Interpreting test results

Raw numbers tell only part of the story. Version B might show 10% higher conversion, but dig deeper. Check if the improvement holds across different user segments. Mobile users might love the change while desktop users hate it. New visitors might convert better while returning customers convert worse.

Look beyond your primary metric. An experiment might increase purchases but decrease average order value, resulting in lower revenue. Or it might boost short-term conversion while hurting long-term retention.

Context also matters for interpretation. A successful holiday season test might fail in January. A test that works for US users might flop in Japan. Time-based patterns like day of week or hour of day can skew results. This is why many teams run tests for full weeks to capture these cycles.