Business
Generador de Experimentos de Crecimiento
Used by developers, writers, and creators worldwide.
A growth experiment generator produces a structured experiment card so growth ideas get tested rigorously rather than shipped on a hunch. Pick the growth lever — acquisition, activation, retention, referral, or revenue — describe your idea, and it returns a card with a falsifiable hypothesis, the one primary metric with a baseline and target, a test design, an ICE priority score, and slots for the result and learning. Growth teams and founders use it to run a disciplined experiment pipeline, prioritise the highest-leverage tests, and capture learnings whether an experiment wins or loses. The discipline matters because most growth ideas fail, and the teams that win are the ones that test cheaply, learn fast, and double down only on what proves out. Everything generates instantly in your browser. Tie each experiment to a single metric, score it with ICE before running, and record the learning even when the result disappoints.
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How to use
- Choose your options above
- Click Generate
- Copy your result
Detailed instructions
- Pick the growth lever and describe the idea.
- Click Generate to produce the experiment card.
- Set the metric, baseline, target, and ICE score.
- Run it, record the result, and capture the learning.
Use Cases
- •Documenting a growth experiment before running it
- •Writing a falsifiable hypothesis tied to one metric
- •Prioritising experiments with an ICE score
- •Building a repeatable growth experiment pipeline
- •Capturing learnings from wins and losses alike
Tips
- →Tie every experiment to a single primary metric.
- →Score with ICE before running to prioritise.
- →Write the hypothesis so it can be proven false.
- →Record the learning even when the test fails.
FAQ
what is an ICE score
ICE rates an experiment on Impact, Confidence, and Ease, each on a 1–10 scale. Multiplying or averaging them gives a quick priority score so you run the highest-leverage tests first instead of whatever feels exciting.
why tie an experiment to one metric
A single primary metric makes the result unambiguous. If a test could move several numbers, you risk cherry-picking the one that looks good afterward. One metric with a baseline and target keeps the experiment honest.
what if the experiment fails
A failed experiment that produces a clear learning still wins. Most growth ideas do not work, so recording why something failed sharpens your model of users and feeds better hypotheses. Capture the learning every time.
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