Template

Experiment Repository & Template (hypothesis, metrics, results)

An interactive, pre-registered experiment template for operational pilots. Captures a measurable hypothesis, baseline, success criteria, experiment design, data sources, monitoring plan, results, interpretation, and next steps. Includes taxonomy tags so experiments can be discovered and aggregated across menu, labor, waste, tech, inventory, and other domains.

Interactive Tool

Experiment Repository & Template

Use this form to pre-register, run, and document operational experiments. Focus on a measurable hypothesis, a clear primary metric with baseline and target, a reproducible design, and an explicit monitoring and analysis plan. Save early and return to update results. Structured entries make experiments reusable across locations.

Short descriptive name
Person responsible for running the experiment
Email or phone
Select one or more categories
Comma-separated list of locations or 'Pilot location only'
State the expected change and who/what it affects. Example: 'Reducing garnish size will reduce food cost per plate by 3% without lowering guest satisfaction.'
Why this experiment matters and any prior observations
The single most important metric you'll use to judge success (e.g., Food cost %, Number of remakes)
Current measured value for the primary metric (same units as target)
Numeric target that will indicate success (same units)
Where the metric comes from (POS, inventory, manual tally, sensors)
How you'll collect the data, who will record it, and how often
Describe control and treatment groups, randomization, timing, shifts, or schedule. Be specific so others can reproduce.
How long the pilot will run. Prefer enough time to collect representative data
YYYY-MM-DD or 'TBD'
Approximate number of transactions / observations needed
People, training, materials, signage, or tech needed
Approximate incremental cost to run the experiment
Potential negative impacts and how you'll reduce them
Specify calculations, comparisons, and statistical tests you'll use to interpret results. Avoid post-hoc changes.
Who monitors progress, frequency of checks, and who to contact if something goes wrong
Any customer/employee data involved and required permissions or approvals
Sharing helps reuse but may require approvals
Measured changes, key numbers, and whether success criteria were met
What the results mean, possible confounders, and confidence level
Rollout, repeat, modify, or abandon; include estimated impact and owners
YYYY-MM-DD or 'TBD'
Anything else to record
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