Email Analytics

Email Analytics — Sub-Skill

Overview

Email analytics transforms raw data into actionable insights. This sub-skill covers every key metric, how to calculate them, what benchmarks to target, how to run cohort analysis, and how to identify trends before they become problems.

Command: /email analytics

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Analytics Audit Checklist

• [ ] Open rate is tracked and benchmarked by industry
• [ ] Click rate and CTOR are measured separately
• [ ] Conversion tracking is set up (UTM parameters, pixel, or integration)
• [ ] Revenue per email is calculated (for e-commerce/monetized lists)
• [ ] List growth rate is monitored monthly
• [ ] Churn rate is tracked (unsubscribes + bounces + complaints)
• [ ] Engagement trending is reviewed weekly
• [ ] Cohort analysis is performed quarterly
• [ ] Campaign-level AND automation-level metrics are tracked
• [ ] Metrics are compared to industry benchmarks

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Key Metrics Defined

1. Open Rate

Open Rate = (Unique Opens / Emails Delivered) x 100

Important caveat: Since Apple's Mail Privacy Protection (MPP, September 2021), open rates are inflated for Apple Mail users. MPP pre-loads tracking pixels, registering opens even if the email wasn't read.

Adjustment: Segment Apple Mail users separately. Focus on click rate as the primary engagement metric for Apple Mail subscribers.

| Performance | Open Rate | Interpretation | |-------------|-----------|---------------| | Excellent | >30% | Top performer or niche audience | | Good | 20-30% | Above average | | Average | 15-20% | Industry standard | | Below Average | 10-15% | Subject line or deliverability issues | | Poor | 5% | Highly engaged audience | | Good | 3-5% | Strong engagement | | Average | 2-3% | Industry standard | | Below Average | 1-2% | Content or CTA issues | | Poor | 20% | Content is highly relevant | | Good | 12-20% | Good content-audience match | | Average | 8-12% | Average content engagement | | Below Average | 5-8% | Content doesn't match audience expectations | | Poor | $0.50 | Highly targeted, promotional | | Good | $0.10-$0.50 | Well-segmented campaigns | | Average | $0.02-$0.10 | Standard newsletter/promotional | | Below Average | 5%/month | Strong acquisition, low churn | | Good | 2-5%/month | Healthy growth | | Flat | 0-2%/month | Growth matches churn | | Declining | Negative | Churn exceeds acquisition — urgent |

7. Churn Rate

Monthly Churn = (Unsubscribes + Bounces + Complaints) / Total List Size x 100

| Performance | Churn Rate | Action | |-------------|-----------|--------| | Excellent | 2%/month | Emergency — fix immediately |

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Cohort Analysis

Cohort analysis groups subscribers by when they joined and tracks their behavior over time. This reveals whether your email program is getting better or worse.

How to Run a Cohort Analysis

1. Define cohorts by signup month (e.g., Jan 2026 signups, Feb 2026 signups)
2. Track key metrics for each cohort over time:

• Open rate by month since signup
• Click rate by month since signup
• Conversion rate by month since signup
• Churn rate by month since signup

Cohort Table Example

Cohort     | Month 1 | Month 2 | Month 3 | Month 4 | Month 5 | Month 6
           | Opens   | Opens   | Opens   | Opens   | Opens   | Opens
-----------+---------+---------+---------+---------+---------+--------
Jan 2026   | 45%     | 32%     | 28%     | 24%     | 22%     | 20%
Feb 2026   | 42%     | 30%     | 25%     | 22%     | 20%     |
Mar 2026   | 48%     | 35%     | 30%     | 27%     |         |
Apr 2026   | 50%     | 38%     | 33%     |         |         |

What to Look For

| Pattern | Meaning | Action | |---------|---------|--------| | Month 1 improving over time | Acquisition quality improving | Keep doing what's working | | Month 1 declining over time | Acquisition quality declining | Audit lead sources | | Steep drop after Month 1 | Welcome sequence not retaining | Improve welcome series | | Gradual steady decline | Normal engagement decay | Add re-engagement triggers | | Sudden drop at Month X | Something broke at that point | Investigate that email/event | | Flat/improving after Month 3 | Strong long-term engagement | Healthy program |

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Engagement Trending

Track these metrics weekly to catch issues before they become crises:

Weekly Dashboard Metrics

| Metric | Green | Yellow | Red | |--------|-------|--------|-----| | Open Rate | Within 10% of 30-day avg | 10-20% below | >20% below | | Click Rate | Within 10% of 30-day avg | 10-20% below | >20% below | | Bounce Rate | 1% | | Spam Complaints | 0.1% | | Unsubscribe Rate | 0.5% | | List Growth | Positive | Flat | Negative |

Trend Alerts

Set up alerts for:

• Open rate drops >15% compared to 30-day average
• Spam complaint rate exceeds 0.1%
• Bounce rate exceeds 1% on any send
• Unsubscribe rate exceeds 0.5% on any send
• Delivery rate drops below 95%

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Attribution Models

Email-Specific Attribution

| Model | How It Works | Best For | |-------|-------------|----------| | Last Click | Email gets credit if it was last click before conversion | Simple, conservative | | First Click | Email gets credit if it was first touch | Awareness campaigns | | Linear | Credit split equally across all email touches | Multi-email journeys | | Time Decay | Recent emails get more credit | Long sales cycles | | View-Through | Email gets credit if opened (not clicked) before conversion | Brand awareness |

UTM Parameter Standards

utm_source=email
utm_medium=email
utm_campaign={campaign_name}
utm_content={cta_variant}
utm_term={subject_line_variant}

Example:

https://example.com/product?utm_source=email&utm_medium=email&utm_campaign=spring_sale_2026&utm_content=cta_button_red&utm_term=subject_a

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Analytics Scoring

| Criterion | Weight | Scoring | |-----------|--------|---------| | Open rate tracked & benchmarked | 15% | 0-100 | | Click rate & CTOR measured | 15% | 0-100 | | Conversion tracking set up | 20% | 0 or 100 | | Revenue attribution configured | 15% | 0 or 100 | | List growth/churn monitored | 10% | 0 or 100 | | Weekly engagement review | 15% | 0 or 100 | | Cohort analysis done quarterly | 10% | 0 or 100 |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: 0-shiv
• Source: 0-shiv/secondstep-claude-skills
• License: MIT

Install and usage instructions live in the source repository linked above.