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A/B test brief for landing page personalization
An A/B test brief is a short document written before a test that states what will change, for whom, which metric decides, how big a sample is needed and when the test stops. Writing it first prevents moving the goalposts after the results come in.
When to use it: Use it every time you test a personalized page against the original, or two personalization approaches against each other.
The brief
- Test name
- [2026-10 Google Ads competitor headline]
- Owner and reviewers
- Who runs it, who signs off on the result.
- Hypothesis
- Because [observation], changing [element] for [audience] will increase [metric], measured by [method].
- Audience
- Which traffic enters the test: channel, campaign, UTM values, new vs. returning.
- Control
- The original page, unchanged.
- Variant(s)
- What changes: headline, supporting copy, CTA. Paste the exact copy or the instruction given to the tool.
- Traffic split
- For example 50/50, or 90/10 if the control is a holdout.
- Primary metric
- One metric that decides: demo requests, trial signups, qualified pipeline.
- Guardrail metrics
- Metrics that must not get worse: bounce rate, lead quality, unsubscribe rate.
- Minimum detectable effect and sample size
- Use a sample size calculator with your baseline conversion rate. Write the number down before launch.
- Confidence level
- For example 95%.
- Stopping rule
- Stop when the sample size is reached, or after [n] weeks, whichever comes first. No peeking-based early stops.
- Decision
- What you will do if the variant wins, loses or is inconclusive.
- Result and learning
- Filled in after the test: numbers, decision, what to try next.
Automate this in GetIntent
GetIntent Experiments (Pro plan and above) split traffic between the original page and one or more personalization variants, let you set weights, UTM targeting, minimum sample size and confidence level, and report conversion rates with confidence intervals for each variant.