A/B Testing Implementation & Analysis

Published: 2026-04-02 · CRO · Ricky Bandelin

Conversion rate optimization (CRO) is both an art and a science. Using the scientific approach, we implement what is called a hypothesis – a building block of an experiment. It is an educated guess that may or may not be validated. Scientists use the word whenever they propose a new theory or prediction.

Key Takeaways

• Proper A/B test implementation requires a clear hypothesis, defined success metric, and adequate sample size before launching
• Testing tools must be correctly configured to ensure variants are served randomly and consistently
• Quality assurance on both variants before launch prevents invalid test results
• Analysis should look beyond the primary metric to understand the full impact on user behavior
• A test log that tracks every experiment helps teams avoid repeating failed tests and builds institutional knowledge

In CRO we call our theories of how a website can be optimized for better outcomes hypotheses as well. The better structured a hypothesis is, the better your chances are of achieving a more successful, more organized outcome.

Example: "I believe personalizing customer logos by region will result in a better form-fill rate because it instills confidence in the prospect."

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