For Email Marketing Specialists ·
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
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.
Take test #1 from the roadmap. Ask ChatGPT to generate the actual variants:
For Test 1 ([test name]), generate the control and variant versions.
Control: [describe your current approach, e.g., "subject lines with the offer amount"]
Variant: [the thing you're testing, e.g., "subject lines with the recipient's name personalized"]
Generate 3 possible variants so I can pick the strongest one. For each: subject line text, preview text, and the hypothesis for why this variant should outperform the control.
Good A/B testing requires a pre-test hypothesis. Ask ChatGPT to help you document it:
I'm running this A/B test: [describe test].
Write a short test brief for my records:
1. Hypothesis: "If we [change X], then [metric Y] will [increase/decrease] because [reason Z]"
2. Success criteria: what result would constitute a meaningful win
3. Risk: what could make this test ambiguous or invalid
4. After the test: what we'll do if variant wins vs. if control wins
Save this document — it becomes your testing log.
Once your test runs, bring the results back to ChatGPT:
Here are my A/B test results:
- Test: [describe]
- Control: [metrics — open rate, CTR, revenue]
- Variant: [metrics]
- Total sample size: [number] per variant
- Test duration: [days/sends]
Was this result statistically significant? What does it mean? Should I roll out the variant, keep the control, or run a follow-up test? What would you test next based on this result?
What you should see: An interpretation of whether the result is meaningful and a clear recommendation on next steps.
After your first few tests, ask ChatGPT to help you schedule them:
I run [number] campaigns per week/month. I want to run [number] A/B tests per month.
Here is my testing roadmap (tests 1-12): [paste the list].
Create a 3-month testing calendar showing which test runs when, accounting for:
- Sufficient time between tests on the same variable
- Not testing during holiday periods that could skew results
- Building toward a cumulative understanding of what works
1. Roadmap generation:
My email metrics: [paste]. Tests already run: [list]. Generate a 12-test A/B roadmap targeting [metric]. Rank by impact.
2. Variant generation:
For test: [name and description], generate 3 variant options with subject line, preview text, and the hypothesis for each.
3. Test brief:
Write a test brief for: [describe test]. Include hypothesis, success criteria, risk factors, and post-test decision rules.
4. Results interpretation:
A/B test results: Control [metrics] vs. Variant [metrics], sample size [n]. Is this significant? What does it mean? What do we test next?
5. Follow-up test design:
Control won (or variant won) in [test]. What follow-up test would help us understand why this happened and what to optimize next?