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Most experiment backlogs are a mix of high-signal ideas and noise. This scorecard separates them — using a weighted formula across impact, confidence, reach, effort, and learning value.
SCORE AN EXPERIMENT
Rate each dimension 1–10. The weighted score out of 10 tells you whether to run it, consider it, or skip it.
Experiment details
Weighted Score
Impact
5
×3
Confidence
5
×2
Reach
5
×2
Effort
5
×1.5
Learning
5
×1.5
EXPERIMENT BACKLOG
Experiments are automatically sorted by score. The top two are highlighted.
Save your backlog
Get a PDF of your scored experiments with a prioritisation note.
HOW THE SCORE IS CALCULATED
Not all dimensions matter equally. Impact has more weight than effort because a high-impact experiment is worth running even if it's hard. Learning value prevents you from discarding experiments that will teach you something even if they fail.
Score = (Impact × 3 + Confidence × 2 + Reach × 2 + Effort × 1.5 + Learning × 1.5) ÷ 10
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