CRO Glossary
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.
Messaging hierarchy refers to the deliberate sequencing of claims, benefits, and proof points down a page, and specifically to the decision of what earns a spot above the fold versus what gets pushed below it. Above the fold is prime real estate — every visitor sees it without scrolling — so it's typically reserved for the single strongest, most broadly resonant message. Below the fold is where supporting detail, secondary benefits, objections-handling, and proof (reviews, logos, specs) live for visitors who are already engaged enough to keep reading.
This matters in CRO because cramming too much into the fold dilutes the primary message and increases cognitive load, while under-using the fold wastes the page's highest-attention zone on something generic like a stock photo. Getting the hierarchy right means matching message strength to visitor attention: the fold answers "why should I care," and everything below answers "why should I believe it and what do I do next."
A common example: an e-commerce landing page tests leading with a discount percentage above the fold versus leading with a trust statement ("free returns, 2M customers") above the fold, with the other message and supporting detail moved below. Teams often run this as a sequenced set of tests rather than one single test, since messaging hierarchy interacts with hero shot, value proposition clarity, and CTA placement simultaneously.
It differs from simple above-the-fold optimization in that it's not just about what's visible, but about the deliberate ordering logic across the whole page — a strategy question, not just a layout question.
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.
Experiment Contamination
When a control or variant group is unintentionally exposed to the other condition, corrupting the comparison.
Metric Dilution
When a test's effect is real but gets averaged away because it only affects a small slice of the measured population.
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