What is a CRO Hypothesis?

A CRO Hypothesis is an educated, testable prediction that grounds an experimentation project in data and user psychology. It ensures that A/B tests are run to test valid business theories rather than randomly changing button colors.

CRO hypotheses connect evidence (what we observed) to intervention (what we will change) and expected behavior (what will happen).

The Standard Hypothesis Structure

Use this formula when defining hypotheses:

Because [Observation / Analytics Evidence],
we believe [Proposed Solution / UI Change]
will cause [Expected User Action / Behavior Change],
measured by [Primary Metric].

Real-World Example

  • Bad Hypothesis: “Changing the Add to Cart button to green will increase sales.” (No evidence, no underlying reason).
  • Good Hypothesis: “Because heatmaps and user recordings show mobile users scroll past the Add to Cart button on long product pages, we believe adding a sticky bottom Add to Cart bar will make checkout initiation effortless, increasing mobile Add to Cart rates by 8%.”

Metric Structure for Hypotheses

Every hypothesis must define:

  1. Primary Metric: The main metric deciding test success (e.g. Mobile Conversion Rate).
  2. Secondary Metrics: Supporting behavioral indicators (e.g. Add to Cart Rate).
  3. Guardrail Metrics: Metrics that must not deteriorate (e.g. Page Load Speed, Refund Rate, Customer Support Tickets).

Developer Takeaway

Code Implementation Rule: Before writing A/B test variation code, ensure all primary, secondary, and guardrail metrics are properly instrumented with analytics tracking events. Never launch an experiment variant without verifying event firing in your analytics QA environment.