Playbook

Menu Redesign A/B Test Plan & Results Tracker

A practical, data-first playbook and interactive test-plan + results tracker that helps teams design low-risk A/B experiments for recipes, prices, or menu layout changes, run them with clear run-rules and stop-rules, capture results, and decide whether to roll out changes.

Interactive Tool

Menu Redesign A/B Test Plan & Results Tracker

Make menu changes with low risk and clear evidence

This playbook helps you design a focused A/B test for a recipe change, price change, or menu layout update. Use the form below to capture your hypothesis, sample and run rules, metrics, and results. The saved submission gives your team a single source of truth for whether a change should be rolled out.

Quick guidance

  • State a single clear hypothesis: what you change and the measurable outcome you expect.
  • Choose one primary metric (for decision-making) and one or two secondary metrics (for safety checks such as ticket time or complaints).
  • Use control days/locations that match test conditions (same dayparts, similar weather, similar events).
  • Set run rules and stop rules before running the test. Avoid changing other variables during the test.
  • Collect at least the estimated sample size or run long enough to observe steady performance; smaller samples can be useful but treat results as provisional.

Statistical note (practical)

You don’t need advanced stats to get practical value. Aim for a detectable lift you care about (for example, a 3-5% increase in average check or a 1-2 percentage point improvement in margin). If you’re unsure about sample size, use the sample-size estimate field to capture a target and the run duration to meet it. Treat significance guidance as an advisory signal, not an absolute rule—focus on meaningful business impact plus consistency across multiple runs.

Example: 'Adding X garnish increases perceived value and raises average check for entree Y by >=3% without increasing ticket times.'
Describe recipe, portion, price, menu position, description wording, photo, or other change precisely so staff can implement it consistently.
Pick the one metric you will use to decide whether to roll out the change.
Secondary metrics help you catch negative operational impacts.
Specify which days, shifts, or locations will serve as the control. Try to match dayparts and guest mix.
Specify where and when the test will run. Use similar conditions to the control.
Example: Use alternate days, split menu by receipt code, or run simultaneous location A (control) and B (test). Avoid self-selection where possible.
Enter a reasonable target or leave blank if unknown. Larger samples give more confidence.
Choose a duration long enough to capture regular variability (often 7-14 days to cover week patterns).
The smallest percent change in the primary metric that would justify rollout (e.g. 3).
Document required behaviors: ADDS to menu, price locks, staffing changes avoided, promotional or discount exclusions, recipe checklists, POS coding, and who trains staff.
Specify operational triggers (e.g. >X complaints in a day, >Y% increase in remakes, severe supply disruption) and safety triggers.
Enter the recent control-period average for the primary metric (for comparison).
Enter the observed test-period average after the run completes.
Optional. Use to calculate margin impact of price or recipe changes.
Optional. Enter observed margin % during the test.
(Baseline vs Test) — platform dashboards may compute this automatically. If not, compute as ((test - baseline)/baseline)*100.
Estimate how margin changed due to the test. Useful when pricing or portion changes are involved.
Capture anything that numbers miss: consistent staff confusion, supply issues, or positive guest notes.
Choose the recommended outcome based on primary metric, secondary checks, and qualitative signals.
Capture the reasons behind the decision, rollout timing, training notes, and monitoring plan.
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