CRO Glossary
Test Iteration Velocity
The rate at which a team ships, learns from, and follows up on experiments over a given period.
Test iteration velocity describes how many experiments a team can plan, launch, analyze, and act on within a fixed window, usually measured per month or per quarter. It's less about raw speed and more about the health of the whole pipeline: idea generation, dev/design capacity, QA, statistical analysis, and the willingness to kill or scale a test based on results. Teams often confuse velocity with volume, but running ten sloppy tests a month is worse than running three well-designed ones if the sloppy tests produce unreliable or unusable data.
Practitioners care about this because experimentation compounds. A program that reliably ships one clean test every two weeks will out-learn a program that occasionally ships a big redesign test every quarter, even if the redesign test has a larger sample. Velocity is also a leading indicator of organizational buy-in — if velocity is dropping, it's often a sign of bottlenecks in dev resourcing, stakeholder approval, or a lack of a clear backlog.
For example, a growth team might track that they shipped 6 tests in Q1 but only 2 reached significance before deadline pressure forced a call. That signals a need to either lower MDE ambitions, increase traffic allocation, or simplify test scope so more experiments finish cleanly within the sprint cycle.
Teams typically improve velocity by templating experiment design docs, pre-building reusable test components (like modal or banner frameworks), and setting a standing cadence for review meetings rather than ad hoc scheduling.
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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