heatmap-generator

Heatmap Generator

Generate interaction heatmaps from real session data — click density, tap zones, rage click hotspots, and dead click dead zones. No visual rendering (Fullstory doesn't expose pixel coordinates natively) — instead, element-level aggregation showing which elements get the most and least interaction.

When to Use

• "What do users click on most on the homepage?"
• "Show me a click map of the pricing page"
• "Are users clicking non-interactive elements?"
• "Where should we place the CTA on the landing page?"
• "Which elements are being ignored?"
• "Compare click patterns between new and returning users"

Mental Model

A heatmap visualizes interaction density. Hot zones = elements users interact with a lot. Cold zones = elements users ignore (or can't find). Rage click zones = elements users click repeatedly — often non-interactive elements that look clickable.

Fullstory doesn't expose cursor coordinates through the MCP, so you can't render a pixel-accurate heatmap. Instead, you generate an element-level interaction map — showing which DOM elements get clicked, how often, and in what pattern.

Workflow

Step 1: Choose the page

Which page? Landing page, pricing page, product page, checkout.

Specify page: /pricing, /landing, /products/*

Step 2: Aggregate clicks by element

Build a metric for click events on the page:

fullstory:build_metric(
  query="click events on /pricing by element",
  output_type="top_n"
)
fullstory:compute_metric(metric_id)

This returns the most-clicked elements, ranked by click count.

Step 3: Identify interaction patterns

Hot elements (high click count):

• Are these the intended CTAs? Good — users are finding them.
• Are these non-interactive elements? Bad — users think they're clickable but they're not (dead click risk).

Cold elements (low/no clicks):

• Is the primary CTA here? Bad — users aren't finding it.
• Is this decorative or secondary content? Good — expected.

Rage click zones (repeated clicks on same element):

fullstory:get_opportunities → filter by page → look for rage click signals
→ "Rage clicks on 'Apply Promo' button: 412 users, concentrated on /checkout"