Build a testing process around useful email decisions
The guide distinguishes engagement, revenue, and list-health measures before developing a testing workflow. It introduces prioritization and hypothesis planning, then explains how to review results. A post-test decision tree considers statistical evidence, practical impact, and potential effects on other goals before implementation or further experimentation.
The process connects each experiment with a documented decision and a record of learning that can inform later email work.
What you’ll find inside
- Engagement, revenue, and list-health metrics
- Test prioritization and hypotheses
- Experiment design and common pitfalls
- Statistical and practical impact review
- Post-test decisions and learning records
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