By the DynoWeb Team
What a heatmap actually is
A heatmap is a visual overlay of a page that aggregates how many visitors interacted with each area. Instead of reading rows of numbers, you see a color-coded picture of behavior. There are three types every Shopify merchant should use, and each answers a different question.
Click maps: where shoppers tap
Click maps show your most-engaged elements — and, crucially, the ones shoppers expect to be clickable but aren’t. Bright clusters on a non-interactive image or label are a frustration signal: shoppers want that element to do something. Either make it clickable or remove the false affordance. These are closely related to dead clicks.
Scroll maps: how far shoppers read
A scroll map uses a color gradient to show where visitors stop scrolling. The single most common finding: your add-to-cart button, your reviews, or your strongest proof sits below where most people stop. That content is effectively invisible. Raise it. See scroll depth analytics for a deeper dive.
Attention maps: what earns focus
Attention maps combine dwell, hover, and interaction to show which sections truly hold attention versus which get skimmed. Cold zones over content you invested in tell you it isn’t earning its place — tighten it, move it, or cut it.
Reading mobile vs desktop separately
Shoppers behave completely differently on a phone. Always view device-specific heatmaps — mobile maps surface fat-finger taps and thumb-zone mis-hits that desktop testing never catches.
Tip
Don’t stop at one map. Cross-reference the click map (what they tap) with the scroll map (what they see) — a hot click zone below the scroll cliff means an element is fighting for attention it can’t get.
From map to fix
Heatmaps diagnose; they don’t prescribe. The workflow is: spot the pattern, confirm the cause with a session replay, make a focused change, and measure it with revenue attribution. DynoWeb shortens this loop by turning the patterns it finds into prioritised, dev-ready fixes — so you spend your time shipping improvements, not interpreting data.