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Testing & Optimization

A/B Testing

Comparing two versions of an email to determine which performs better.

Definition

A/B testing (also called split testing) in email marketing is the practice of sending two versions of an email with one variable changed to different segments of your audience to determine which version performs better. Common elements to test include subject lines, send times, content, and calls to action.

Why It Matters

A/B testing removes guesswork from email optimization. Instead of assuming what works, you let data guide decisions. Even small improvements from testing compound over time, significantly improving overall email performance and ROI.

How It Works

You create two versions of an email with one element different (A and B). Your email platform randomly splits a portion of your list and sends each version to half. After a set time, it measures which version achieved better results (opens, clicks, conversions). The winner can then be sent to the remaining list.

Best Practices

  • 1Test only one variable at a time for clear results
  • 2Use a large enough sample size for statistical significance
  • 3Define your success metric before starting the test
  • 4Run tests long enough to capture meaningful data
  • 5Document and apply learnings to future campaigns

Built-in A/B Testing

Sequenzy makes A/B testing easy with built-in tools for subject lines, content, and send times.

Learn More

Frequently Asked Questions

Start with high-impact elements: subject lines (biggest impact on opens), send times, CTA button copy and color, email length, personalization, and offers. Test one element at a time.

For reliable results, you typically need at least 1,000 subscribers per variation. Smaller lists can still test, but results may be less statistically reliable. Your ESP likely has a built-in calculator.

Run tests long enough to reach statistical significance, typically 2-4 hours for subject line tests with decent list sizes. Longer tests (24+ hours) may be needed for click or conversion metrics.