What is Heatmap?

Heatmaps translate raw user behavior data into intuitive visual patterns that reveal how visitors actually interact with your pages—as opposed to how you assume they do. The gap between designer intent and real user behavior is often surprising. Elements you expect to draw attention may be completely ignored, while unexpected areas might receive concentrated interaction. Heatmaps close this knowledge gap and provide the evidence needed to make informed optimization decisions.

Three primary heatmap types serve different analytical purposes. Click heatmaps show where visitors click or tap, revealing which elements attract interaction and which are ignored. Scroll heatmaps show how far down a page visitors travel, identifying the point where attention drops off—critical for deciding where to place high-priority content like reviews and calls-to-action. Move heatmaps (or attention heatmaps) track cursor movement, which correlates with visual attention and reading patterns.

For Shopify store owners, heatmaps provide actionable insights at every level of the funnel. On product pages, they reveal whether customers are scrolling past your review section, whether they engage more with photo reviews or text reviews, and whether your add-to-cart button is positioned in a high-attention zone. On collection pages, they show which products and filters receive the most interaction. On the homepage, they reveal which promotional sections drive engagement and which are invisible to visitors.

The most valuable application of heatmap data is informing A/B tests. Rather than testing random variations, heatmap insights tell you what to test and why. If a heatmap shows that 70% of visitors never scroll to your review section, you know to test moving reviews higher on the page. If click data shows visitors tapping on non-clickable review images, you know to make those images interactive. This evidence-based approach to testing produces higher win rates and larger lifts than intuition-driven experimentation.

Eevy AI's genetic algorithm approach to layout testing effectively automates what heatmap analysis suggests manually. Instead of studying heatmaps to hypothesize better review widget placements and formats, Eevy continuously tests layout variations and evolves toward the highest-performing configurations based on actual conversion data.