Skip to content

How to review the scroll journey of a landing page

A useful long page answers the reader's next question as they move down. Review its sequence, visual transitions and actions without inventing a visitor path.

By the Heatpoints team. Published . Updated . 3 minute read.

Try the image heatmap
Predicted attention on a whole fictional landing page
Fictional design, real model output. Colours are relative to this image only. Provenance

Give each section a job

A scroll journey is the sequence of information available as someone moves through a page. For a product, that could mean understanding the offer, seeing how it works, checking a relevant use case, resolving an objection and choosing a next step.

This is a design hypothesis, not a promise that everyone reads linearly. Some people go directly to pricing; others arrive on a deep link. Section headings and navigation should make those routes usable too.

Audit the page screen by screen

Use the Chrome extension to review the visible page as you scroll. Pause between sections to allow a new prediction. Record what question each screen answers and whether a visually strong element supports that answer.

A full-page image is useful for checking overall composition, but it is not the same view a person sees through a browser window. Do not infer scroll reach from the attention colours on a tall export.

The fictional page with five viewport windows and their metrics
Five windows down one fictional page, with the strongest cell and hierarchy of each.

References: Chrome extension workflow

Look for gaps rather than adding length

A section that repeats the hero's promise without adding proof or detail is unlikely to answer a new question. Replace it with something concrete: a product example, a limitation, a workflow, or the conditions under which a feature is useful.

Check the transitions. After a demonstration, the reader may need to understand access limits. After a comparison, they may need a way to try the relevant feature. A useful next link is more valuable than a generic action repeated after every paragraph.

Validate the journey with actual behaviour

Predicted attention cannot measure how many visitors reach a section or why they leave. Use consent-appropriate behavioural analytics and user research when those are the questions. Check the first screen and later content without assuming a universal attention percentage.

References: NN/g: Scrolling and Attention

A scroll review, measured

The fictional Fieldnote landing page on this site is 2,815 pixels tall at a desktop width. Its whole-page map was computed once, then the metrics were read inside a 720-pixel window at forty positions, which is what the extension shows as you scroll. Five of them are in the table. The hierarchy metric swings from 50 at the top, where the headline leads, to 1 a quarter of the way down, where three equal feature cards share the screen, and back to 32 at the closing headline.

That swing is exactly the gap the review above is looking for: a screen in the middle of the journey where nothing leads. It is not visible on a first-screen capture.

Metrics of the map in a 720-pixel window at five scroll positions of the fictional Fieldnote page, 2,815 pixels tall.
Scroll positionAttentionFocusHierarchyCoverageStrongest cell
Top of page968508%Centre left
25% down1073111%Centre
50% down971810%Centre right
75% down657206%Centre left
Bottom872329%Centre left
The fictional Fieldnote landing page, full heightUNISAL prediction for the whole page
The fictional Fieldnote page, 2,815 pixels tall, and its whole-page prediction. The extension shows you the window that fits your screen.

References: Provenance, hashes and metrics · Readout of all forty scroll positions

A practical review record

For each screen, write down the reader's question, the evidence offered, the next action and one potential distraction. Keep a screenshot of the original and any revision. This makes a long-page review concrete enough to discuss and repeat.

  1. Offer: can I understand what this is?
  2. Demonstration: can I see what happens when I use it?
  3. Fit: does it work for my situation?
  4. Conditions: what are the limits and data implications?
  5. Action: can I try it or reach the next useful page?