A/B Testing

Compare two versions of a design to determine which performs better against specific metrics.

Research Classification

Research Type

Attitudinal Behavioral

Behavioral: Focuses on what people do: their actual behaviors and actions.

Data Type

Qualitative Quantitative

Quantitative: Collects numerical data that can be measured and statistically analyzed.

Requirements

Budget

medium

Moderate investment needed

Timeline

medium

2-4 weeks

Team Size

small

Works with 2-3 people

Research Goals

evaluation

Pros & Cons

Pros

  • ✓ Provides clear quantitative results
  • ✓ Reduces decision-making based on opinion
  • ✓ Can test specific elements or entire designs
  • ✓ Directly measures impact on business metrics
  • ✓ Builds a culture of experimentation

Cons

  • × Requires significant traffic for statistical significance
  • × Limited to testing existing concepts
  • × May not explain why one version performs better
  • × Technical implementation can be complex
  • × Results may vary across different user segments

Use Cases

Example Scenario

Testing two different checkout flows to see which results in higher conversion rates.

Additional Applications

  • • Call-to-action button testing
  • • Landing page optimization
  • • Pricing strategy evaluation
  • • Feature introduction methods
  • • Content layout comparison

Resources

Learn about A/B testing | Adobe