A/B Test Calculator

A/B testing (also called split testing) is essential for optimizing cold email performance. By testing variations of your emails with statistical rigor, you can make data-driven decisions about what works best for your audience.

What Can You A/B Test in Cold Emails?

• Subject lines: The highest-impact element. Test length, personalization, curiosity vs. direct approaches.
• Opening lines: First impressions matter. Test different hooks and personalizations.
• Call-to-action: Compare soft asks vs. direct meeting requests.
• Email length: Short and punchy vs. detailed and thorough.
• Send times: Morning vs. afternoon, weekdays vs. specific days.
• Sender name: Personal name vs. company name, with or without title.

Understanding Statistical Significance

Statistical significance tells you whether the difference between your two versions is likely real or just random chance. A 95% confidence level (the industry standard) means there's only a 5% probability the difference is due to random variation.

How Many Emails Do You Need?

The sample size depends on your baseline conversion rate and the minimum difference you want to detect. As a general rule:

• For a 20% open rate testing a 10% improvement: ~1,500 emails per variation
• For a 5% reply rate testing a 20% improvement: ~3,000 emails per variation
• Smaller differences require larger sample sizes to detect reliably

A/B Testing Best Practices

• Test one variable at a time for clear insights
• Run tests until you reach statistical significance
• Use random assignment to eliminate bias
• Document your hypotheses before testing
• Implement winners and keep iterating