#data-driven

Design a rigorous A/B test for product optimization. Process: 1. Define hypothesis (changing X will increase Y by Z%). 2. Choose primary and secondary metrics. 3. Calculate required sample size for statistical power. 4. Determine test duration (minimum 1 week, 2 business cycles). 5. Randomize users (50/50 split). 6. Implement tracking and QA. 7. Monitor for novelty effects and external factors. Analyze results with statistical significance testing. Document learnings. Iterate based on insights.

Build organization-wide culture of data-driven experimentation. Experimentation principles: 1. Hypothesis-driven: clear prediction before testing. 2. Statistical rigor: proper sample sizes, significance testing. 3. Learning over winning: failed tests provide valuable insights. 4. Democratized testing: enable teams to run their own experiments. Organizational structure: 1. Centralized platform: shared tooling and statistical expertise. 2. Embedded analysts: help teams design and analyze tests. 3. Experimentation review boards: ensure quality and prevent conflicts. 4. Test calendar: avoid contradictory experiments. Process framework: 1. Idea prioritization: impact potential × ease of implementation. 2. Experiment design: hypothesis, metrics, sample size calculation. 3. Implementation: feature flags, proper randomization. 4. Analysis: statistical significance, practical significance. 5. Documentation: results database for institutional learning. Tools: Optimizely, LaunchDarkly for testing infrastructure. Success metrics: experiments per team per quarter, percentage of features launched with tests, speed of insight-to-action.