A/B Test Emails in Loops

Prerequisites

• Loops audience large enough for statistical significance (minimum 500 contacts per variant)
• Baseline metrics from previous sends to compare against

Steps

1. Choose what to test. Test one variable at a time. High-impact variables in order: Subject line (biggest impact on open rate), CTA text and placement (biggest impact on click rate), Send time (impacts open rate), Email length (impacts engagement). Never test multiple variables simultaneously -- you will not know what caused the difference.

2. Create variants. For subject line tests in broadcasts, create two versions of your email with different subject lines but identical body content. Make the variants meaningfully different -- "New Feature: AI Reports" vs "Your reports just got smarter" tests framing, not just word choice.

3. Set the test split. Send variant A to 25% of the segment and variant B to 25%. Hold the remaining 50% to receive the winning variant after results are in. This maximizes the impact of your test.

4. Define your success metric. Before launching, decide what wins: open rate (for subject line tests), click rate (for content tests), or reply rate (for sales-oriented emails). Write down your hypothesis: "I believe the question subject line will have 10% higher open rate than the statement."

5. Wait for statistical significance. Let the test run for 24-48 hours before declaring a winner. You need at least 100 opens per variant for a subject line test to be meaningful. Small differences (less than 5%) may not be statistically significant.

6. Document and compound learnings. Record every test result in a shared doc: what was tested, hypothesis, result, and takeaway. Over time, you build a library of proven patterns for your specific audience. Apply winning patterns to your automated sequences.