Measure reading signals without turning insight into guesswork.

Events captured

• Page view and document start.
• Reaching a specific PDF page.
• CTA click and share actions.
• Form start, submit and error events.
• Private PDF download.

What the numbers can tell you

Page reach helps distinguish a quick bounce from readers who moved deeper into a document. CTA and form activity show where readers chose an action.

Because events are tied to anonymous sessions, a team can compare behavior across page versions without collecting visitor names before a form is submitted.

Traffic attribution

Recognizable search and AI-assistant referrers are classified separately. UTM values can identify campaigns. A large share of traffic may remain direct or unknown because browsers and assistants often omit a referrer.

PDFLP does not convert missing attribution into an AI referral. Unattributed traffic stays visible as a separate category.

Analytics limitations

• Reading depth is based on observed events, not eye tracking.
• One visitor may use multiple sessions or devices.
• Ad blockers, privacy settings and network failures can suppress events.
• A download or form submission does not prove that the file was read later.
• Small samples should not be presented as reliable conversion benchmarks.

Privacy-conscious collection

The public event payload contains an anonymous session identifier and limited metadata. Lead form content is not included in analytics events.

The workspace can review aggregate behavior without receiving a complete behavioral profile for every reader.