Growth
ICE score calculator and growth experiment planner
By Charles Summers · Updated · Free, no signup
Short answer
This scores a growth experiment using the ICE framework (Impact, Confidence, Ease) and returns more than a number: a properly formed hypothesis, the minimum sample size question you need to answer before starting, a kill criterion, and where the test belongs in your sprint order. Confidence is weighted more heavily than the raw ICE average because low-confidence high-impact ideas are the most common way growth teams waste a quarter.
Use the growth experiment planner
What does this tool actually do?
This scores a growth experiment using the ICE framework (Impact, Confidence, Ease) and returns more than a number: a properly formed hypothesis, the minimum sample size question you need to answer before starting, a kill criterion, and where the test belongs in your sprint order. Confidence is weighted more heavily than the raw ICE average because low-confidence high-impact ideas are the most common way growth teams waste a quarter..
It runs entirely in your browser. Nothing you type is sent to a server, no account is required, and there is no usage limit, because there is no cost per run to control.
What does the output look like?
This is the exact output the tool produces from the example inputs. It is generated by the same code that runs when you click the button, so what you see here is what you get.
Frequently asked questions
How is the ICE score calculated?
The classic version is the mean of Impact, Confidence and Ease. This tool reports that, and also a confidence-weighted score that squares the confidence term. The reason is practical: a 10/2/10 idea and a 7/7/7 idea have nearly the same plain ICE score, but the first is a guess and the second is a plan. Weighting confidence separates them.
What is a good ICE score?
Scores are only meaningful relative to your other ideas, not against an absolute bar. A backlog where everything scores above 8 means the scoring is not honest yet. Expect a spread, and expect most ideas to land between 4 and 7.
Why does the output include a kill criterion?
Because the most expensive experiment is the one nobody ends. Deciding in advance what result would make you stop is the difference between a test and a preference. The tool proposes one based on your ease score, since cheap tests deserve shorter leashes.
Should low-ease experiments ever go first?
Only when the impact is high and confidence is genuinely high, which is rare. The sequencing note in the output tells you which bucket your idea falls into and what should be true before you commit engineering time to it.
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