What you'll accomplish
You'll have a structured 3-month A/B testing roadmap with 12+ prioritized test hypotheses, a system for generating variants for each test, and a process for interpreting results and deciding what to do next. You'll stop running random "emoji vs. no emoji" tests and start running tests that actually move your metrics.
What you'll need
How-To Guide: Build a Systematic A/B Testing Program with ChatGPT
Open ChatGPT and paste this prompt with your real metrics:
I run email marketing for a [brand type] with these metrics:
- Open rate: [%] (industry avg for our category is ~[%])
- Click-through rate: [%]
- Click-to-open rate: [%]
- Conversion rate: [%]
- Unsubscribe rate: [%]
Tests we've already run: [list them]
My biggest gap vs. benchmarks: [which metric is furthest below average]
Generate a 3-month A/B testing roadmap with 12 specific test hypotheses. For each:
1. Test name
2. What we're testing (control vs. variant)
3. Which metric it targets
4. Why this test has high potential impact
5. Minimum list size needed to get statistical significance
6. Expected test duration (# of sends to see reliable data)
Rank by expected impact on [your priority metric].
What you should see: A numbered list of 12 tests organized by priority, with clear hypotheses and measurement criteria.