January 20266 min read

Email Analytics

Opens, clicks, conversions, and A/B testing for email success.

Key Metrics

Delivery Metrics

code

Sent: Total emails sent
Delivered: Emails that reached servers
Delivery Rate = Delivered / Sent

Bounces:
├── Hard Bounce: Invalid address (remove immediately)
├── Soft Bounce: Temporary issue (retry)
├── Bounce Rate = Bounces / Sent

Engagement Metrics

| Metric | Formula | What It Tells You | |--------|---------|-------------------| | Open Rate | Opens / Delivered | Subject line effectiveness | | Click Rate | Clicks / Delivered | Content + CTA effectiveness | | CTOR | Clicks / Opens | Content effectiveness (for openers) | | Unsubscribe Rate | Unsubs / Delivered | Content relevance/frequency |

Conversion Metrics

code

Conversion Rate = Conversions / Clicks
Revenue per Email = Total Revenue / Emails Sent
Revenue per Click = Total Revenue / Clicks
Average Order Value = Revenue / Orders

List Health Metrics

code

List Growth Rate = (New - Unsubscribes - Bounces) / Total
Active Rate = Active Subscribers / Total List
Churn Rate = Lost Subscribers / Total List
Email Frequency Tolerance = Unsubs / Email Volume

Industry Benchmarks

Open Rate Benchmarks

| Industry | Average | Good | Great | |----------|---------|------|-------| | E-commerce | 15% | 20% | 25%+ | | SaaS/Tech | 20% | 25% | 30%+ | | Media/Publishing | 22% | 27% | 32%+ | | B2B Services | 21% | 26% | 31%+ | | Retail | 18% | 23% | 28%+ |

Click Rate Benchmarks

| Industry | Average | Good | Great | |----------|---------|------|-------| | E-commerce | 2.0% | 3.0% | 4.0%+ | | SaaS/Tech | 2.5% | 3.5% | 4.5%+ | | Media/Publishing | 4.0% | 5.0% | 6.0%+ | | B2B Services | 2.5% | 3.5% | 4.5%+ | | Retail | 2.5% | 3.5% | 4.5%+ |

Important Context

code

Open Rate Caveats (2025):
├── Apple Mail Privacy impacts tracking
├── 40-50% of opens may be machine opens
├── Focus on click rate as more reliable
├── Use engaged segment for true opens

A/B Testing

What to Test

code

Highest Impact:
├── Subject lines
├── Send time/day
├── From name
├── CTA text

Medium Impact:
├── Preheader text
├── Email length
├── Image vs no image
├── Personalization

Lower Impact:
├── Button color
├── Font choices
├── Minor copy changes

Testing Best Practices

code

Sample Size:
├── Minimum: 500 per variation
├── Recommended: 1,000+ per variation
├── For statistical significance

Duration:
├── Wait 24-48 hours minimum
├── Account for time zones
├── Don't end tests on weekends

Process:
1. Form hypothesis
2. Test ONE variable
3. Split audience randomly
4. Wait for significance
5. Document results
6. Apply winning variation

Subject Line Testing

code

Test Approaches:
├── Long vs short
├── Question vs statement
├── Personalized vs generic
├── Emoji vs no emoji
├── Urgency vs curiosity
├── Benefit vs feature

Example Test:
A: "Your weekly marketing tips"
B: "[Name], 3 quick wins for this week"
Winner: B (+15% opens)

Send Time Testing

code

Common Findings:
├── B2B: Tuesday-Thursday, 9-11am
├── B2C: Varies by audience
├── E-commerce: Evenings and weekends often work
├── Newsletter: Consistent day builds habit

Test Approach:
├── Split list into time zones
├── Test 3-4 different times
├── Run for 4+ weeks
├── Control for content differences

Revenue Attribution

Attribution Models

code

Last-Click Attribution:
├── Credit goes to last email clicked
├── Simple but incomplete
├── Common default in platforms

Multi-Touch Attribution:
├── Credit spread across touchpoints
├── More accurate picture
├── More complex to implement

View-Through Attribution:
├── Credit for opens without clicks
├── Captures influence
├── Can over-credit

Attribution Windows

code

Click Attribution:
├── 24 hours (conservative)
├── 3 days (common)
├── 7 days (generous)
├── 30 days (very generous)

View Attribution:
├── Not recommended for opens
├── Apple Privacy makes unreliable
├── Click-based is more accurate

Revenue Tracking Setup

code

Requirements:
├── Track clicks to website
├── Pass email campaign ID
├── Connect to conversion event
├── Calculate attributed revenue

UTM Parameters:
├── utm_source=email
├── utm_medium=campaign (or automation)
├── utm_campaign=[campaign_name]
├── utm_content=[variation]

Reporting

Weekly Metrics Dashboard

code

## Weekly Email Report

### Campaigns Sent
| Campaign | Sent | Opens | Clicks | Revenue |
|----------|------|-------|--------|---------|
| Weekly Newsletter | 10,000 | 22% | 3.2% | $1,500 |
| Product Launch | 8,000 | 25% | 4.5% | $3,200 |

### Automation Performance
| Flow | Sent | Opens | Clicks | Revenue |
|------|------|-------|--------|---------|
| Welcome Series | 500 | 55% | 12% | $2,100 |
| Abandoned Cart | 200 | 45% | 8% | $1,800 |

### List Health
├── Total Subscribers: 25,000
├── New This Week: 350
├── Unsubscribed: 45
├── Active (30 days): 15,000 (60%)

Monthly Review

code

Analyze:
├── Revenue by campaign type
├── Best performing subject lines
├── Optimal send times
├── Segment performance comparison
├── Automation ROI
├── List growth trends

Optimization

Low Open Rate Fixes

code

If Open Rate < 15%:
├── Test subject lines more aggressively
├── Clean inactive subscribers
├── Check deliverability (authentication)
├── Try different send times
├── Segment by engagement
├── Check spam folder placement

Low Click Rate Fixes

code

If Click Rate < 2%:
├── Improve CTA visibility
├── Simplify email (one clear action)
├── Better value proposition
├── More relevant content
├── Test button vs text links
├── Check mobile rendering

High Unsubscribe Rate Fixes

code

If Unsubscribe > 0.5%:
├── Reduce sending frequency
├── Improve segmentation
├── Better content quality
├── Add preference center
├── Set expectations at signup
├── Review recent changes

Continuous Improvement

code

Monthly:
├── Review metrics vs benchmarks
├── Identify top/bottom performers
├── Extract learnings
├── Apply to future campaigns

Quarterly:
├── Deep dive analysis
├── Strategy review
├── Automation audit
├── List health assessment

Pro Tip: Create a simple scorecard with your 5 most important metrics. Review it weekly and trend it monthly. Patterns become visible quickly, and you'll catch problems before they become serious.

January 20266 min read

Email Analytics

Opens, clicks, conversions, and A/B testing for email success.

Key Metrics

Delivery Metrics

code

Sent: Total emails sent
Delivered: Emails that reached servers
Delivery Rate = Delivered / Sent

Bounces:
├── Hard Bounce: Invalid address (remove immediately)
├── Soft Bounce: Temporary issue (retry)
├── Bounce Rate = Bounces / Sent

Engagement Metrics

Conversion Metrics

code

Conversion Rate = Conversions / Clicks
Revenue per Email = Total Revenue / Emails Sent
Revenue per Click = Total Revenue / Clicks
Average Order Value = Revenue / Orders

List Health Metrics

code

List Growth Rate = (New - Unsubscribes - Bounces) / Total
Active Rate = Active Subscribers / Total List
Churn Rate = Lost Subscribers / Total List
Email Frequency Tolerance = Unsubs / Email Volume

Industry Benchmarks

Open Rate Benchmarks

Click Rate Benchmarks

Important Context

code

Open Rate Caveats (2025):
├── Apple Mail Privacy impacts tracking
├── 40-50% of opens may be machine opens
├── Focus on click rate as more reliable
├── Use engaged segment for true opens

A/B Testing

What to Test

code

Highest Impact:
├── Subject lines
├── Send time/day
├── From name
├── CTA text

Medium Impact:
├── Preheader text
├── Email length
├── Image vs no image
├── Personalization

Lower Impact:
├── Button color
├── Font choices
├── Minor copy changes

Testing Best Practices

code

Sample Size:
├── Minimum: 500 per variation
├── Recommended: 1,000+ per variation
├── For statistical significance

Duration:
├── Wait 24-48 hours minimum
├── Account for time zones
├── Don't end tests on weekends

Process:
1. Form hypothesis
2. Test ONE variable
3. Split audience randomly
4. Wait for significance
5. Document results
6. Apply winning variation

Subject Line Testing

code

Test Approaches:
├── Long vs short
├── Question vs statement
├── Personalized vs generic
├── Emoji vs no emoji
├── Urgency vs curiosity
├── Benefit vs feature

Example Test:
A: "Your weekly marketing tips"
B: "[Name], 3 quick wins for this week"
Winner: B (+15% opens)

Send Time Testing

code

Common Findings:
├── B2B: Tuesday-Thursday, 9-11am
├── B2C: Varies by audience
├── E-commerce: Evenings and weekends often work
├── Newsletter: Consistent day builds habit

Test Approach:
├── Split list into time zones
├── Test 3-4 different times
├── Run for 4+ weeks
├── Control for content differences

Revenue Attribution

Attribution Models

code

Last-Click Attribution:
├── Credit goes to last email clicked
├── Simple but incomplete
├── Common default in platforms

Multi-Touch Attribution:
├── Credit spread across touchpoints
├── More accurate picture
├── More complex to implement

View-Through Attribution:
├── Credit for opens without clicks
├── Captures influence
├── Can over-credit

Attribution Windows

code

Click Attribution:
├── 24 hours (conservative)
├── 3 days (common)
├── 7 days (generous)
├── 30 days (very generous)

View Attribution:
├── Not recommended for opens
├── Apple Privacy makes unreliable
├── Click-based is more accurate

Revenue Tracking Setup

code

Requirements:
├── Track clicks to website
├── Pass email campaign ID
├── Connect to conversion event
├── Calculate attributed revenue

UTM Parameters:
├── utm_source=email
├── utm_medium=campaign (or automation)
├── utm_campaign=[campaign_name]
├── utm_content=[variation]

Reporting

Weekly Metrics Dashboard

code

## Weekly Email Report

### Campaigns Sent
| Campaign | Sent | Opens | Clicks | Revenue |
|----------|------|-------|--------|---------|
| Weekly Newsletter | 10,000 | 22% | 3.2% | $1,500 |
| Product Launch | 8,000 | 25% | 4.5% | $3,200 |

### Automation Performance
| Flow | Sent | Opens | Clicks | Revenue |
|------|------|-------|--------|---------|
| Welcome Series | 500 | 55% | 12% | $2,100 |
| Abandoned Cart | 200 | 45% | 8% | $1,800 |

### List Health
├── Total Subscribers: 25,000
├── New This Week: 350
├── Unsubscribed: 45
├── Active (30 days): 15,000 (60%)

Monthly Review

code

Analyze:
├── Revenue by campaign type
├── Best performing subject lines
├── Optimal send times
├── Segment performance comparison
├── Automation ROI
├── List growth trends

Optimization

Low Open Rate Fixes

code

If Open Rate < 15%:
├── Test subject lines more aggressively
├── Clean inactive subscribers
├── Check deliverability (authentication)
├── Try different send times
├── Segment by engagement
├── Check spam folder placement

Low Click Rate Fixes

code

If Click Rate < 2%:
├── Improve CTA visibility
├── Simplify email (one clear action)
├── Better value proposition
├── More relevant content
├── Test button vs text links
├── Check mobile rendering

High Unsubscribe Rate Fixes

code

If Unsubscribe > 0.5%:
├── Reduce sending frequency
├── Improve segmentation
├── Better content quality
├── Add preference center
├── Set expectations at signup
├── Review recent changes

Continuous Improvement

code

Monthly:
├── Review metrics vs benchmarks
├── Identify top/bottom performers
├── Extract learnings
├── Apply to future campaigns

Quarterly:
├── Deep dive analysis
├── Strategy review
├── Automation audit
├── List health assessment

Pro Tip: Create a simple scorecard with your 5 most important metrics. Review it weekly and trend it monthly. Patterns become visible quickly, and you'll catch problems before they become serious.