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Working with objectivesRunning experimentsUsing playbooksAI features

Running experiments

Track your growth experiments from hypothesis to learnings.

Experiments are where the work happens. Each experiment tracks your hypothesis, implementation, results, and learnings - building your growth knowledge base over time.

Starting an experiment

You can start an experiment from a playbook recommendation, or create one from scratch:

  • •From a playbook: Click "Run this experiment" to pre-fill the method template
  • •From scratch: Click "New experiment" on the experiments page
  • •From an objective: Select a recommended playbook to start

Experiment workflow

Each experiment follows a simple workflow:

  • •Draft: Define your hypothesis and plan the implementation
  • •Running: Execute the experiment and collect data
  • •Analyzing: Review results and determine outcome
  • •Complete: Document learnings and next steps

Writing a good hypothesis

A good hypothesis is specific and testable. Use this format: "If we [action], then [expected outcome], because [reasoning]".

Example hypotheses:

  • •"If we add a progress bar to onboarding, then completion rate will increase by 20%, because users can see how close they are to done."
  • •"If we send a reminder email on day 3, then day-7 retention will improve by 15%, because users who stall often forget about us."

Logging results

When your experiment is complete, log your results honestly - including failures. Failed experiments are valuable learning opportunities.

Tip: Document what surprised you, not just what you expected. Unexpected results often lead to the biggest insights.

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