Assessing heatmaps

A heatmap answers "where do people interact with this page?" — clicks, rage clicks, mouse movement, and how far down they scroll. The data is pure geometry: pointer_relative_x (0..1 across the viewport), pointer_y (absolute pixels down the page), and a count per spot. It does not know what was clicked. Turning "lots of clicks at (0.5, 220)" into "lots of clicks on the Pricing nav link" is the whole job, and it comes from cross-referencing autocapture on the same URL.

Core principle: coordinates + meaning

You can't see the page — there is no screenshot in your context. A good assessment fuses two sources and leans on autocapture to supply the layout/identity you can't see:

1. Heatmap data — where interactions land and how far people scroll (heatmaps-list).
2. Autocapture — what element sits under the hot spots, by element text / selector on the same page. This is what turns coordinates into meaning; without it you only have dots.

When the user wants to see the heatmap, create a saved heatmap (Step 4) — that renders the page with the data overlaid for them to open in PostHog. You reason from the data; they look at the picture.

The flow

Step 1: Pin the page and window

You need an exact url_exact (one page) or a url_pattern (regex, to aggregate across query strings). Confirm the URL with the user if ambiguous. Default to the last 7 days; widen to 30 if volume is low. Heatmap data is retained for 90 days.

Step 2: Pull the data

Call heatmaps-list once per signal you care about (or query the heatmaps table directly via SQL — see the querying-posthog-data skill, models-heatmaps):

• type: "click" — the primary "what draws attention" map.
• type: "rageclick" — repeated frustrated clicks. The single strongest "something is broken or misleading" signal. Any meaningful rageclick cluster deserves a callout.
• type: "scrolldepth" — how far people get. Use it to find the fold and spot CTAs that sit below where most people ever scroll.

Use aggregation: "unique_visitors" when you care about how many people (not how many clicks); total_count exaggerates a few heavy clickers.

Click results come back hottest-first and are capped at limit (default 500). A busy page can have thousands of distinct coordinates, so the default page plus the fold summary is almost always enough — the hottest points are what analysis turns on. Don't ask for everything: raise limit or page with offset only when you specifically need more, and check has_more to know the list was truncated. scrolldepth ignores limit and always returns every bucket.

Step 2b: Above the fold — read the fold summary

For the click types, heatmaps-list returns a fold object alongside results:

• pct_below_fold — share of non-fixed interactions that landed below the user's initial viewport (they had to scroll to reach them). This is one of the highest-value findings: content people actively click that sits below the fold is a prime candidate to move up.
• below_fold_count / total_count — the raw counts behind the percentage (fixed-position elements are excluded, since they're always on screen).
• median_viewport_height — the typical fold line in CSS pixels, to recommend against.

Report it concretely, e.g. "the fold is ~600px for most visitors, yet 35% of clicks land below it, so users scroll before interacting — that content is a candidate for the first screen." Segment by device with viewport_width_min/viewport_width_max (desktop and mobile have very different folds) and read fold per band rather than blending them.

Need a distribution rather than a single percentage (e.g. clicks bucketed by how far below the fold)? Drop to SQL on the raw heatmaps table, which has y and viewport_height in the same scaled units — see the querying-posthog-data skill, models-heatmaps.

Step 3: Name the hot elements (autocapture overlap)

For each notable cluster, find what's actually there. Query autocapture on the same URL — either via the exploring-autocapture-events skill or directly:

SELECT properties.$el_text AS text, count() AS clicks
FROM events
WHERE event = '$autocapture'
  AND properties.$current_url = 'https://example.com/pricing'
  AND timestamp >= now() - INTERVAL 7 DAY
GROUP BY text
ORDER BY clicks DESC
LIMIT 25

elements_chain gives the selector/DOM path when you need to disambiguate two elements with the same text. Match autocapture's top elements to the heatmap's hot coordinates: clicks concentrated on something that is not a link or button (plain text, an image, a disabled control) is a classic "users expect this to be clickable" finding.

Step 4: Give the user a heatmap to look at (optional)

You can't see the page, but the user can. When a visual would help them follow your findings, create a saved heatmap so they can open the rendered page with the data overlaid in PostHog:

1. heatmaps-saved-create with the page url (type defaults to screenshot). This enqueues a headless render — it is asynchronous. Pass widths matching the viewport band you analyzed in Step 2.
2. Poll heatmaps-saved-get (by the returned short_id) until status is completed, then tell the user it's ready to view in PostHog.

This is for the human's benefit — your own reasoning still comes from the Step 2 data and the Step 3 autocapture identity, not from the picture.

Step 5: Drill into hotspots (when you need the "why")

For a surprising cluster, heatmaps-events returns the individual sessions behind specific points. Hand the session IDs to the investigating-replay skill to watch what people actually did.

Step 6: Summarize and recommend

Produce a short, concrete report:

• What the heatmap shows — top engaged elements, dead zones, scroll reach, and the above/below-the-fold click split (e.g. "viewport is ~600px for most visitors, yet 35% of clicks land below it").
• Problems, ranked by signal strength — rage-click clusters first, then clicks on non-interactive elements, then important CTAs sitting below the scroll cliff, then ignored primary actions.
• Recommendations tied to evidence — move/raise a CTA above the fold, make a clicked-but-dead element a real link, cut competing elements near a rage-click cluster, etc. Every recommendation should cite the signal it came from.

Reading the signals

Signal	Likely meaning	Typical recommendation
Rage clicks on an element	Broken, slow, or looks-clickable-but-isn't	Fix the handler, add feedback, or make it actually interactive
Many clicks on non-link text/image	Users expect it to be clickable	Make it a link/button, or remove the affordance
Primary CTA gets few clicks	Buried, low-contrast, or out-competed	Raise it, increase contrast, reduce nearby noise
Scroll cliff before key content	Content/CTA is below where people stop	Move it up or add a reason to scroll
High % of clicks below the fold	Engaged content sits below the initial viewport — users scroll before interacting	Move the most-clicked elements onto the first screen
Hot clicks on nav, cold body	Page isn't delivering; people bail to nav	Re-evaluate the page's core content

Gotchas

• Heatmaps must be opted in (Team.heatmaps_opt_in). If heatmaps-list returns nothing for a page that clearly gets traffic, capture may be off or the URL is wrong — check both before concluding "no engagement".
• Coordinates are scaled by a factor of 16 in storage; the API already returns CSS-pixel pointer_y and relative x, so use the API/tool values directly rather than the raw table columns.
• You can't see the screenshot. The saved-heatmap render is for the user to open in PostHog; don't claim to have looked at the page. Ground every layout claim in autocapture identity + coordinates, not vision.
• Saved-heatmap rendering is async. After heatmaps-saved-create, poll heatmaps-saved-get until status is completed before telling the user it's viewable. Only screenshot-type heatmaps render an image; iframe and recording types do not.
• Mind the viewport. A desktop click map and a mobile one are different pages' worth of behavior — filter with viewport_width_min/viewport_width_max rather than blending them.