By use case
A/B Testing
Run controlled product experiments with explicit hypotheses, segmented traffic, and outcome-driven ramp decisions.
When to use this playbook
- You need evidence before committing to a broad feature rollout.
- You want to compare onboarding, pricing, or engagement variants against a control.
- You need a safe path to stop exposure if guardrail metrics degrade.
Experiment sequence
- 1. Define the hypothesis and KPI. Name the target metric and a guardrail metric before launch.
- 2. Build variants as flag values. Use string or numeric values to represent treatment variants.
- 3. Target the experiment audience. Segment by stable attributes and exclude high-risk cohorts when needed.
- 4. Ramp in checkpoints. Increase exposure only when both KPI trend and operational health are acceptable.
- 5. Promote or roll back. Move winning variants to default or disable the experiment instantly.
Cross-links
Configure, measure, and govern experiments.
- Targeting and Segments for audience eligibility rules.
- Progressive Rollouts for controlled exposure ramps.
- Analytics and Reporting for evaluation loops and metric review.
- Product Team Solution for portfolio-level experimentation strategy.