• Complaint rates
• Engagement rates (opens, clicks, replies)
• Sender reputation scores
• Blacklist status
• Sending volume and patterns

Timeline Analysis:

• When did the problem start?
• What changed before the problem?
• What changed after the problem?
• Are there seasonal patterns?

Step 2: Look for Multiple Indicators

Real causes usually show up in multiple metrics:

Example: List Quality Issues Show Up As:

• High bounce rates
• Low engagement rates
• High complaint rates
• Reputation degradation
• Blacklist listings

Example: Authentication Issues Show Up As:

• DMARC failures
• SPF/DKIM misalignment
• Increased spam folder placement
• Provider-specific rejections

Step 3: Test Hypotheses Systematically

Don't assume—test:

A/B Testing Approach:

1. Form a hypothesis about the cause
2. Make one change at a time
3. Measure results
4. Compare to control group
5. Verify the fix addresses the root cause

Example Test:

• Hypothesis: Subject lines cause low opens
• Test: Send same content with different subject lines
• Measure: Open rates, inbox placement
• Result: If opens improve but spam placement doesn't, subject lines weren't the cause

Step 4: Consider Hidden Factors

Look beyond the obvious:

Common Hidden Factors:

• List acquisition methods
• Email content quality
• Sending frequency changes
• Provider policy updates
• Seasonal patterns
• Recipient behavior changes
• Infrastructure changes

Questions to Ask:

• What else changed around the same time?
• Are there external factors (holidays, events)?
• Did provider policies change?
• Are recipients' behaviors different?

Step 5: Use Proper Tools

Don't guess—use data:

Essential Tools:

• DMARC Reports: Show authentication failures
• Seed Lists: Test actual inbox placement
• Reputation Monitoring: Track sender scores
• Blacklist Checkers: Monitor list status
• Analytics Platforms: Track engagement metrics
• Email Testing Tools: Verify authentication

What to Monitor:

• Real-time inbox placement
• Authentication pass rates
• Reputation scores across providers
• Complaint and bounce trends
• Engagement patterns

Real-World Example: The Case of the Mysterious Deliverability Drop

Let's walk through a real scenario:

The Situation

A B2B company noticed deliverability dropping over 3 months:

• Inbox placement: 85% → 60%
• Open rates: 25% → 12%
• Complaints: 0.1% → 0.5%

Initial Assumption (Correlation)

The team noticed they implemented DMARC around the same time and concluded: "DMARC caused our deliverability to drop."

Investigation (Finding Causation)

Timeline Analysis:

1. Month 1: DMARC implemented
2. Month 2: Deliverability starts dropping
3. Month 3: Significant drop continues

Data Review:

• DMARC reports showed 30% authentication failures
• SPF records were correct
• DKIM signatures were failing
• Complaint rates increased before DMARC
• List growth accelerated (new low-quality sources)

The Real Cause (Causation)

Root Cause: Poor list quality + DKIM failures

The Chain:

1. Marketing team started buying email lists (Month 0)
2. Low-quality addresses generated complaints (Month 1)
3. DKIM setup was incomplete (ongoing)
4. DMARC exposed the DKIM failures (Month 1)
5. Providers enforced DMARC policies (Month 2-3)
6. Deliverability dropped due to authentication failures + complaints

The Fix:

1. Fixed DKIM configuration
2. Stopped buying email lists
3. Cleaned existing lists
4. Gradually increased DMARC policy (none → quarantine → reject)
5. Deliverability recovered over 2 months

Lessons Learned

• DMARC didn't cause the problem—it revealed it
• Multiple factors contributed (list quality + authentication)
• The visible change (DMARC) wasn't the cause
• Investigation revealed the real issues

Best Practices for Avoiding Correlation Mistakes

1. Document All Changes

Keep a change log:

• What changed
• When it changed
• Why it changed
• Expected impact

Example Log Entry:

Date: 2024-12-01
Change: Updated SPF record to include new ESP
Reason: Added new email service provider
Expected: No impact (just adding authorized sender)
Actual: Need to monitor

2. Monitor Multiple Metrics

Don't rely on single indicators:

Create a Dashboard:

• Inbox placement rate
• Authentication pass rates
• Engagement metrics
• Complaint and bounce rates
• Reputation scores
• Blacklist status

3. Establish Baselines

Know your normal performance:

• What are typical open rates?
• What's normal bounce rate?
• What's expected complaint rate?
• What's baseline inbox placement?

Compare Against Baseline:

• Is this change significant?
• Is it within normal variation?
• Does it correlate with other changes?

4. Test One Change at a Time

Avoid multiple simultaneous changes:

Problem:

• Change subject lines
• Update SPF records
• Increase sending volume
• Switch email templates
• Result: Can't tell what caused what

Solution:

• Make one change
• Measure results
• Wait for stabilization
• Make next change

5. Use Control Groups

Compare against baseline:

Example:

• Send to 50% of list with new approach
• Send to 50% with old approach
• Compare results
• Identify what actually changed

6. Question Your Assumptions

Challenge your conclusions:

Questions to Ask:

• What else could cause this?
• What data contradicts my assumption?
• What would I need to see to prove causation?
• Are there alternative explanations?

7. Seek External Validation

Get outside perspective:

• Consult deliverability experts
• Use professional tools
• Get second opinions
• Review with team

Common Patterns: Correlation vs Causation

Pattern 1: The "Last Change" Fallacy

Mistake: Assuming the last change caused the problem

Reality: Problems often have delayed effects or multiple causes

Example:

• Change SPF on Monday
• Deliverability drops on Friday
• Assume SPF caused it
• Reality: List quality issue from previous week finally caught up

Pattern 2: The "Visible Symptom" Trap

Mistake: Treating visible symptoms as causes

Reality: Symptoms point to causes but aren't causes themselves

Example:

• See blacklist listing
• Focus on delisting
• Reality: Should focus on why you got listed (complaints, poor practices)

Pattern 3: The "Coincidence" Error

Mistake: Assuming simultaneous events are related

Reality: Many things happen at once without causal relationship

Example:

• Implement DMARC
• Provider updates filtering
• Deliverability changes
• Assume DMARC caused it
• Reality: Provider update was the actual cause

Pattern 4: The "Single Metric" Mistake

Mistake: Relying on one metric to diagnose issues

Reality: Multiple metrics needed to understand causation

Example:

• See low open rates
• Assume spam filtering
• Reality: Could be subject lines, content, timing, or spam filtering

Tools and Techniques for Proper Diagnosis

1. DMARC Reports

What They Show:

• Authentication failures
• Which providers are rejecting
• Failure reasons
• Volume of failures

How to Use:

• Identify authentication issues
• See provider-specific problems
• Track failure trends
• Verify fixes

2. Seed Lists

What They Show:

• Actual inbox placement
• Provider-specific placement
• Spam folder placement
• Inbox placement trends

How to Use:

• Test real deliverability
• Compare across providers
• Verify improvements
• Identify provider-specific issues

3. Reputation Monitoring

What They Show:

• Sender reputation scores
• Reputation trends
• Provider-specific reputation
• Reputation factors

How to Use:

• Track reputation changes
• Identify reputation issues
• Monitor recovery
• Compare across providers

4. Engagement Analytics

What They Show:

• Open rates
• Click rates
• Reply rates
• Unsubscribe rates

How to Use:

• Identify engagement issues
• Compare segments
• Track trends
• Correlate with deliverability

5. Complaint and Bounce Analysis

What They Show:

• Complaint rates
• Bounce rates
• Bounce types
• Complaint trends

How to Use:

• Identify list quality issues
• Find sending practice problems
• Track improvements
• Correlate with reputation

Conclusion: Think Like a Scientist

Email deliverability troubleshooting requires scientific thinking:

1. Observe: Gather comprehensive data
2. Hypothesize: Form testable theories
3. Test: Make controlled changes
4. Analyze: Compare results to hypotheses
5. Conclude: Identify actual causes
6. Verify: Confirm fixes address root causes

Key Takeaways

• Correlation ≠ Causation: Just because two things happen together doesn't mean one caused the other
• Look Deeper: Real causes are often hidden or delayed
• Test Systematically: Don't assume—verify with data
• Monitor Multiple Metrics: Single metrics can mislead
• Document Changes: Track what changed and when
• Question Assumptions: Challenge your conclusions

Next Steps

1. Audit Your Current Approach: Are you confusing correlation with causation?
2. Improve Your Monitoring: Set up comprehensive metrics tracking
3. Document Changes: Create a change log
4. Test Systematically: Make one change at a time
5. Seek Help: Consult experts when needed

Remember: The goal isn't to find quick fixes—it's to identify and address root causes. By understanding correlation vs causation, you'll make better decisions, implement more effective fixes, and achieve sustainable deliverability improvements.

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Want to improve your email deliverability? Check your domain's email authentication setup or send a test email to verify your SPF, DKIM, and DMARC configuration.