Definition of Split Testing
Split testing (also known as A/B testing) is dividing your subscriber list to send different newsletter versions and see which performs better.
You test one element at a time by pitting your current best version, the "control" or "champion," against a new version, the "challenger." You can test anything from subject lines to layout to CTAs, measuring success through opens, clicks, or conversions to make data-driven decisions.
Why you should care
Split testing helps you stop throwing darts in the dark and start hitting targets consistently. By testing elements like send times, you might discover your Tuesday 10am newsletter actually performs 20% better at Thursday 3pm. Or that personalizing your greeting boosts engagement more than changing your CTA color.
The biggest challenge is maintaining testing discipline. Many creators run one test, see minimal results, and abandon testing altogether.
The secret is consistent testing of high-impact elements. Things like subject lines and CTAs over multiple sends.
Even small 2-3% improvements compound over time, potentially turning a 20% unique click-through rate into 30%+ within months. That means hundreds or thousands more readers engaging with your content.
Split while you're ahead
Let us identify, configure, and manage effective split testing for you. This way, you got more time to develop content.
Some resources we rely on
Ask Claude for help with Split Testing
Copy and paste this prompt into Claude or the AI of your choice. Be sure to tweak the context for your situation.
<goal>
Help me implement a systematic split testing program that improves both open and click rates for my newsletter.
</goal>
<context>
* I publish a [FREQUENCY] newsletter to [# of SUBS] subscribers
* Current open rate: [OPEN RATE]%, click rate: [CLICK RATE]%
* Using [PLATFORM] as my ESP
* Tried random tests before without clear results
* Need a structured approach that shows progress in 4-6 weeks
</context>
<o>
Please provide:
* A 6-week split testing calendar with specific elements to test each week
* How to prioritize which elements to test first for biggest impact
* Minimum sample sizes needed for statistical significance
* Simple method to track results and make decisions
* When to stick with winners vs. continue testing
</o>
<example>
Week 1 Test Plan:
- Element: Subject Line Length
- Version A: Short (3-5 words)
- Version B: Medium (7-9 words)
- Success metric: Open rate
- Sample size: 30% of list for each variant
</example>
<guardrails>
* Focus on tests that require minimal design/coding skills
* Keep tracking simple without needing external analytics
* Prioritize tests with highest potential ROI
* Avoid suggesting complex segmentation beyond basic A/B splits
</guardrails>