CRO Glossary
Lift
The relative or absolute difference in a metric between a test variant and the control, expressed as the improvement attributed to the change.
Lift is the headline number most stakeholders actually want from an experiment: how much better (or worse) did the variant perform compared to control. It's usually expressed as a relative percentage — for example, 'the new headline produced a 12% lift in signups' — though it can also be reported as an absolute difference in percentage points or raw counts.
Lift matters because it translates statistical output into a business-relevant statement, but it can also be misleading if reported without its confidence interval or significance level. A reported lift of 12% with a wide confidence interval spanning -2% to +26% is a very different claim than a 12% lift with a tight interval of +9% to +15%, even though the headline number is identical. Responsible reporting always pairs lift with its uncertainty range.
As a concrete example, if a control checkout page converts at 3.0% and a variant converts at 3.3%, the absolute lift is 0.3 percentage points, while the relative lift is 10%. Teams typically prefer relative lift for cross-experiment comparison, since it normalizes for differing baseline conversion rates across pages or segments.
A common pitfall is quoting lift from an underpowered test or from a metric peeked at mid-flight, which tends to overstate the true effect due to the winner's curse and regression to the mean — so lift figures should always be read alongside sample size and test duration.
Related terms
Above vs Below Median Split
A segmentation technique that divides users into two groups at the median of a metric to compare high and low engagers.
Qualitative Feedback Loop
A recurring process of collecting and acting on user comments, session recordings, and survey responses to inform test hypotheses.
Experiment Metadata
The structured record of who, what, when, and how for a test — hypothesis, owner, dates, variants, and metrics used to track and audit it.
Pre-Test Analysis
The upfront work of validating traffic, metrics, and baseline data quality before launching an experiment.
Above-the-Fold vs Below-the-Fold Messaging Hierarchy
How the order and prominence of value-proposition messaging is split between the visible screen and the rest of the page.
Experiment Contamination
When a control or variant group is unintentionally exposed to the other condition, corrupting the comparison.
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