🧪 Experimentation Framework
Experimentation replaces opinion-based decisions with evidence-based ones. A/B testing is the most common form, but experimentation includes multivariate tests, feature flags, beta programs, and fake-door tests. The key is forming clear hypotheses, defining success metrics before running tests, and being willing to accept results that contradict your assumptions.
Experiment Design
Hypothesis
Clear, falsifiable statement of what you believe will happen
Metrics
Primary and guardrail metrics defined before the test runs
Design
Sample size, duration, segmentation, control group selection
Analysis
Statistical significance, practical significance, long-term effects
Real-World Example
Booking.com runs over 25,000 experiments per year. Their culture is built around the principle that no one's opinion — including the CEO's — outranks a well-designed experiment. This experimentation velocity is what drove their conversion rate optimization.