Analytics, Testing, and Deliverability

Measuring What Matters

🔄 Quick Recall: In the previous lesson, you built your monetization stack — sponsorships, paid subscriptions, affiliates, and digital products. Now you’ll learn to measure everything, test systematically, and ensure your emails reach the inbox in the first place.

Data without action is just numbers on a screen. This lesson teaches you which metrics to track, how to test improvements, and the technical settings that determine whether your newsletter reaches subscribers at all.

The Metrics That Matter

Not all metrics are equal. Focus on these in order of importance:

✅ Quick Check: Why is deliverability rate more important than open rate? Because open rate measures the percentage of delivered emails that get opened. If your deliverability drops from 98% to 80%, you’ve lost 20% of your audience before they even have the chance to open. Fix deliverability first, then optimize opens. A 40% open rate on 80% deliverability reaches fewer people than a 30% open rate on 99% deliverability.

A/B Testing Framework

A/B testing is how you systematically improve. Send version A to half your audience and version B to the other half. The winner becomes your new baseline.

What to test (in order of impact):

1. Subject lines — Highest impact, easiest to test. Test different formulas (curiosity vs. direct benefit), different lengths, emoji vs. no emoji.

2. Send time — Morning vs. afternoon vs. evening. Test across 4-6 issues to find your audience’s optimal window. Research suggests 3-7 PM gets the most opens for many audiences.

3. Content format — Long-form vs. short-form, text-heavy vs. mixed media, one topic vs. curated links.

4. CTA placement — Top of email vs. bottom, inline text link vs. button, single CTA vs. multiple.

Testing rules:

• Change only one variable per test
• Need 500+ subscribers per variant for reliable results
• Run the test long enough to reach statistical significance (most platforms tell you when)
• Document every test and its results — patterns emerge over time

AI for test analysis:

Analyze my A/B test results and suggest next steps:

Test: [what you tested]
Version A: [description] → Open rate: [X%], CTR: [X%]
Version B: [description] → Open rate: [X%], CTR: [X%]
Sample size: [subscribers per variant]

Questions:
1. Is this result statistically significant or could it be random?
2. What does this tell me about my audience's preferences?
3. What should I test next based on this result?
4. How should I apply this finding to my non-test issues?

Email Deliverability

Deliverability is the foundation everything else rests on. If emails don’t reach the inbox, nothing else matters.

Authentication (One-Time Setup)

Three protocols that tell email providers your emails are legitimate:

Impact: Authenticated domains achieve 2.7x higher inbox placement. Gmail, Yahoo, and Outlook all require authentication for bulk senders as of 2025.

Important: Most newsletter platforms handle basic SPF and DKIM automatically. But if you use a custom domain (recommended), you must configure these DNS records yourself. Your platform will provide step-by-step instructions.

List Hygiene

A clean list has better deliverability and more accurate metrics:

Monthly maintenance:

• Remove subscribers who haven’t opened in 90 days (they’re hurting your metrics)
• Remove bounced addresses immediately
• Monitor spam complaint rate (must stay below 0.1%)

Re-engagement before removal: Send a “We miss you” email to inactive subscribers. If they don’t engage after 1-2 re-engagement attempts, remove them. A smaller, engaged list outperforms a larger, disengaged one.

✅ Quick Check: Why should you remove inactive subscribers even though it shrinks your list? Because email providers monitor your engagement rates. If you send to 10,000 subscribers but only 3,000 ever open, Gmail learns that your emails aren’t wanted and starts putting them in spam — even for the subscribers who do want them. Removing 7,000 inactive subscribers improves deliverability for the 3,000 who engage, and your metrics become accurate instead of artificially depressed.

Avoiding Spam Filters

Building a Performance Dashboard

Track your newsletter’s health with a simple weekly review:

Review every Sunday. Identify what changed and why. Adjust for the coming week.

Key Takeaways

• Track metrics in order of importance: deliverability → opens → clicks → replies → unsubscribes → growth
• A/B test one variable at a time, starting with subject lines (highest impact)
• Authentication (SPF, DKIM, DMARC) provides 2.7x better inbox placement — set it up before sending
• Clean your list monthly — removing inactive subscribers improves deliverability for everyone who remains
• Keep spam complaint rate below 0.1% and bounce rate below 2%
• Build a weekly performance review habit to catch trends before they become problems

Up Next: In the final lesson, you’ll combine everything into your complete newsletter launch plan — ready to execute on Day 1.