cro-optimization

A funnel audit identifies conversion bottlenecks by analyzing quantitative and qualitative friction points in your checkout flow. It requires baseline metrics from existing analytics to locate where users drop off before reaching conversion.

The best way to generate data-driven hypotheses for A/B testing is to translate funnel audit findings into testable statements with clear rationale. This approach prioritizes hypotheses based on identified friction points to ensure tests target meaningful conversion improvements.

To design a robust A/B test for a landing page, define your primary and guardrail metrics alongside sample size and test duration. You must also establish segmentation strategies and QA checks to ensure statistical significance and valid results.

Yes, you need an existing analytics tool and an A/B testing platform to run conversion rate optimization effectively. The workflow requires baseline metrics, access to analytics data, and defined sample size and decision criteria to interpret results accurately.

To interpret A/B test results, evaluate statistical significance against your predefined primary and guardrail metrics. Use these results and decision criteria to confidently ship the variation, extend the experiment, or kill it, while documenting the learnings for future reference.

Yes, you can use this CRO workflow for onboarding funnels. It applies to landing pages, checkout flows, and onboarding processes, allowing you to audit user friction and generate targeted hypotheses across any organization with established testing capabilities.