Comparing variants with live traffic under controlled conditions to measure which experience better meets a defined success metric. Requires clear hypotheses, instrumentation and statistical caution.
Category
Analytics & Behaviour
Talks that might mention this method (38)
- Metrics-driven Design
- Design Experimentation for Growth
- The Designer with Business Confidence
- UX Metrics and Research
- Nobody Knows What They're Doing (and That's to Your Advantage)
- Using Design for Competitive Advantage
- From insight to impact: A hypothesis-driven framework for product teams
- Measure UX Impact: Metrics Beyond CTRs - Sponsored by Google
- Measure UX Impact: Metrics Beyond CTRs - Sponsored by Google
- Design like a Scientist: A/B Testing UX at Netflix
- Using Design for Competitive Advantage
- Why Personalisation is like Detective Work
- Predicting winners
- AI - With great power comes great responsibility
- Validated != Valid: Why Our Beloved UX Metrics Fail App-First Journeys
- The Power of Atomic UX Research
- The Power of Atomic UX Research
- Using Design for Competitive Advantage
- The Role of Metrics in a UX Strategy
- The Power of Atomic UX Research
- Measure UX impact: Metrics beyond CTRs
- Improve your design system with confidence through A/B testing
- Design Ops Metrics
- Personal UX is the only UX that matters
- Human Centeredness At a Time of AI Amplified Business Metrics
- Incremental Metrics: Getting Your Kpi's Right
- A/B Testing At Scale
- A/B Testing at the Scale of Millions
- The Role of Metrics in a UX Strategy
- Metrics-Driven Design
- From insight to impact A hypothesis-driven framework for product teams
- Human Centeredness At a Time of AI Amplified Business Metrics
- Incremental Metrics: Getting Your Kpi's Right
- A/B Testing At Scale
- Pros and Cons of A/B Testing
- From insight to impact: A hypothesis-driven framework for product teams
- The Power of Atomic UX Research
- Analytics Best Practice