I used to write emails based on gut feeling. Some worked. Most didn’t. I had no idea why.
Then I started tracking four numbers every week. Within three months, my click rate doubled and my email revenue went up 67%. Nothing changed about my writing ability. What changed is that I stopped guessing and started measuring.
Most bloggers either ignore their email analytics entirely or drown in data they don’t understand. Both are equally useless. You don’t need a data science degree. You need to watch four metrics, understand what they’re telling you, and make small adjustments based on what you learn.
The Four Email Metrics That Matter
There are dozens of metrics your email platform tracks. Ignore most of them. Focus on these four.
Open Rate
This tells you the percentage of delivered emails that get opened. It answers one question: are your subject lines working?
A quick note on accuracy: Apple’s Mail Privacy Protection (introduced in 2021) pre-loads tracking pixels, inflating open rates for Apple Mail users. This affects roughly 40-50% of most lists. Your real open rate is probably 5-10 percentage points lower than what your dashboard shows. Don’t panic about this. Just know that open rate is now a directional metric, not a precise one. Trends matter more than absolute numbers.
What to do when open rates drop: First, check your deliverability (Chapter 11). If inbox placement is fine, the problem is your subject lines. Test new approaches. If open rates are declining gradually over weeks, your list might be going cold, and you need to increase email frequency or improve relevance.
Click Rate
This is the percentage of email recipients who clicked a link. Not the percentage of people who opened, but the percentage of everyone who received it. This is your most important metric because it measures action, not just attention.
Click rate tells you whether your email content and CTA are compelling enough to make someone stop what they’re doing and tap a link. That’s a high bar. You’re competing with everything else on their phone.
When click rates are low but open rates are healthy, the problem is inside your email. Your hook isn’t strong enough, your CTA is unclear, or you’re asking for too many actions at once. Go back to Chapter 12 and tighten your copy.
Conversion Rate
This measures how many people who clicked actually did the thing: bought the product, signed up for the webinar, downloaded the resource. Your email platform might not track this natively. You’ll need UTM parameters and Google Analytics (or whatever analytics tool you use) to connect the dots.
Conversion rate tells you whether your email sent the right people to the right page with the right expectations. If lots of people click but few convert, the issue is usually a mismatch between what the email promised and what the landing page delivers. Or the landing page just isn’t good enough. Either way, it’s not an email problem. It’s a funnel problem.
Unsubscribe Rate
This is the percentage of recipients who unsubscribe per email. A healthy unsubscribe rate is 0.1-0.3% per email. Above 0.5% per email consistently, and something is off. Either you’re emailing too frequently, your content doesn’t match what subscribers signed up for, or your list quality is poor.
Some bloggers freak out about unsubscribes. Don’t. Every unsubscribe makes your list healthier. The person leaving was never going to buy from you. They’re doing you a favor by self-selecting out. The only time to worry is when unsubscribe rates spike on a specific email or trend upward over several weeks.
Benchmarks by Niche and List Size
I’m going to share real numbers from lists I’ve managed and benchmarks I’ve collected from working with 800+ clients. These aren’t from some generic industry report. They’re from actual blogger and creator email programs.
By niche:
Personal finance and business lists tend to have lower open rates (18-25%) but higher conversion rates because the audience is more transactional. They’re used to buying things they read about.
Creative and lifestyle lists often get higher open rates (28-38%) but lower click rates because the audience is there for inspiration, not action. Getting them to click requires more persuasion.
Tech and developer lists sit in the middle (22-30% open rates) with decent click rates when you share tools or resources. This audience clicks on anything that might save them time.
Marketing and blogging lists (which is probably you) typically see 25-35% open rates and 3-5% click rates for a healthy list.
By list size:
Smaller lists almost always outperform larger ones on a percentage basis. A 500-person list getting 40% open rates and 8% click rates is normal. That same creator’s list at 15,000 will likely see 28% open rates and 4% click rates. This isn’t failure. It’s math. Larger lists include more casual subscribers, people who signed up a year ago and forgot about you.
The metric that should scale with your list is revenue per email sent. When I’m working with a client, I track dollars generated per email delivered. A 500-person list generating $200 per email ($0.40/subscriber/email) should, at 15,000 subscribers, generate $3,000-$4,500 per email if everything is working right. If revenue per subscriber drops significantly as you grow, that tells you something about your list quality or your offers, and it’s time to investigate.
A/B Testing Emails
A/B testing is how you turn opinions into data. Instead of guessing whether a curiosity subject line beats a benefit subject line, you test both and let your audience decide.
What to test first: Subject lines. This is where A/B testing has the highest impact with the lowest effort. Most email platforms let you split your list automatically, send version A to 15% and version B to another 15%, wait a few hours, then send the winner to the remaining 70%.
Things I’ve learned from hundreds of subject line tests:
Shorter usually beats longer. My sweet spot is 4-7 words. Anything over 10 words loses mobile readers because the subject gets cut off.
Numbers beat no numbers. “5 tools I use daily” beats “The tools I use daily” almost every time. Specificity signals value.
Lowercase often beats title case. “how I got 200 subscribers from one post” feels more personal than “How I Got 200 Subscribers From One Post.” It looks like a message from a friend, not a marketing email. This doesn’t always win, but it wins more often than you’d expect.
What to test second: Send times. Tuesday through Thursday at 8-10am tends to be the default recommendation. But I’ve had clients whose lists perform best on Sunday mornings. One client in the personal finance niche gets their highest open rates on Saturday at 7am. The only way to know is to test.
Split your list in half. Send the same email to one half on Tuesday morning and the other half on Thursday morning. Compare open and click rates. Do this three times with different emails to get reliable data. One test isn’t enough because individual emails have too many variables.
What to test third: Content format. This takes more work but can produce big insights. Test a story-driven email against a tips-based email. Test a long email against a short one. Test plain text against designed HTML. I’ve found that plain-text-style emails (minimal formatting, no images, no fancy templates) outperform designed emails for most blogger audiences. But your audience might be different. Test it.
Testing rules I follow:
Test one variable at a time. If you change the subject line AND the send time AND the CTA, you won’t know which change caused the result.
Wait for statistical significance. Most email platforms will tell you when a test has a clear winner. Don’t call a test after 200 opens. Wait until you’ve got enough data, usually 500+ recipients per variation at minimum.
Document everything. Keep a testing log with the date, what you tested, the results, and what you learned. After six months of disciplined testing, you’ll have a playbook that’s specific to your audience. That playbook is worth more than any generic email marketing advice.
Analyzing Automation Performance
Your automated sequences (welcome series, nurture sequences, sales funnels) need regular attention. I check mine monthly.
For each email in a sequence, track:
The drop-off rate. What percentage of people who opened Email 1 also opened Email 2? Email 3? When you see a sharp drop-off between two specific emails, that’s where your sequence is losing people. That email needs rewriting or removing.
Per-email click rates. Which emails in your sequence generate the most clicks? Those are your strongest emails. Study them. What’s different about the subject line, the hook, the CTA? Apply those lessons to your weaker emails.
Time-to-conversion. How long does it take from when someone enters your sequence to when they convert? If your welcome sequence is 7 emails over 14 days but most conversions happen on Email 2, your sequence might be too long. Or your best offer is buried too deep. Test moving it earlier.
I review automation stats on the first Monday of every month. I keep a spreadsheet with each email’s open rate, click rate, and conversion rate tracked month over month. When something dips two months in a row, I rewrite it. This monthly habit is the reason my automated sequences keep performing year after year instead of decaying.
One thing that surprises people: automated emails don’t stay effective forever. Subject lines that worked two years ago might not work today. Cultural references go stale. Links break. Offers change. Review your sequences at least quarterly and update anything that feels dated.
The Monthly Email Analytics Review
Block 60 minutes on the first Monday of each month. This is your email analytics review. Here’s exactly what I do.
Step 1: Pull the numbers. Open rate, click rate, conversion rate, unsubscribe rate for every email sent last month. Export to a spreadsheet if your platform doesn’t have good reporting.
Step 2: Find your best and worst. Identify the top-performing email (highest click rate) and the worst-performing email (lowest click rate). Compare them. What’s different? Subject line style? Content format? Length? Send day?
Step 3: Check automation health. Pull stats for your automated sequences. Look for drop-offs, declining open rates, or conversion changes compared to the previous month.
Step 4: Review list growth. How many new subscribers did you add? How many unsubscribed? What’s the net change? Is the list growing, flat, or shrinking? If you lost more than you gained, figure out why.
Step 5: Set one goal for next month. Not five goals. One. “Improve average click rate from 3.2% to 3.8%.” Or “Test three new subject line formats.” Or “Rewrite the weakest email in the welcome sequence.” One focused goal makes you ten times more likely to follow through than a list of five improvements you’ll ignore.
This review takes an hour. Do it twelve times a year, and you’ll improve your email program more than someone who reads fifty blog posts about email marketing. Data beats theory. Consistency beats intensity.
Using Data to Improve Future Emails
Analytics aren’t just for looking backward. They’re your roadmap for what to write next.
Topic mining from click data. When an email about a specific topic gets significantly higher clicks, your audience is telling you something. Double down. Write more about that topic. Create a product around it. If your email about “how I use Notion for content planning” gets 8% click rate while everything else gets 3-4%, you’ve found a vein of gold. Mine it.
Subject line patterns. After three months of tracking, you’ll notice patterns. Maybe your audience loves numbers. Maybe they respond to questions. Maybe “how I…” outperforms “how to…” consistently. These patterns are specific to your audience and more valuable than any generic advice.
Optimal email length. Track click rates against email word count. Some audiences prefer 300-word punchy emails. Others engage more with 800-word stories. I’ve found that most blogger audiences prefer 400-600 words for regular emails and don’t mind 800-1,000 words for story-driven emails. But your data will tell you what works for your specific list.
Send day and time patterns. After six months of data, you’ll know your audience’s rhythm. Maybe they’re most active Tuesday mornings. Maybe Friday afternoon is dead. Schedule accordingly.
Feedback loops. Ask subscribers what they want. Once a quarter, send a one-question email: “What’s your biggest challenge with [your topic] right now?” The replies are content gold. They tell you exactly what to write about, in your subscribers’ own words. I’ve generated entire product ideas from these responses.
The bloggers who make real money from email aren’t the ones with the biggest lists or the fanciest tools. They’re the ones who pay attention to what their data is telling them and adjust accordingly. That’s the optimization loop: send, measure, learn, improve, repeat. Do it consistently for a year, and your email program will be unrecognizable compared to where you started.
Chapter Checklist
- Know your current open rate, click rate, conversion rate, and unsubscribe rate
- Set up UTM parameters for all email links to track conversions
- Run your first A/B test on subject lines with your next email
- Create a testing log spreadsheet (date, variable tested, results, learning)
- Review your automated sequence stats and identify the biggest drop-off point
- Block 60 minutes on the first Monday of next month for your analytics review
- Identify your best-performing email topic from the last 90 days
- Set up Google Postmaster Tools if you haven’t already (from Chapter 11)
- Calculate your revenue per subscriber per email for the last month
Chapter Exercise
Do your first monthly analytics review right now, even if it’s not the first Monday of the month.
- Export your email stats from the last 30 days into a spreadsheet with columns: date sent, subject line, open rate, click rate, unsubscribe rate.
- Sort by click rate. Study your top three emails. Write down what they have in common (topic, subject line style, email length, send day).
- Study your bottom three emails. Write down what they have in common.
- Look at your automated sequences. Find the email with the biggest drop-off from the previous email in the sequence.
- Based on what you’ve learned, set one specific goal for next month. Write it down where you’ll see it.
If you don’t have 30 days of data yet, that’s fine. Start tracking now. Set up your spreadsheet, add your next five emails to it, and come back to this exercise in a month. The important thing is building the habit. The insights will come once you have enough data to work with.