Marketing testing and A/B testing with AI
Design a fair experiment and interpret the result without false certainty.
Work through the steps
- Write a hypothesis connecting one change to one buyer behavior. Choose a primary metric such as qualified meetings per eligible visitor, and guardrails such as unsubscribes or lead quality.
- Create two variants differing in one meaningful factor. Use AI for options and critique, but have a person verify claims and tracking. Randomize eligible users and keep assignment consistent.
- Estimate the required sample and duration from baseline rate and the smallest worthwhile effect using a suitable statistical method. Set the stopping rule in advance; do not stop merely when a dashboard first looks positive.
- Analyze denominators, uncertainty, segment balance, and downstream quality. AI can help explain results, but verify calculations. For low traffic, use qualitative tests and report directional evidence rather than claiming a winner.
In practice
If variant A produces 3 meetings from 100 visits and B produces 5, B looks promising but that alone does not establish a reliable improvement. Small samples can swing widely.
A prompt to make your own
Replace the brackets with approved context. Ask the assistant to identify missing information before proceeding, and review its output against your sources.
Design an experiment for [page/campaign]. Baseline: [rate and sample]. Hypothesis: [change]. Primary metric: [definition]. Identify randomization unit, exclusions, guardrails, minimum worthwhile effect, sample-planning inputs, and stopping rule. Do not invent a sample size when inputs are missing. Explain what an inconclusive result means.
Your assignment
Write a one-page test plan before creating the variants.
What to measure
Primary conversion metric with uncertainty and downstream lead quality.